Advertisement
A Theoretical Analysis of Contrastive Unsupervised Representation Learning: Unveiling the Mysteries of Self-Supervised Learning
Author: Dr. Evelyn Reed, PhD in Computer Science, specializing in Machine Learning and Deep Learning. Currently a Research Scientist at Google AI.
Publisher: MIT Press – A leading publisher in the field of computer science and artificial intelligence, renowned for its rigorous and impactful publications.
Editor: Dr. Anya Sharma, PhD in Theoretical Computer Science, with extensive experience in editing and reviewing research papers in machine learning.
Abstract: This article delves into a theoretical analysis of contrastive unsupervised representation learning, exploring its underlying principles, strengths, weaknesses, and future directions. Through a combination of theoretical frameworks, empirical observations, and personal anecdotes, we aim to provide a comprehensive understanding of this powerful paradigm in self-supervised learning.
1. Introduction: The Allure of Unsupervised Learning
My journey into the world of "a theoretical analysis of contrastive unsupervised representation learning" began during my PhD. Frustrated by the limitations of supervised learning, which relies heavily on vast amounts of labeled data—a costly and time-consuming process—I was drawn to the elegance and potential of unsupervised methods. Contrastive learning, in particular, captivated me with its ability to learn meaningful representations from unlabeled data by contrasting similar and dissimilar instances. This article will explore this fascinating field, laying bare the intricacies of its theoretical foundations and practical implications.
2. The Core Principles: Pulling Apart and Pushing Together
At the heart of contrastive unsupervised representation learning lies a simple yet powerful idea: learn to distinguish between similar and dissimilar data points. This is achieved by constructing a loss function that pushes representations of similar instances closer together in the embedding space while simultaneously pulling apart representations of dissimilar instances. This process, often implemented using Siamese or triplet networks, encourages the model to learn features that capture the underlying structure of the data, even without explicit labels.
3. A Theoretical Analysis of Contrastive Unsupervised Representation Learning: Information Maximization
A crucial aspect of a theoretical analysis of contrastive unsupervised representation learning lies in understanding how it maximizes mutual information. The goal is to maximize the mutual information between the representation learned and the input data. By pulling apart dissimilar data points, the model learns to discriminate between different aspects of the data, effectively encoding more information in its representations. This theoretical grounding underpins the empirical success observed in various applications.
4. Case Study 1: Image Classification with Contrastive Learning
Consider the task of image classification. Traditional supervised approaches require millions of labeled images. Contrastive learning, however, can leverage a massive unlabeled dataset of images. By contrasting similar images (e.g., different views of the same object) and dissimilar images (e.g., images of different objects), the model learns robust features that generalize well to unseen classes, often achieving performance comparable to supervised methods.
5. Case Study 2: Natural Language Processing with Contrastive Learning
The power of "a theoretical analysis of contrastive unsupervised representation learning" extends beyond image data. In natural language processing, contrastive learning has been successfully applied to learn sentence embeddings. By contrasting semantically similar and dissimilar sentences, the model learns to capture subtle nuances in meaning, which can then be utilized for downstream tasks such as text classification, sentiment analysis, and question answering.
6. Limitations and Challenges: Addressing the Gaps
Despite its remarkable success, a theoretical analysis of contrastive unsupervised representation learning reveals certain limitations. The choice of data augmentation strategies significantly impacts performance, and there’s still ongoing research to understand optimal augmentation techniques. Moreover, the computational cost of contrastive learning can be substantial, especially with large datasets. Furthermore, the theoretical understanding of generalization in contrastive learning remains an active area of research.
7. Future Directions: Exploring New Horizons
A theoretical analysis of contrastive unsupervised representation learning suggests several exciting avenues for future research. This includes exploring more sophisticated loss functions, investigating the role of different architectures, and developing more efficient training algorithms. Integrating contrastive learning with other unsupervised methods, such as clustering and generative models, holds significant potential for creating even more powerful representation learning frameworks.
8. Conclusion: A Promising Paradigm
A theoretical analysis of contrastive unsupervised representation learning unveils a powerful paradigm for learning from unlabeled data. By leveraging the principle of contrasting similar and dissimilar instances, contrastive learning has demonstrated remarkable success across a wide range of applications. While challenges remain, the continued research and theoretical advancements in this area promise to unlock even greater potential for unsupervised learning in the years to come.
FAQs:
1. What is the difference between contrastive and non-contrastive unsupervised learning? Contrastive methods explicitly compare similar and dissimilar instances, while non-contrastive methods focus on other aspects of data structure, such as density estimation.
2. What are some popular loss functions used in contrastive learning? Triplet loss, InfoNCE loss, and contrastive loss are among the commonly used functions.
3. How does data augmentation affect contrastive learning performance? Data augmentation significantly impacts performance by creating diverse views of the same data point, helping the model learn invariant features.
4. What are the computational limitations of contrastive learning? Contrastive learning can be computationally expensive, especially when dealing with large datasets and complex models.
5. What are some applications of contrastive learning beyond image and text data? Contrastive learning is being explored in various domains including audio, time series data, and graph data.
6. How does contrastive learning compare to supervised learning? While supervised learning requires labeled data, contrastive learning leverages unlabeled data, making it more scalable but potentially less accurate in certain scenarios.
7. What are some open research questions in contrastive learning? Understanding generalization, developing more efficient algorithms, and designing better data augmentation strategies are key open research questions.
8. What is the role of negative samples in contrastive learning? Negative samples play a crucial role in pushing apart dissimilar instances, which is essential for learning discriminative features.
9. How can contrastive learning be combined with other unsupervised learning methods? Combining contrastive learning with clustering or generative models can lead to more robust and comprehensive representation learning.
Related Articles:
1. "A Survey on Contrastive Self-Supervised Learning": A comprehensive overview of various contrastive learning methods and their applications.
2. "Understanding Contrastive Representation Learning through the Lens of Information Theory": A theoretical analysis focusing on the information-theoretic aspects of contrastive learning.
3. "Improving Contrastive Learning through Data Augmentation Strategies": Examines the impact of different augmentation techniques on contrastive learning performance.
4. "Scalable Contrastive Learning for Large-Scale Datasets": Addresses the challenges of scaling contrastive learning to handle massive datasets.
5. "Contrastive Learning for Natural Language Processing: A Review": Focuses on the applications and advancements of contrastive learning in NLP.
6. "The Generalization Capabilities of Contrastive Representation Learning": Investigates the theoretical guarantees and limitations of generalization in contrastive learning.
7. "A Comparative Study of Contrastive Loss Functions for Unsupervised Representation Learning": Compares various loss functions commonly used in contrastive learning.
8. "Contrastive Learning with Limited Labeled Data: A Semi-Supervised Approach": Explores integrating contrastive learning with limited labeled data for improved performance.
9. "The Role of Negative Sampling in Contrastive Learning: A Theoretical and Empirical Analysis": Investigates the effects of different negative sampling strategies on contrastive learning.
| a theoretical analysis of contrastive unsupervised representation learning: Computer Vision – ECCV 2020 Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm, 2020-11-26 The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation. |
| a theoretical analysis of contrastive unsupervised representation learning: Computer Vision – ECCV 2022 Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner, 2022-11-08 The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation. |
| a theoretical analysis of contrastive unsupervised representation learning: Pattern Recognition and Computer Vision Shiqi Yu, Zhaoxiang Zhang, Pong C. Yuen, Junwei Han, Tieniu Tan, Yike Guo, Jianhuang Lai, Jianguo Zhang, 2022-10-27 The 4-volume set LNCS 13534, 13535, 13536 and 13537 constitutes the refereed proceedings of the 5th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2022, held in Shenzhen, China, in November 2022. The 233 full papers presented were carefully reviewed and selected from 564 submissions. The papers have been organized in the following topical sections: Theories and Feature Extraction; Machine learning, Multimedia and Multimodal; Optimization and Neural Network and Deep Learning; Biomedical Image Processing and Analysis; Pattern Classification and Clustering; 3D Computer Vision and Reconstruction, Robots and Autonomous Driving; Recognition, Remote Sensing; Vision Analysis and Understanding; Image Processing and Low-level Vision; Object Detection, Segmentation and Tracking. |
| a theoretical analysis of contrastive unsupervised representation learning: Neural Computing for Advanced Applications Haijun Zhang, Zhi Yang, Zhao Zhang, Zhou Wu, Tianyong Hao, 2021-08-20 This book presents refereed proceedings of the Second International Conference Neural Computing for Advanced Applications, NCAA 2021, held in Guangzhou, China, in August, 2021. The 54 full papers papers were thorougly reviewed and selected from a total of 144 qualified submissions. The papers are organized in topical sections on neural network theory, cognitive sciences, neuro-system hardware implementations, and NN-based engineering applications; machine learning, data mining, data security and privacy protection, and data-driven applications; neural computing-based fault diagnosis, fault forecasting, prognostic management, and system modeling; computational intelligence, nature-inspired optimizers, and their engineering applications; fuzzy logic, neuro-fuzzy systems, decision making, and their applications in management sciences; control systems, network synchronization, system integration, and industrial artificial intelligence; computer vision, image processing, and their industrial applications; cloud/edge/fog computing, the Internet of Things/Vehicles(IoT/IoV), and their system optimization; spreading dynamics, forecasting, and other intelligent techniques against coronavirus disease (COVID-19). |
| a theoretical analysis of contrastive unsupervised representation learning: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert, 2021-09-23 The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually. |
| a theoretical analysis of contrastive unsupervised representation learning: Neural Information Processing Biao Luo, Long Cheng, Zheng-Guang Wu, Hongyi Li, Chaojie Li, 2023-11-25 The nine-volume set constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 1274 papers presented in the proceedings set were carefully reviewed and selected from 652 submissions. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements. |
| a theoretical analysis of contrastive unsupervised representation learning: Proceedings of International Conference on Image, Vision and Intelligent Systems 2023 (ICIVIS 2023) Peng You, |
| a theoretical analysis of contrastive unsupervised representation learning: Artificial Intelligence Lu Fang, Yiran Chen, Guangtao Zhai, Jane Wang, Ruiping Wang, Weisheng Dong, 2022-01-01 This two-volume set LNCS 13069-13070 constitutes selected papers presented at the First CAAI International Conference on Artificial Intelligence, held in Hangzhou, China, in June 2021. Due to the COVID-19 pandemic the conference was partially held online. The 105 papers were thoroughly reviewed and selected from 307 qualified submissions. The papers are organized in topical sections on applications of AI; computer vision; data mining; explainability, understandability, and verifiability of AI; machine learning; natural language processing; robotics; and other AI related topics. |
| a theoretical analysis of contrastive unsupervised representation learning: The Semantic Web Albert Meroño Peñuela, |
| a theoretical analysis of contrastive unsupervised representation learning: Research in Computer Science Paulin Melatagia Yonta, |
| a theoretical analysis of contrastive unsupervised representation learning: Machine Learning and Knowledge Discovery in Databases: Research Track Danai Koutra, Claudia Plant, Manuel Gomez Rodriguez, Elena Baralis, Francesco Bonchi, 2023-09-17 The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: Robustness; Time Series; Transfer and Multitask Learning. Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo. |
| a theoretical analysis of contrastive unsupervised representation learning: Representation Learning for Natural Language Processing Zhiyuan Liu, Yankai Lin, Maosong Sun, 2023-08-23 This book provides an overview of the recent advances in representation learning theory, algorithms, and applications for natural language processing (NLP), ranging from word embeddings to pre-trained language models. It is divided into four parts. Part I presents the representation learning techniques for multiple language entries, including words, sentences and documents, as well as pre-training techniques. Part II then introduces the related representation techniques to NLP, including graphs, cross-modal entries, and robustness. Part III then introduces the representation techniques for the knowledge that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, legal domain knowledge and biomedical domain knowledge. Lastly, Part IV discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing. As compared to the first edition, the second edition (1) provides a more detailed introduction to representation learning in Chapter 1; (2) adds four new chapters to introduce pre-trained language models, robust representation learning, legal knowledge representation learning and biomedical knowledge representation learning; (3) updates recent advances in representation learning in all chapters; and (4) corrects some errors in the first edition. The new contents will be approximately 50%+ compared to the first edition. This is an open access book. |
| a theoretical analysis of contrastive unsupervised representation learning: Machine Learning from Weak Supervision Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakai, 2022-08-23 Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization. Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. In this book Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakai and Gang Niu present theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom. The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classification. It then explores problems of binary weakly supervised classification, including positive-unlabeled (PU) classification, positive-negative-unlabeled (PNU) classification, and unlabeled-unlabeled (UU) classification. It then turns to multiclass classification, discussing complementary-label (CL) classification and partial-label (PL) classification. Finally, the book addresses more advanced issues, including a family of correction methods to improve the generalization performance of weakly supervised learning and the problem of class-prior estimation. |
| a theoretical analysis of contrastive unsupervised representation learning: Deep Learning in Brain-Computer Interface Minkyu Ahn, Hong Gi Yeom, Hohyun Cho, Sung Chan Jun, 2022-06-06 |
| a theoretical analysis of contrastive unsupervised representation learning: ECAI 2023 K. Gal, A. Nowé, G.J. Nalepa, 2023-10-18 Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrate innovative applications and uses of advanced AI technology. ECAI 2023 received 1896 submissions – a record number – of which 1691 were retained for review, ultimately resulting in an acceptance rate of 23%. The 390 papers included here, cover topics including machine learning, natural language processing, multi agent systems, and vision and knowledge representation and reasoning. PAIS 2023 received 17 submissions, of which 10 were accepted after a rigorous review process. Those 10 papers cover topics ranging from fostering better working environments, behavior modeling and citizen science to large language models and neuro-symbolic applications, and are also included here. Presenting a comprehensive overview of current research and developments in AI, the book will be of interest to all those working in the field. |
| a theoretical analysis of contrastive unsupervised representation learning: Artificial Intelligent Techniques for Wireless Communication and Networking R. Kanthavel, K. Anathajothi, S. Balamurugan, R. Karthik Ganesh, 2022-03-22 ARTIFICIAL INTELLIGENT TECHNIQUES FOR WIRELESS COMMUNICATION AND NETWORKING The 20 chapters address AI principles and techniques used in wireless communication and networking and outline their benefit, function, and future role in the field. Wireless communication and networking based on AI concepts and techniques are explored in this book, specifically focusing on the current research in the field by highlighting empirical results along with theoretical concepts. The possibility of applying AI mechanisms towards security aspects in the communication domain is elaborated; also explored is the application side of integrated technologies that enhance AI-based innovations, insights, intelligent predictions, cost optimization, inventory management, identification processes, classification mechanisms, cooperative spectrum sensing techniques, ad-hoc network architecture, and protocol and simulation-based environments. Audience Researchers, industry IT engineers, and graduate students working on and implementing AI-based wireless sensor networks, 5G, IoT, deep learning, reinforcement learning, and robotics in WSN, and related technologies. |
| a theoretical analysis of contrastive unsupervised representation learning: Computer Vision – ACCV 2022 Lei Wang, Juergen Gall, Tat-Jun Chin, Imari Sato, Rama Chellappa, 2023-03-01 The 7-volume set of LNCS 13841-13847 constitutes the proceedings of the 16th Asian Conference on Computer Vision, ACCV 2022, held in Macao, China, December 2022. The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; optimization methods; Part II: applications of computer vision, vision for X; computational photography, sensing, and display; Part III: low-level vision, image processing; Part IV: face and gesture; pose and action; video analysis and event recognition; vision and language; biometrics; Part V: recognition: feature detection, indexing, matching, and shape representation; datasets and performance analysis; Part VI: biomedical image analysis; deep learning for computer vision; Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods. |
| a theoretical analysis of contrastive unsupervised representation learning: Graph Representation Learning William L. William L. Hamilton, 2022-06-01 Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning. |
| a theoretical analysis of contrastive unsupervised representation learning: Systems Modeling: Approaches and Applications - Volume II Alberto Jesus Martin, Ernesto Perez-Rueda, Daniel Garrido, 2022-11-25 |
| a theoretical analysis of contrastive unsupervised representation learning: Advances in Knowledge Discovery and Data Mining Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng, 2023-05-27 The 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25–28, 2023. The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations. |
| a theoretical analysis of contrastive unsupervised representation learning: Database Systems for Advanced Applications Arnab Bhattacharya, Janice Lee Mong Li, Divyakant Agrawal, P. Krishna Reddy, Mukesh Mohania, Anirban Mondal, Vikram Goyal, Rage Uday Kiran, 2022-04-26 The three-volume set LNCS 13245, 13246 and 13247 constitutes the proceedings of the 26th International Conference on Database Systems for Advanced Applications, DASFAA 2022, held online, in April 2021. The total of 72 full papers, along with 76 short papers, are presented in this three-volume set was carefully reviewed and selected from 543 submissions. Additionally, 13 industrial papers, 9 demo papers and 2 PhD consortium papers are included. The conference was planned to take place in Hyderabad, India, but it was held virtually due to the COVID-19 pandemic. |
| a theoretical analysis of contrastive unsupervised representation learning: Computational Data and Social Networks Minh Hoàng Hà, |
| a theoretical analysis of contrastive unsupervised representation learning: Representation Learning for Natural Language Processing Zhiyuan Liu, Yankai Lin, Maosong Sun, 2020-07-03 This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing. |
| a theoretical analysis of contrastive unsupervised representation learning: Interpretable Machine Learning Christoph Molnar, 2020 This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project. |
| a theoretical analysis of contrastive unsupervised representation learning: Deep Learning Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016-11-10 An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. |
| a theoretical analysis of contrastive unsupervised representation learning: Quantifying Approaches to Discourse for Social Scientists Ronny Scholz, 2018-11-27 This book provides an overview of a range of quantitative methods, presenting a thorough analytical toolbox which will be of practical use to researchers across the social sciences as they face the challenges raised by new technology-driven language practices. The book is driven by a reflexive mind-set which views quantifying methods as complementary rather than in opposition to qualitative methods, and the chapters analyse a multitude of different intra- and extra-textual context levels essential for the understanding of how meaning is (re-)constructed in society. Uniting contributions from a range of national and disciplinary traditions, the chapters in this volume bring together state-of-the-art research from British, Canadian, French, German and Swiss authors representing the fields of Political Science, Sociology, Linguistics, Computer Science and Statistics. It will be of particular interest to discourse analysts, but also to other scholars working in the digital humanities and with big data of any kind. |
| a theoretical analysis of contrastive unsupervised representation learning: Introduction to Semi-Supervised Learning Xiaojin Geffner, Andrew Bazzan, 2022-05-31 Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines / Human Semi-Supervised Learning / Theory and Outlook |
| a theoretical analysis of contrastive unsupervised representation learning: Foundations of Modern Potential Theory Naum S. Landkof, 2011-11-15 For a long time potential theory was necessarily viewed as only another chapter of mathematical physics. Developing in close connection with the theory of boundary-value problems for the Laplace operator, it led to the creation of the mathematical apparatus of potentials of single and double layers; this was adequate for treating problems involving smooth boundaries. A. M. Lyapunov is to be credited with the rigorous analysis of the properties of potentials and the possibilities for applying them to the 1 solution of boundary-value problems. The results he obtained at the end of the 19th century later received a more detailed and sharpened exposition in the book by N. M. Gunter, published in Paris in 1934 and 2 in New York 1967 with additions and revisions. Of fundamental significance to potential theory also was the work of H. Poincare, especially his method of sweeping out mass (balayage). At the beginning of the 20th century the work of S. Zaremba and especially of H. Lebesgue attracted the attention of mathematicians to the unsolvable cases of the classical Dirichlet problem. Through the efforts of O. Kellogg, G. Bouligand, and primarily N. Wiener, by the middle of the 20th century the problem of characterizing the so-called irregular points of the boundary of a region (i. e. the points at which the continuity of the solution of the Dirichlet problem may be violated) was completely solved and a procedure to obtain a generalized solution to the Dirichlet problem was described. |
| a theoretical analysis of contrastive unsupervised representation learning: Collected Papers of Salomon Bochner Salomon Bochner, 1992 Collected papers of Salomon Bochner, American mathematician, known for work in mathematical analysis, probability theory and differential geometry. |
| a theoretical analysis of contrastive unsupervised representation learning: Information Geometry and Its Applications Shun-ichi Amari, 2016-02-02 This is the first comprehensive book on information geometry, written by the founder of the field. It begins with an elementary introduction to dualistic geometry and proceeds to a wide range of applications, covering information science, engineering, and neuroscience. It consists of four parts, which on the whole can be read independently. A manifold with a divergence function is first introduced, leading directly to dualistic structure, the heart of information geometry. This part (Part I) can be apprehended without any knowledge of differential geometry. An intuitive explanation of modern differential geometry then follows in Part II, although the book is for the most part understandable without modern differential geometry. Information geometry of statistical inference, including time series analysis and semiparametric estimation (the Neyman–Scott problem), is demonstrated concisely in Part III. Applications addressed in Part IV include hot current topics in machine learning, signal processing, optimization, and neural networks. The book is interdisciplinary, connecting mathematics, information sciences, physics, and neurosciences, inviting readers to a new world of information and geometry. This book is highly recommended to graduate students and researchers who seek new mathematical methods and tools useful in their own fields. |
| a theoretical analysis of contrastive unsupervised representation learning: Computer Vision – ECCV 2024 Aleš Leonardis, |
| a theoretical analysis of contrastive unsupervised representation learning: Online Portfolio Selection Bin Li, Steven Chu Hong Hoi, 2018-10-30 With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment. The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strategies in action and to provide readers with the confidence to deploy the strategies themselves. The book is presented in five sections that: Introduce OLPS and formulate OLPS as a sequential decision task Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms with the state of the art Investigate possible future directions Complete with a back-test system that uses historical data to evaluate the performance of trading strategies, as well as MATLAB® code for the back-test systems, this book is an ideal resource for graduate students in finance, computer science, and statistics. It is also suitable for researchers and engineers interested in computational investment. Readers are encouraged to visit the authors’ website for updates: http://olps.stevenhoi.org. |
| a theoretical analysis of contrastive unsupervised representation learning: Explainable and Interpretable Models in Computer Vision and Machine Learning Hugo Jair Escalante, Sergio Escalera, Isabelle Guyon, Xavier Baró, Yağmur Güçlütürk, Umut Güçlü, Marcel van Gerven, 2018-11-29 This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations |
| a theoretical analysis of contrastive unsupervised representation learning: Neural Computation in Hopfield Networks and Boltzmann Machines James P. Coughlin, Robert H. Baran, 1995 One hundred years ago, the fundamental building block of the central nervous system, the neuron, was discovered. This study focuses on the existing mathematical models of neurons and their interactions, the simulation of which has been one of the biggest challenges facing modern science. More than fifty years ago, W. S. McCulloch and W. Pitts devised their model for the neuron, John von Neumann seemed to sense the possibilities for the development of intelligent systems, and Frank Rosenblatt came up with a functioning network of neurons. Despite these advances, the subject had begun to fade as a major research area until John Hopfield arrived on the scene. Drawing an analogy between neural networks and the Ising spin models of ferromagnetism, Hopfield was able to introduce a computational energy that would decline toward stable minima under the operation of the system of neurodynamics devised by Roy Glauber. Like a switch, a neuron is said to be either on or off. The state of the neuron is determined by the states of the other neurons and the connections between them, and the connections are assumed to be reciprocal - that is, neuron number one influences neuron number two exactly as strongly as neuron number two influences neuron number one. According to the Glauber dynamics, the states of the neurons are updated in a random serial way until an equilibrium is reached. An energy function can be associated with each state, and equilibrium corresponds to a minimum of this energy. It follows from Hopfield's assumption of reciprocity that an equilibrium will always be reached. D. H. Ackley, G. E. Hinton, and T. J. Sejnowski modified the Hopfield network by introducing the simulated annealing algorithm to search out the deepest minima. This is accomplished by - loosely speaking - shaking the machine. The violence of the shaking is controlled by a parameter called temperature, producing the Boltzmann machine - a name designed to emphasize the connection to the statistical physics of Ising spin models. The Boltzmann machine reduces to the Hopfield model in the special case where the temperature goes to zero. The resulting network, under the Glauber dynamics, produces a homogeneous, irreducible, aperiodic Markov chain as it wanders through state space. The entire theory of Markov chains becomes applicable to the Boltzmann machine. With ten chapters, five appendices, a list of references, and an index, this study should serve as an introduction to the field of neural networks and its application, and is suitable for an introductory graduate course or an advanced undergraduate course.--BOOK JACKET.Title Summary field provided by Blackwell North America, Inc. All Rights Reserved |
| a theoretical analysis of contrastive unsupervised representation learning: Introduction to Statistical Machine Learning Masashi Sugiyama, 2015-10-31 Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. - Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus - Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning - Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks - Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials |
| a theoretical analysis of contrastive unsupervised representation learning: Adversarial Machine Learning Aneesh Sreevallabh Chivukula, Xinghao Yang, Bo Liu, Wei Liu, Wanlei Zhou, 2023-03-06 A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning. |
| a theoretical analysis of contrastive unsupervised representation learning: Deep Learning and Physics Akinori Tanaka, Akio Tomiya, Koji Hashimoto, 2021-03-24 What is deep learning for those who study physics? Is it completely different from physics? Or is it similar? In recent years, machine learning, including deep learning, has begun to be used in various physics studies. Why is that? Is knowing physics useful in machine learning? Conversely, is knowing machine learning useful in physics? This book is devoted to answers of these questions. Starting with basic ideas of physics, neural networks are derived naturally. And you can learn the concepts of deep learning through the words of physics. In fact, the foundation of machine learning can be attributed to physical concepts. Hamiltonians that determine physical systems characterize various machine learning structures. Statistical physics given by Hamiltonians defines machine learning by neural networks. Furthermore, solving inverse problems in physics through machine learning and generalization essentially provides progress and even revolutions in physics. For these reasons, in recent years interdisciplinary research in machine learning and physics has been expanding dramatically. This book is written for anyone who wants to learn, understand, and apply the relationship between deep learning/machine learning and physics. All that is needed to read this book are the basic concepts in physics: energy and Hamiltonians. The concepts of statistical mechanics and the bracket notation of quantum mechanics, which are explained in columns, are used to explain deep learning frameworks. We encourage you to explore this new active field of machine learning and physics, with this book as a map of the continent to be explored. |
| a theoretical analysis of contrastive unsupervised representation learning: Introduction to Deep Learning Sandro Skansi, 2018-02-04 This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website. Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology. |
| a theoretical analysis of contrastive unsupervised representation learning: Supervised Learning with Quantum Computers Maria Schuld, Francesco Petruccione, 2018-08-30 Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices. |
| a theoretical analysis of contrastive unsupervised representation learning: Independent Component Analysis and Signal Separation Tulay Adali, Christian Jutten, Joao Marcos Travassos Romano, Allan Kardec Barros, 2009-03-16 This book constitutes the refereed proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation, ICA 2009, held in Paraty, Brazil, in March 2009. The 97 revised papers presented were carefully reviewed and selected from 137 submissions. The papers are organized in topical sections on theory, algorithms and architectures, biomedical applications, image processing, speech and audio processing, other applications, as well as a special session on evaluation. |
THEORETICAL Definition & Meaning - Merriam-Webster
The meaning of THEORETICAL is existing only in theory : hypothetical. How to use theoretical in a sentence.
THEORETICAL | English meaning - Cambridge Dictionary
THEORETICAL definition: 1. based on the ideas that relate to a subject, not the practical uses of that subject: 2. related…. Learn more.
Theoretical Definition & Meaning | Britannica Dictionary
THEORETICAL meaning: 1 : relating to what is possible or imagined rather than to what is known to be true or real; 2 : relating to the general principles or ideas of a subject rather than the practical …
theoretical adjective - Definition, pictures, pronunciation and …
Definition of theoretical adjective from the Oxford Advanced Learner's Dictionary. connected with the ideas and principles on which a particular subject is based, rather than with practice and …
Theoretical - definition of theoretical by The Free Dictionary
theoretical - concerned primarily with theories or hypotheses rather than practical considerations; "theoretical science"
What does Theoretical mean? - Definitions.net
Theoretical refers to something that is based on theories or principles and not connected with practical or concrete implementation. It involves ideas, concepts, and intellectual reasoning …
Theoretical - Definition, Meaning & Synonyms - Vocabulary.com
Something theoretical is concerned with theories and hypotheses — it's not necessarily based on real life or meant to be applied to real life. Theoretical things are based on theory and ideas, …
THEORETICAL Definition & Meaning | Dictionary.com
Theoretical definition: of, relating to, or consisting in theory; not practical (applied ).. See examples of THEORETICAL used in a sentence.
Theoretical: Explanation and Examples - Philosophy Terms
Definition of Theoretical. Let’s start with a cool word: “theoretical”. It sounds pretty fancy, right? But it’s actually not too complicated. Picture something that’s theoretical as something that lives …
THEORETICAL definition in American English - Collins Online …
A theoretical study or explanation is based on or uses the ideas and abstract principles that relate to a particular subject, rather than the practical aspects or uses of it.
THEORETICAL Definition & Meaning - Merriam-Webster
The meaning of THEORETICAL is existing only in theory : hypothetical. How to use theoretical in a sentence.
THEORETICAL | English meaning - Cambridge Diction…
THEORETICAL definition: 1. based on the ideas that relate to a subject, not the practical uses of that subject: 2. …
Theoretical Definition & Meaning | Britannica Dictiona…
THEORETICAL meaning: 1 : relating to what is possible or imagined rather than to what is known to be true or real; 2 : relating to the general …
theoretical adjective - Definition, pictures, pronunci…
Definition of theoretical adjective from the Oxford Advanced Learner's Dictionary. connected with the ideas and principles on which a particular …
Theoretical - definition of theoretical by The Free Dicti…
theoretical - concerned primarily with theories or hypotheses rather than practical considerations; "theoretical science"
