A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
David Johnson, Christos H. Papadimitriou, Kenneth Steiglitz
A comprehensive textbook on combinatorial optimization covering algorithms, complexity, NP-completeness, and approximation methods.
Unknown
This paper provides a practical cookbook of fundamental self-supervised learning techniques, serving as a foundation for training and exploring SSL methods.
Unknown
This book part establishes foundational concepts and modern approaches for multi-agent reinforcement learning, defining the learning problem and building upon prior work.
Unknown
This book focuses on adversarial robustness in machine learning, covering topics like GANs, multiagent systems, and game-oriented learning.
H. Andrew Schwartz, Johannes C. Eichstaedt, Margaret L. Kern, et al.
Analyzed 700 million words from 75,000 Facebook users to reveal how personality, gender, and age are reflected in natural language using an open-vocabulary approach.
William Stallings
A comprehensive textbook survey of cryptography and network security principles and practice, covering both foundational concepts and modern applications.
Krzysztof Czarnecki, Ulrich W. Eisenecker
This book introduces generative programming, a paradigm for automating software component assembly using domain engineering, feature modeling, and code generation.
Dan Gusfield
A comprehensive textbook on string algorithms, suffix trees, and sequence alignment with applications in computational biology.
Anil K. Jain
A comprehensive textbook covering fundamental digital image processing techniques including transforms, enhancement, restoration, analysis, compression, and reconstruction.
Zhi‐Hua Zhou
A comprehensive textbook covering ensemble methods including Boosting, Bagging, Random Forest, and diversity measures.
Kevin P. Murphy
A comprehensive textbook introducing machine learning through a unified probabilistic perspective, covering foundational theory and modern developments.
Daniel L. Koller, Nir Friedman
This book provides a comprehensive framework for probabilistic graphical models, covering representation, inference, and learning for Bayesian networks, Markov networks, and extensions to dynamical and relational data.