conftrace_

Learning Types

56,253 papers (3 classified directly here)

Subtopics · a paper can sit in several, so these overlap

Self-Supervised Learning (8,965) Adversarial Learning (5,204) Unsupervised Learning (4,874) Transfer Learning (4,808) Weakly Supervised Learning (3,938) Contrastive Learning (3,748) Zero-Shot Learning (3,130) Semi-Supervised Learning (3,128) Reinforcement Learning (3,118) Multi-Task Learning (2,556) Evaluation (2,164) Continual Learning (2,107) Active Learning (1,983) Few-Shot Learning (1,840) Deep Learning (1,404) Online Learning (1,360) Multi-Armed Bandits (1,327) Multimodal Learning (1,289) Supervised Learning (986) Multi-Agent Systems (813) Uncertainty Quantification (805) Causal Inference (636) Robustness (576) Retrieval-Augmented Generation (565) Fine-Tuning (490) Ensemble Learning (482) In-Context Learning (439) Multi-Label Classification (430) Imitation Learning (387) Prompt Engineering (270) Multi-Lingual Learning (269) Distribution Shift (264) Reinforcement Learning from Human Feedback (209) Regularization (207) Exploration-Exploitation (203) Generative Models (200) Multi-Objective Optimization (185) Out-of-Distribution Detection (176) Sparse Learning (165) Sampling (150) Preference Learning (136) Offline Reinforcement Learning (130) Multi-Label Learning (128) Markov Decision Process (127) Neural Architecture Search (122) Multi-Instance Learning (121) Multi-View Learning (117) Robust Learning (110) Generative Model (110) Time Series (108) Large Language Models (104) Exploration (103) Hyperparameter Optimization (99) Machine Unlearning (95) Imbalanced Learning (93) Machine Translation (93) Multi-Class Classification (85) Differential Privacy (82) Time Series Analysis (71) Reasoning (70) Calibration (67) Structure Learning (66) Physics-Informed Learning (62) Class Imbalance (58) Model-Based RL (55) Interactive Learning (55) Multi-Domain Learning (47) Inverse Reinforcement Learning (46) Crowdsourcing (40) Off-Policy Learning (38) Label Noise (38) Noisy Label Learning (35) Offline RL (32) Attention (32) Evolutionary Algorithm (32) Decision Making (31) Policy Learning (30) Personalization (28) Low-Resource Learning (26) Collaborative Filtering (25) Self-Training (24) Sequential Modeling (24) Parameter Estimation (23) Survival Analysis (21) Machine Learning (20) Parameter-Efficient Fine-Tuning (20) Synthetic Data Generation (20) Annotation (18) Symbolic Regression (18) AutoML (16) Security (15) Multi-Source Learning (13) Distant Supervision (13) Text Generation (11) Distributional Reinforcement Learning (9) Sentiment Analysis (9) Multi-Hop Reasoning (9) Language Modeling (9) Code Generation (8) Multi-Object Tracking (8) Natural Language Inference (7) Chain-of-Thought (6) Question Answering (5)

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Papers (including subtopics)

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion JMLR 2010 Structural epitome: a way to summarize one’s visual experience NeurIPS 2010 Structured sparsity-inducing norms through submodular functions NeurIPS 2010 Subgraph Detection Using Eigenvector L1 Norms NeurIPS 2010 Sufficient Conditions for Generating Group Level Sparsity in a Robust Minimax Framework NeurIPS 2010 Supervised Clustering NeurIPS 2010 Synergies in learning words and their referents NeurIPS 2010 The LASSO risk: asymptotic results and real world examples NeurIPS 2010 The Multidimensional Wisdom of Crowds NeurIPS 2010 Throttling Poisson Processes NeurIPS 2010 t-logistic regression NeurIPS 2010 Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models NeurIPS 2010 Towards Property-Based Classification of Clustering Paradigms NeurIPS 2010 Towards Understanding Situated Natural Language AISTATS 2010 Trading off Mistakes and Don't-Know Predictions NeurIPS 2010 Transduction with Matrix Completion: Three Birds with One Stone NeurIPS 2010 Universal Consistency of Multi-Class Support Vector Classification NeurIPS 2010 Unsupervised Aggregation for Classification Problems with Large Numbers of Categories AISTATS 2010 Unsupervised Kernel Dimension Reduction NeurIPS 2010 Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels JMLR 2010 Using Contextual Representations to Efficiently Learn Context-Free Languages JMLR 2010 Using Descendants as Instrumental Variables for the Identification of Direct Causal Effects in Linear SEMs AISTATS 2010 Variable margin losses for classifier design NeurIPS 2010 Why Does Unsupervised Pre-training Help Deep Learning? AISTATS 2010 Why Does Unsupervised Pre-training Help Deep Learning? JMLR 2010