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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)

Papers per year

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20
220
477
4,652
9,258
2000
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Papers (including subtopics)

Learning Transferable Features for Point Cloud Detection via 3D Contrastive Co-training NeurIPS 2021 Learning Transferable Visual Models From Natural Language Supervision ICML 2021 Learning Treatment Effects in Panels with General Intervention Patterns NeurIPS 2021 Learning Universal Authorship Representations EMNLP 2021 Learning Unsupervised Metaformer for Anomaly Detection ICCV 2021 Learning Value Functions in Deep Policy Gradients using Residual Variance ICLR 2021 Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization CVPR 2021 Learning Visible Connectivity Dynamics for Cloth Smoothing CoRL 2021 Learning Visual Words for Weakly-Supervised Semantic Segmentation IJCAI 2021 Learning "What-if" Explanations for Sequential Decision-Making ICLR 2021 Learning What To Do by Simulating the Past ICLR 2021 Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes JMLR 2021 Learning where to learn: Gradient sparsity in meta and continual learning NeurIPS 2021 Learning While Playing in Mean-Field Games: Convergence and Optimality ICML 2021 Learning with Algorithmic Supervision via Continuous Relaxations NeurIPS 2021 Learning with Different Amounts of Annotation: From Zero to Many Labels EMNLP 2021 Learning with Feature-Dependent Label Noise: A Progressive Approach ICLR 2021 Learning with Group Noise AAAI 2021 Learning with Holographic Reduced Representations NeurIPS 2021 Learning with Instance Bundles for Reading Comprehension EMNLP 2021 Learning with Instance-Dependent Label Noise: A Sample Sieve Approach ICLR 2021 Learning with Labeling Induced Abstentions NeurIPS 2021 Learning With Memory-Based Virtual Classes for Deep Metric Learning ICCV 2021 Learning with Noisy Correspondence for Cross-modal Matching NeurIPS 2021 Learning With Noisy Labels for Robust Point Cloud Segmentation ICCV 2021