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Learning Types

56,253 papers (3 classified directly here)

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)

Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning ACL 2021 Uncovering Latent Biases in Text: Method and Application to Peer Review AAAI 2021 Uncovering Main Causalities for Long-tailed Information Extraction EMNLP 2021 Uncovering the Acoustic Cues of COVID-19 Infection Interspeech 2021 Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability ICML 2021 Understanding and Improving Early Stopping for Learning with Noisy Labels NeurIPS 2021 Understanding and Interpreting the Impact of User Context in Hate Speech Detection NAACL 2021 Understanding Bandits with Graph Feedback NeurIPS 2021 Understanding Catastrophic Overfitting in Single-step Adversarial Training AAAI 2021 Understanding Decoupled and Early Weight Decay AAAI 2021 Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics NAACL 2021 Understanding Failures in Out-of-Distribution Detection with Deep Generative Models ICML 2021 Understanding Hard Negatives in Noise Contrastive Estimation NAACL 2021 Understanding Instance-Level Label Noise: Disparate Impacts and Treatments ICML 2021 Understanding Model Robustness to User-generated Noisy Texts EMNLP 2021 Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning NeurIPS 2021 Understanding Over-parameterization in Generative Adversarial Networks ICLR 2021 Understanding Partial Multi-Label Learning via Mutual Information NeurIPS 2021 Understanding Robustness in Teacher-Student Setting: A New Perspective AISTATS 2021 Understanding self-supervised learning dynamics without contrastive pairs ICML 2021 Understanding Structural Vulnerability in Graph Convolutional Networks IJCAI 2021 Understanding the Behaviour of Contrastive Loss CVPR 2021 Understanding the Effect of Stochasticity in Policy Optimization NeurIPS 2021 Understanding the failure modes of out-of-distribution generalization ICLR 2021 Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning NeurIPS 2021