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
1
8
12
26
18
20
135
136
147
149
220
231
316
526
460
477
660
1,314
2,083
3,689
4,652
5,914
6,826
8,201
8,755
9,258
2,019
2000
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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