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
'5
'10
'15
'20
'25
Papers (including subtopics)
What Variables Affect Out-of-Distribution Generalization in Pretrained Models?
NeurIPS 2024
What When and Where? Self-Supervised Spatio-Temporal Grounding in Untrimmed Multi-Action Videos from Narrated Instructions
CVPR 2024
When 3D Bounding-Box Meets SAM: Point Cloud Instance Segmentation With Weak-and-Noisy Supervision
WACV 2024
When ”A Helpful Assistant” Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models
EMNLP 2024
When are dynamical systems learned from time series data statistically accurate?
NeurIPS 2024
When Are Two Lists Better than One?: Benefits and Harms in Joint Decision-Making
AAAI 2024
When Argumentation Meets Cohesion: Enhancing Automatic Feedback in Student Writing
COLING 2024
When Causal Inference Meets Graph Machine Learning
AAAI 2024
When Does Monolingual Data Help Multilingual Translation: The Role of Domain and Model Scale
NAACL 2024
When does perceptual alignment benefit vision representations?
NeurIPS 2024
When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets
EACL 2024
When Do LLMs Need Retrieval Augmentation? Mitigating LLMs’ Overconfidence Helps Retrieval Augmentation
ACL 2024
When Generative Adversarial Networks Meet Sequence Labeling Challenges
EMNLP 2024
When is a Metaphor Actually Novel? Annotating Metaphor Novelty in the Context of Automatic Metaphor Detection
EACL 2024
When Is a Name Sensitive? Eponyms in Clinical Text and Implications for De-Identification
EACL 2024
When Is Inductive Inference Possible?
NeurIPS 2024
When is Multicalibration Post-Processing Necessary?
NeurIPS 2024
When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languages
EMNLP 2024
When Life Gives You Lemons, Make Cherryade: Converting Feedback from Bad Responses into Good Labels
NAACL 2024
When LLM Meets DRL: Advancing Jailbreaking Efficiency via DRL-guided Search
NeurIPS 2024
When LLMs Meets Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large Language Models for Depression Detection
EMNLP 2024
When Model Meets New Normals: Test-Time Adaptation for Unsupervised Time-Series Anomaly Detection
AAAI 2024
When No-Rejection Learning is Consistent for Regression with Rejection
AISTATS 2024
When Parts Are Greater Than Sums: Individual LLM Components Can Outperform Full Models
EMNLP 2024
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?
EMNLP 2024