Learning Types
32,670 papers (1 classified directly here)
Subtopics · a paper can sit in several, so these overlap
Representation Learning (5,307)
Multimodal Learning (3,969)
Self-Supervised Learning (3,492)
Transfer Learning (2,890)
Adversarial Learning (2,199)
Deep Learning (1,883)
Contrastive Learning (1,730)
Reinforcement Learning (1,500)
Knowledge Distillation (1,494)
Fine-Tuning (1,117)
Multi-Task Learning (1,069)
Domain Adaptation (977)
Zero-Shot Learning (758)
Unsupervised Learning (734)
Few-Shot Learning (694)
Classification (621)
Retrieval-Augmented Generation (606)
Data Augmentation (583)
Semi-Supervised Learning (450)
Weakly Supervised Learning (429)
Continual Learning (408)
In-Context Learning (387)
Sequence Modeling (375)
Meta-Learning (252)
Metric Learning (232)
Ensemble Learning (223)
Federated Learning (215)
Multi-Lingual Learning (199)
Robustness (197)
Parameter-Efficient Fine-Tuning (190)
Imitation Learning (180)
Prompt Engineering (170)
Chain-of-Thought Reasoning (153)
Curriculum Learning (146)
Synthetic Data Generation (143)
Reinforcement Learning from Human Feedback (141)
Multi-Label Classification (123)
Regression (117)
Generative Model (109)
Semantic Segmentation (106)
Domain Generalization (105)
Uncertainty Quantification (98)
Controllable Generation (93)
Text Generation (86)
Neural Architecture Search (80)
Object Detection (80)
Image Generation (79)
Multi-Agent Systems (76)
Feature Learning (74)
Preference Learning (73)
Machine Translation (71)
Out-of-Distribution Detection (68)
Machine Unlearning (63)
Generation (61)
Reasoning (54)
Multi-View Learning (53)
Time Series (52)
Instruction Tuning (50)
Distributed Learning (50)
Image Restoration (49)
Chain-of-Thought (48)
Active Learning (47)
Text Classification (43)
Distribution Shift (43)
Structured Prediction (42)
Clustering (41)
Image Classification (40)
Model Merging (37)
Sparse Learning (36)
Sequence Labeling (36)
Multi-Instance Learning (35)
Anomaly Detection (33)
Image Processing (33)
Code Generation (31)
Fairness (30)
Robust Learning (28)
Multi-Domain Learning (27)
Supervised Learning (26)
Model-Based RL (25)
Natural Language Generation (24)
Privacy (21)
Multi-Objective Optimization (19)
Multi-Scale Learning (19)
Neural Machine Translation (18)
Causal Inference (14)
Instance Segmentation (9)
Interactive Learning (9)
Multi-Label Learning (9)
Multi-Source Learning (9)
Multi-Class Classification (8)
Papers per year
1
22
25
29
26
38
46
60
109
129
179
310
686
1,294
2,286
3,018
3,254
3,930
4,067
4,819
5,737
2,605
'10
'15
'20
'25
Papers (including subtopics)
Weak Reward Model Transforms Generative Models into Robust Causal Event Extraction Systems
EMNLP 2024
Weak-to-Strong 3D Object Detection with X-Ray Distillation
CVPR 2024
Weak-to-Strong Reasoning
EMNLP 2024
WebCiteS: Attributed Query-Focused Summarization on Chinese Web Search Results with Citations
ACL 2024
Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach
NeurIPS 2024
WeditGAN: Few-Shot Image Generation via Latent Space Relocation
AAAI 2024
WeiPer: OOD Detection using Weight Perturbations of Class Projections
NeurIPS 2024
Well, Now We Know! Unveiling Sarcasm: Initiating and Exploring Multimodal Conversations with Reasoning
AAAI 2024
What Are You Token About? Differentiable Perturbed Top-k Token Selection for Scientific Document Summarization
ACL 2024
What Do Language Models Hear? Probing for Auditory Representations in Language Models
ACL 2024
What Do Language Models Learn in Context? The Structured Task Hypothesis.
ACL 2024
What Effects the Generalization in Visual Reinforcement Learning: Policy Consistency with Truncated Return Prediction
AAAI 2024
What Have We Achieved on Non-autoregressive Translation?
ACL 2024
What How and When Should Object Detectors Update in Continually Changing Test Domains?
CVPR 2024
What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models
EMNLP 2024
“What is the value of templates?” Rethinking Document Information Extraction Datasets for LLMs
EMNLP 2024
What Makes and Breaks Safety Fine-tuning? A Mechanistic Study
NeurIPS 2024
What Makes CLIP More Robust to Long-Tailed Pre-Training Data? A Controlled Study for Transferable Insights
NeurIPS 2024
What Makes Good Collaborative Views? Contrastive Mutual Information Maximization for Multi-Agent Perception
AAAI 2024
What Makes Partial-Label Learning Algorithms Effective?
NeurIPS 2024
What Matters in Graph Class Incremental Learning? An Information Preservation Perspective
NeurIPS 2024
What Matters in Memorizing and Recalling Facts? Multifaceted Benchmarks for Knowledge Probing in Language Models
EMNLP 2024
What matters when building vision-language models?
NeurIPS 2024
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
AAAI 2024
What When and Where? Self-Supervised Spatio-Temporal Grounding in Untrimmed Multi-Action Videos from Narrated Instructions
CVPR 2024