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)
Wasserstein Gradient Flows for Scalable and Regularized Barycenter Computation
UAI 2026
Watch and Learn: Learning to Use Computers from Online Videos
CVPR 2026
Weakly Supervised Video Anomaly Detection with Anomaly-Connected Components and Intention Reasoning
CVPR 2026
WeaveTime: Streaming from Earlier Frames into Emergent Memory in VideoLLMs
CVPR 2026
WebSTAR: Scalable Data Synthesis for Computer Use Agents with Step-Level Filtering
ACL 2026
WeightLoRA: Keep Only Necessary Adapters
ACL 2026
Weight Space Representation Learning via Neural Field Adaptation
CVPR 2026
WeMMU: Enhanced Bridging of Vision-Language Models and Diffusion Models via Noisy Query Tokens
CVPR 2026
WFM: 3D Wavelet Flow Matching for Ultrafast Multi-Modal MRI Synthesis
MIDL 2026
What Brain Data Adds to Language Model Training
CoNLL 2026
What Do LLMs Learn First? Asymmetric Learning Dynamics of Input Complexity and Output Ambiguity in Preference Alignment
ACL 2026
What Fine-Tuning Changes: A Radiomic Lens on Prostate Foundation Model Representations
MIDL 2026
What Is It Like to Be a Noise? An Entropy-based Gaussian Noise Regularization for Diffusion Models
CVPR 2026
What Makes a Good Curriculum? Disentangling the Effects of Data Ordering on LLM Mathematical Reasoning
ACL 2026
What Makes Good Instruction-Tuning Data? An In-Context Learning Perspective
ACL 2026
What Makes Good Synthetic Training Data for Zero-Shot Stereo Matching?
CVPR 2026
What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context
ACL 2026
What Matters in Practical Learned Image Compression
CVPR 2026
What's Wrong with Synthetic Data for Scene Text Recognition? A Strong Synthetic Engine with Diverse Simulations and Self-Evolution
CVPR 2026
When Agents Look the Same: Quantifying Distillation-Induced Similarity in Tool-Use Behaviors
ACL 2026
When Benchmarks Leak: Inference-Time Decontamination for LLMs
ACL 2026
When in Doubt, Consult: Expert Debate for Sexism Detection via Confidence-Based Routing
ACL 2026
When Local Rules Create Global Order: Self-Organized Representation Learning for Latent Diffusion Models
CVPR 2026
When Pretty Isn't Useful: Investigating Why Modern Text-to-Image Models Fail as Reliable Training Data Generators
CVPR 2026
When Visualizing is the First Step to Reasoning: MIRA, a Benchmark for Visual Chain-of-Thought
CVPR 2026