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Learning Types

32,670 papers (1 classified directly here)

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)
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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