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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)

What Really Matters in Many-Shot Attacks? An Empirical Study of Long-Context Vulnerabilities in LLMs ACL 2025 What’s in a prompt? Language models encode literary style in prompt embeddings EMNLP 2025 What's in the Image? A Deep-Dive into the Vision of Vision Language Models CVPR 2025 What's Making That Sound Right Now? Video-centric Audio-Visual Localization ICCV 2025 What to Distill? Fast Knowledge Distillation with Adaptive Sampling ICCV 2025 When2Call: When (not) to Call Tools NAACL 2025 When and How Does CLIP Enable Domain and Compositional Generalization? ICML 2025 When and How to Augment Your Input: Question Routing Helps Balance the Accuracy and Efficiency of Large Language Models NAACL 2025 When and Where do Data Poisons Attack Textual Inversion? ICCV 2025 When Big Models Train Small Ones: Label-Free Model Parity Alignment for Efficient Visual Question Answering using Small VLMs EMNLP 2025 When Can We Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis? AAAI 2025 When Cars Meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse Weather WACV 2025 When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need ICML 2025 When Dynamic Data Selection Meets Data Augmentation: Achieving Enhanced Training Acceleration ICML 2025 When Hypergraph Meets Heterophily: New Benchmark Datasets and Baseline AAAI 2025 When Long Helps Short: How Context Length in Supervised Fine-tuning Affects Behavior of Large Language Models EMNLP 2025 When natural language is not enough: The limits of in-context learning demonstrations in multilingual reasoning NAACL 2025 When Should We Prefer State-to-Visual DAgger over Visual Reinforcement Learning? AAAI 2025 When the Future Becomes the Past: Taming Temporal Correspondence for Self-supervised Video Representation Learning CVPR 2025 When The MOS Predictor Asks For Training Annotation In Cross Lingual/Domain Adaptation Interspeech 2025 When to Continue Thinking: Adaptive Thinking Mode Switching for Efficient Reasoning EMNLP 2025 When to Learn and When to Stop: Quitting at the Optimal Time (Student Abstract) AAAI 2025 When to Speak, When to Abstain: Contrastive Decoding with Abstention ACL 2025 When Truthful Representations Flip Under Deceptive Instructions? EMNLP 2025 When Visual State Space Model Meets Backdoor Attacks WACV 2025