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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)

OneLLM: One Framework to Align All Modalities with Language CVPR 2024 One-Prompt to Segment All Medical Images CVPR 2024 One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models CVPR 2024 One Self-Configurable Model to Solve Many Abstract Visual Reasoning Problems AAAI 2024 One-Shot Machine Unlearning with Mnemonic Code ACML 2024 One Step Closer to Unbiased Aleatoric Uncertainty Estimation AAAI 2024 One-Step Effective Diffusion Network for Real-World Image Super-Resolution NeurIPS 2024 One-to-many testing for code generation from (just) natural language EMNLP 2024 One-to-Multiple: A Progressive Style Transfer Unsupervised Domain-Adaptive Framework for Kidney Tumor Segmentation NeurIPS 2024 One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection NeurIPS 2024 On Feature Learning in Structured State Space Models NeurIPS 2024 On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion NeurIPS 2024 On Improving Repository-Level Code QA for Large Language Models ACL 2024 On Learning Discriminative Features from Synthesized Data for Self-Supervised Fine-Grained Visual Recognition ECCV 2024 On Learning Frequency-Instance Correlations by Model-Agnostic Training for Synthetic Speech Detection ACML 2024 Online Continuous Generalized Category Discovery ECCV 2024 OnlineTAS: An Online Baseline for Temporal Action Segmentation NeurIPS 2024 On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability NeurIPS 2024 On Scaling Up a Multilingual Vision and Language Model CVPR 2024 On Stronger Computational Separations Between Multimodal and Unimodal Machine Learning ICML 2024 On task-relevant loss functions in meta-reinforcement learning L4DC 2024 On the Comparison between Multi-modal and Single-modal Contrastive Learning NeurIPS 2024 On the Concept Trustworthiness in Concept Bottleneck Models AAAI 2024 On the connection between Noise-Contrastive Estimation and Contrastive Divergence AISTATS 2024 On the Convergence of an Adaptive Momentum Method for Adversarial Attacks AAAI 2024