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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)

Towards Artwork Explanation in Large-scale Vision Language Models ACL 2024 Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics NeurIPS 2024 Towards a theory of how the structure of language is acquired by deep neural networks NeurIPS 2024 Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution NeurIPS 2024 Towards Backward-Compatible Continual Learning of Image Compression CVPR 2024 Towards Better Understanding of Contrastive Sentence Representation Learning: A Unified Paradigm for Gradient ACL 2024 Towards Calibrated Multi-label Deep Neural Networks CVPR 2024 Towards CLIP-driven Language-free 3D Visual Grounding via 2D-3D Relational Enhancement and Consistency CVPR 2024 Towards Combating Frequency Simplicity-biased Learning for Domain Generalization NeurIPS 2024 Towards Continual Knowledge Graph Embedding via Incremental Distillation AAAI 2024 Towards Croppable Implicit Neural Representations NeurIPS 2024 Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language Models EMNLP 2024 Towards Diverse Behaviors: A Benchmark for Imitation Learning with Human Demonstrations ICLR 2024 Towards Diverse Perspective Learning with Selection over Multiple Temporal Poolings AAAI 2024 Towards Efficient and Effective Text-to-Video Retrieval with Coarse-to-Fine Visual Representation Learning AAAI 2024 Towards efficient self-supervised representation learning in speech processing EACL 2024 Towards Efficient Visual-Language Alignment of the Q-Former for Visual Reasoning Tasks EMNLP 2024 Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing NeurIPS 2024 Towards Exact Gradient-based Training on Analog In-memory Computing NeurIPS 2024 Towards Explainable Joint Models via Information Theory for Multiple Intent Detection and Slot Filling AAAI 2024 Towards Fairer Centroids in K-means Clustering AAAI 2024 Towards Fairness-Aware Adversarial Learning CVPR 2024 Towards Generalization beyond Pointwise Learning: A Unified Information-theoretic Perspective ICML 2024 Towards Generalizing to Unseen Domains with Few Labels CVPR 2024 Towards Global Optimal Visual In-Context Learning Prompt Selection NeurIPS 2024