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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

38
179
3,018
5,737
'10
'15
'20
'25

Papers (including subtopics)

GOAT: A Global Transformer on Large-scale Graphs ICML 2023 Good Data, Large Data, or No Data? Comparing Three Approaches in Developing Research Aspect Classifiers for Biomedical Papers ACL 2023 Good Helper Is around You: Attention-Driven Masked Image Modeling AAAI 2023 Goodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented Models EMNLP 2023 GPT-4 as an Effective Zero-Shot Evaluator for Scientific Figure Captions EMNLP 2023 GPTR: Gestalt-Perception Transformer for Diagram Object Detection AAAI 2023 GPU-accelerated Guided Source Separation for Meeting Transcription Interspeech 2023 GP-VTON: Towards General Purpose Virtual Try-On via Collaborative Local-Flow Global-Parsing Learning CVPR 2023 GRACE: Discriminator-Guided Chain-of-Thought Reasoning EMNLP 2023 GRACE: Gradient-guided Controllable Retrieval for Augmenting Attribute-based Text Generation ACL 2023 Gradient Ascent Post-training Enhances Language Model Generalization ACL 2023 Gradient-Based Uncertainty Attribution for Explainable Bayesian Deep Learning CVPR 2023 GradOrth: A Simple yet Efficient Out-of-Distribution Detection with Orthogonal Projection of Gradients NeurIPS 2023 GradPU: Positive-Unlabeled Learning via Gradient Penalty and Positive Upweighting AAAI 2023 GradSim: Gradient-Based Language Grouping for Effective Multilingual Training EMNLP 2023 GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets ICML 2023 Grammatical Error Correction via Mixed-Grained Weighted Training EMNLP 2023 Graph Component Contrastive Learning for Concept Relatedness Estimation AAAI 2023 Graph Contrastive Learning with Group Whitening ACML 2023 Graph Contrastive Learning with Stable and Scalable Spectral Encoding NeurIPS 2023 Graphix-T5: Mixing Pre-trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing AAAI 2023 Graph Knows Unknowns: Reformulate Zero-Shot Learning as Sample-Level Graph Recognition AAAI 2023 Graph Mixup with Soft Alignments ICML 2023 Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs ICLR 2023 Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure ICML 2023