conftrace_

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

1

22

25

29

26

38

46

60

109

129

179

310

686

1,294

2,286

3,018

3,254

3,930

4,067

4,819

5,737

2,605

'10

'15

'20

'25

Papers (including subtopics)

Feature Unlearning for Pre-trained GANs and VAEs AAAI 2024 FeatWalk: Enhancing Few-Shot Classification through Local View Leveraging AAAI 2024 FedAS: Bridging Inconsistency in Personalized Federated Learning CVPR 2024 FedCD: Federated Semi-Supervised Learning with Class Awareness Balance via Dual Teachers AAAI 2024 FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning AAAI 2024 FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels AAAI 2024 Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning AAAI 2024 Federated Continual Learning via Prompt-based Dual Knowledge Transfer ICML 2024 Federated Generalized Category Discovery CVPR 2024 Federated Graph Learning for Cross-Domain Recommendation NeurIPS 2024 Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method NeurIPS 2024 Federated Learning with Extremely Noisy Clients via Negative Distillation AAAI 2024 Federated Model Heterogeneous Matryoshka Representation Learning NeurIPS 2024 Federated Representation Learning in the Under-Parameterized Regime ICML 2024 FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning NeurIPS 2024 FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models EMNLP 2024 FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learning ACML 2024 FedLTF: Linear Probing Teaches Fine-tuning to Mitigate Noisy Labels in Federated Learning ACML 2024 FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data ICML 2024 FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning CVPR 2024 FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning AAAI 2024 FEEL-SNN: Robust Spiking Neural Networks with Frequency Encoding and Evolutionary Leak Factor NeurIPS 2024 FELGA: Unsupervised Fragment Embedding for Fine-Grained Cross-Modal Association WACV 2024 Feriji: A French-Zarma Parallel Corpus, Glossary & Translator ACL 2024 Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity NeurIPS 2024