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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)

Resistance Training Using Prior Bias: Toward Unbiased Scene Graph Generation AAAI 2022 Resource-Adaptive Federated Learning with All-In-One Neural Composition NeurIPS 2022 Respecting Transfer Gap in Knowledge Distillation NeurIPS 2022 Retaining Knowledge for Learning with Dynamic Definition NeurIPS 2022 Rethinking Alignment in Video Super-Resolution Transformers NeurIPS 2022 Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination NeurIPS 2022 Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning CVPR 2022 Rethinking Controllable Variational Autoencoders CVPR 2022 Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation CVPR 2022 Rethinking Document-level Neural Machine Translation ACL 2022 Rethinking Efficient Lane Detection via Curve Modeling CVPR 2022 Rethinking Image Cropping: Exploring Diverse Compositions From Global Views CVPR 2022 Rethinking InfoNCE: How Many Negative Samples Do You Need? IJCAI 2022 Rethinking Minimal Sufficient Representation in Contrastive Learning CVPR 2022 Rethinking Multi-Modal Alignment in Multi-Choice VideoQA from Feature and Sample Perspectives EMNLP 2022 Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem ACL 2022 Rethinking Positional Encoding in Tree Transformer for Code Representation EMNLP 2022 Rethinking Pseudo Labels for Semi-supervised Object Detection AAAI 2022 Rethinking Resolution in the Context of Efficient Video Recognition NeurIPS 2022 Rethinking Self-Supervision Objectives for Generalizable Coherence Modeling ACL 2022 Rethinking Style Transformer with Energy-based Interpretation: Adversarial Unsupervised Style Transfer using a Pretrained Model EMNLP 2022 Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance With Expanded Views CVPR 2022 Rethinking the compositionality of point clouds through regularization in the hyperbolic space NeurIPS 2022 Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification IJCAI 2022 Rethinking the Reverse-engineering of Trojan Triggers NeurIPS 2022