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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)

Howard University-AI4PC at SemEval-2025 Task 9: Using Open-weight BART-MNLI for Zero Shot Classification of Food Recall Documents SemEval 2025 How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework ICLR 2025 How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Code EMNLP 2025 How Does DPO Reduce Toxicity? A Mechanistic Neuron-Level Analysis EMNLP 2025 How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning? AAAI 2025 How Do Position Encodings Affect Length Generalization? Case Studies On In-Context Function Learning AAAI 2025 How Far Can LLMs Improve from Experience? Measuring Test-Time Learning Ability in LLMs with Human Comparison EMNLP 2025 How Much is a Noisy Image Worth? Data Scaling Laws for Ambient Diffusion. ICLR 2025 How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM? NAACL 2025 How Not to Stitch Representations to Measure Similarity: Task Loss Matching Versus Direct Matching AAAI 2025 How Sampling Affects the Detectability of Machine-written texts: A Comprehensive Study EMNLP 2025 How to Compare Things Properly? A Study of Argument Relevance in Comparative Question Answering ACL 2025 How to Fine-Tune Safely on a Budget: Model Adaptation Using Minimal Resources EMNLP 2025 How to inject knowledge efficiently? Knowledge Infusion Scaling Law for Pre-training Large Language Models EMNLP 2025 How to Mitigate Overfitting in Weak-to-strong Generalization? ACL 2025 How to Recover Long Audio Sequences Through Gradient Inversion Attack With Dynamic Segment-based Reconstruction Interspeech 2025 How to select slices for annotation to train best-performing deep learning segmentation models for cross-sectional medical images? MIDL 2025 How to Train Long-Context Language Models (Effectively) ACL 2025 How Well Can Transformers Emulate In-Context Newton’s Method? AISTATS 2025 HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models ICCV 2025 HSCR: Hierarchical Self-Contrastive Rewarding for Aligning Medical Vision Language Models ACL 2025 HSRDiff: A Hierarchical Self-Regulation Diffusion Model for Stochastic Semantic Segmentation AAAI 2025 HUANG: A Robust Diffusion Model-based Targeted Adversarial Attack Against Deep Hashing Retrieval AAAI 2025 HU at SemEval-2025 Task 9: Leveraging LLM-Based Data Augmentation for Class Imbalance SemEval 2025 HuBERT-VIC: Improving Noise-Robust Automatic Speech Recognition of Speech Foundation Model via Variance-Invariance-Covariance Regularization Interspeech 2025