conftrace_
← Core Methods
Machine Learning › Core Methods ›

Representation Learning

18,863 papers

Papers per year

Papers

Learning Infinitesimal Generators of Continuous Symmetries from Data NIPS 2024
StreamingDialogue: Prolonged Dialogue Learning via Long Context Compression with Minimal Losses NIPS 2024
On Feature Learning in Structured State Space Models NIPS 2024
Neural Model Checking NIPS 2024
SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead NIPS 2024
Neur2BiLO: Neural Bilevel Optimization NIPS 2024
Transcendence: Generative Models Can Outperform The Experts That Train Them NIPS 2024
Adaptive $Q$-Aid for Conditional Supervised Learning in Offline Reinforcement Learning NIPS 2024
You Only Look Around: Learning Illumination-Invariant Feature for Low-light Object Detection NIPS 2024
Alleviate Anchor-Shift: Explore Blind Spots with Cross-View Reconstruction for Incomplete Multi-View Clustering NIPS 2024
Instruction Embedding: Latent Representations of Instructions Towards Task Identification NIPS 2024
Posture-Informed Muscular Force Learning for Robust Hand Pressure Estimation NIPS 2024
DeformableTST: Transformer for Time Series Forecasting without Over-reliance on Patching NIPS 2024
bit2bit: 1-bit quanta video reconstruction via self-supervised photon prediction NIPS 2024
ProtGO: Function-Guided Protein Modeling for Unified Representation Learning NIPS 2024
Provable and Efficient Dataset Distillation for Kernel Ridge Regression NIPS 2024
Once Read is Enough: Domain-specific Pretraining-free Language Models with Cluster-guided Sparse Experts for Long-tail Domain Knowledge NIPS 2024
Molecule Design by Latent Prompt Transformer NIPS 2024
Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers NIPS 2024
ActFusion: a Unified Diffusion Model for Action Segmentation and Anticipation NIPS 2024
Aligner: Efficient Alignment by Learning to Correct NIPS 2024
A Generative Model of Symmetry Transformations NIPS 2024
Learning Structured Representations with Hyperbolic Embeddings NIPS 2024
How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks NIPS 2024
DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs NIPS 2024