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← Core Methods
Machine Learning
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Core Methods
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Representation Learning
18,863 papers
Papers per year
2000: 2
2001: 4
2002: 2
2003: 18
2004: 4
2005: 9
2006: 62
2007: 61
2008: 74
2009: 73
2010: 96
2011: 103
2012: 131
2013: 283
2014: 255
2015: 295
2016: 426
2017: 691
2018: 1066
2019: 1798
2020: 1830
2021: 2207
2022: 2109
2023: 2560
2024: 2177
2025: 1926
2026: 601
Papers
Latent Intrinsics Emerge from Training to Relight
NIPS 2024
Approximately Equivariant Neural Processes
NIPS 2024
A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences
NIPS 2024
Structure Consistent Gaussian Splatting with Matching Prior for Few-shot Novel View Synthesis
NIPS 2024
Few-Shot Task Learning through Inverse Generative Modeling
NIPS 2024
On the Efficiency of ERM in Feature Learning
NIPS 2024
Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts
NIPS 2024
PaCE: Parsimonious Concept Engineering for Large Language Models
NIPS 2024
Scalable Kernel Inverse Optimization
NIPS 2024
Efficiently Learning Significant Fourier Feature Pairs for Statistical Independence Testing
NIPS 2024
A theoretical design of concept sets: improving the predictability of concept bottleneck models
NIPS 2024
Global Distortions from Local Rewards: Neural Coding Strategies in Path-Integrating Neural Systems
NIPS 2024
Geometry of naturalistic object representations in recurrent neural network models of working memory
NIPS 2024
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening
NIPS 2024
From Causal to Concept-Based Representation Learning
NIPS 2024
Foundations of Multivariate Distributional Reinforcement Learning
NIPS 2024
Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component Analysis
NIPS 2024
Multi-Scale Representation Learning for Protein Fitness Prediction
NIPS 2024
Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis
NIPS 2024
Generate Universal Adversarial Perturbations for Few-Shot Learning
NIPS 2024
Identifiability Guarantees for Causal Disentanglement from Purely Observational Data
NIPS 2024
Graph-based Unsupervised Disentangled Representation Learning via Multimodal Large Language Models
NIPS 2024
Simplified and Generalized Masked Diffusion for Discrete Data
NIPS 2024
From Dictionary to Tensor: A Scalable Multi-View Subspace Clustering Framework with Triple Information Enhancement
NIPS 2024
Bootstrapping Top-down Information for Self-modulating Slot Attention
NIPS 2024
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