Deep learning · representation learning papers, 2023–2026

Key findings

• Deep learning · representation learning fell from 259 papers in 2023 to 214 in 2026, so interest is cooling.
• NeurIPS publishes the most Deep learning · representation learning work: 279 papers from 2023 to 2026, 35% of the four-venue total.
• The fastest-growing overlap is with Multimodal LLMs: papers tagged with both rose from 11 in 2023 to 28 in 2026.
• Among the 11 topics under Deep Learning, it ranks #7 by 2026 paper count; the fastest grower in the group, Diffusion Models, is up 4.1x versus 0.8x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026Active Learning as Nullspace Regulation: A Spectral Representation Perspective
• ICML 2026Addressing Instrument-Outcome Confounding in Mendelian Randomization through Representation Learning
• ICLR 2026Boosting Open Set Recognition Performance through Modulated Representation Learning
• CVPR 2026CanonCGT Reference-Based Color Grading via Canonical Pivot Representation
• NeurIPS 2025Causality-Induced Positional Encoding for Transformer-Based Representation Learning of Non-Sequential Features
• ICML 2025Banyan: Improved Representation Learning with Explicit Structure
• ICLR 2025Edge-aware Image Smoothing with Relative Wavelet Domain Representation
• CVPR 20253D-SLNR: A Super Lightweight Neural Representation for Large-scale 3D Mapping
• NeurIPS 2024GV-Rep: A Large-Scale Dataset for Genetic Variant Representation Learning
• ICML 2024Data-free Neural Representation Compression with Riemannian Neural Dynamics
• ICLR 2024$\texttt{NAISR}$: A 3D Neural Additive Model for Interpretable Shape Representation
• CVPR 2024Disentangled Prompt Representation for Domain Generalization
• NeurIPS 2023[Re] CrossWalk: Fairness-enhanced Node Representation Learning
• ICML 2023A theory of representation learning gives a deep generalisation of kernel methods
• ICLR 2023Bispectral Neural Networks
• CVPR 2023AnyFlow: Arbitrary Scale Optical Flow With Implicit Neural Representation
• NeurIPS 2026Approximation in Contrastive Representation Learning
• ICML 2026Amortized Maximum Inner Product Search with Learned Support Functions
• ICLR 2026Decoupling Dynamical Richness from Representation Learning: Towards Practical Measurement
• CVPR 2026Enhancing Visual Representation with Textual Semantics Textual Semantics-Powered Prototypes for

All Deep learning · representation learning papers

152 papers, page 2 of 4

51. ICLR 2025Population Transformer: Learning Population-level Representations of Neural Activity
52. ICLR 2025Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks
53. ICLR 2025Range, not Independence, Drives Modularity in Biologically Inspired Representations
54. ICLR 2025REEF: Representation Encoding Fingerprints for Large Language Models
55. ICLR 2025Rethinking Fair Representation Learning for Performance-Sensitive Tasks
56. ICLR 2025Robust Representation Consistency Model via Contrastive Denoising
57. ICLR 2025Robustness Reprogramming for Representation Learning
58. ICLR 2025Semantic Aware Representation Learning for Lifelong Learning
59. ICLR 2025Sensor-Invariant Tactile Representation
60. ICLR 2025SPA: 3D Spatial-Awareness Enables Effective Embodied Representation
61. ICLR 2025Towards a learning theory of representation alignment
62. ICLR 2025URLOST: Unsupervised Representation Learning without Stationarity or Topology
63. ICLR 2025Wavelet-based Positional Representation for Long Context
64. ICML 2025A Closer Look at Backdoor Attacks on CLIP
65. ICML 2025A Closer Look at Multimodal Representation Collapse
66. ICML 2025Approximating Latent Manifolds in Neural Networks via Vanishing Ideals
67. ICML 2025Attention-Only Transformers via Unrolled Subspace Denoising
68. ICML 2025Banyan: Improved Representation Learning with Explicit Structure
69. ICML 2025Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation
70. ICML 2025Cover learning for large-scale topology representation
71. ICML 2025Functional Alignment Can Mislead: Examining Model Stitching
72. ICML 2025Generalization Analysis for Supervised Contrastive Representation Learning under Non-IID Settings
73. ICML 2025Geometric Algebra Planes: Convex Implicit Neural Volumes
74. ICML 2025GPEN: Global Position Encoding Network for Enhanced Subgraph Representation Learning
75. ICML 2025Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning
76. ICML 2025Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
77. ICML 2025Learning Representations of Instruments for Partial Identification of Treatment Effects
78. ICML 2025Measuring Representational Shifts in Continual Learning: A Linear Transformation Perspective
79. ICML 2025NEAR: Neural Electromagnetic Array Response
80. ICML 2025On the Importance of Embedding Norms in Self-Supervised Learning
81. ICML 2025On the Local Complexity of Linear Regions in Deep ReLU Networks
82. ICML 2025On the Similarities of Embeddings in Contrastive Learning
83. ICML 2025Online Laplacian-Based Representation Learning in Reinforcement Learning
84. ICML 2025Point-Level Topological Representation Learning on Point Clouds
85. ICML 2025PRIME: Deep Imbalanced Regression with Proxies
86. ICML 2025Q-Supervised Contrastive Representation: A State Decoupling Framework for Safe Offline Reinforcement Learning
87. ICML 2025Random Feature Representation Boosting
88. ICML 2025Reconstructing Cell Lineage Trees from Phenotypic Features with Metric Learning
89. ICML 2025Representation Preserving Multiclass Agnostic to Realizable Reduction
90. ICML 2025sciLaMA: A Single-Cell Representation Learning Framework to Leverage Prior Knowledge from Large Language Models
91. ICML 2025Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows
92. ICML 2025Sliding Puzzles Gym: A Scalable Benchmark for State Representation in Visual Reinforcement Learning
93. ICML 2025Stabilizing Sample Similarity in Representation via Mitigating Random Consistency
94. ICML 2025Stable Fair Graph Representation Learning with Lipschitz Constraint
95. ICML 2025Structure Is All You Need: Structural Representation Learning on Hyper-Relational Knowledge Graphs
96. ICML 2025The Complexity of Learning Sparse Superposed Features with Feedback
97. ICML 2025The Generalized Skew Spectrum of Graphs
98. ICML 2025When do neural networks learn world models?
99. NeurIPS 2025$\boldsymbol{\lambda}$-Orthogonality Regularization for Compatible Representation Learning
100. NeurIPS 2025Adv-SSL: Adversarial Self-Supervised Representation Learning with Theoretical Guarantees

FAQ

How many Deep learning · representation learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

802 in total: 259 in 2023, 177 in 2024, 152 in 2025, 214 in 2026.

How many Deep learning · representation learning papers are at NeurIPS 2026?

NeurIPS 2026 has 59 Deep learning · representation learning papers, versus 54 the year before.

How many Deep learning · representation learning papers are at ICLR 2026?

ICLR 2026 has 39 Deep learning · representation learning papers, versus 37 the year before.

How many Deep learning · representation learning papers are at ICML 2026?

ICML 2026 has 74 Deep learning · representation learning papers, versus 35 the year before.

How many Deep learning · representation learning papers are at CVPR 2026?

CVPR 2026 has 42 Deep learning · representation learning papers, versus 26 the year before.

Which conference publishes the most Deep learning · representation learning papers?

NeurIPS, with 279 papers from 2023 to 2026.