NeurIPS 2023 Papers

Conference on Neural Information Processing Systems

2026 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015

Decision breakdown

3,584papers

Counted from the official paper list; may differ slightly from the number the conference announced.

Oral
772.1%
Spotlight
40011.2%
Poster
3,10786.7%

Top 10 topics (click to filter)

Deep learning · generative models and autoencoders776 Deep learning · generative models and autoencoders paper trends, 2023–2026
Deep learning · graph neural networks489 Deep learning · graph neural networks paper trends, 2023–2026
Reinforcement Learning484 Reinforcement Learning paper trends, 2023–2026
Multimodal LLMs433 Multimodal LLMs paper trends, 2023–2026
Deep learning · large language models415 Deep learning · large language models paper trends, 2023–2026
Diffusion Models327 Diffusion Models paper trends, 2023–2026
General ML · unsupervised and semisupervised learning318 General ML · unsupervised and semisupervised learning paper trends, 2023–2026
Reasoning Models271 Reasoning Models paper trends, 2023–2026
Graph Neural Networks251 Graph Neural Networks paper trends, 2023–2026
Datasets and Benchmarks227 Datasets and Benchmarks paper trends, 2023–2026

Trends for all 207 research topics

All3,584 Oral77 Spotlight400 Poster3,107

TitleTypeTopic

RELIC: Reproducibility and Extension on LIC metric in quantifying bias in captioning models

Martijn van Raaphorst, Egoitz Gonzalez, Marta Grasa, Paula Antequera Hernández

Poster

Reasoning Models Deep learning · generative models and autoencoders

[Re] VAE Approximation Error: ELBO and Exponential Families

Volodymyr Kyrylov, Navdeep Singh Bedi, Qianbo Zang

Poster

Alignment and Safety Social aspects · accountability transparency and interpretability

Reproducibility study of the Fairness-enhanced Node Representation Learning

Gijs Moens, Job De Witte, Tobias Gobel, Meggie Van den Oever

Poster

Social aspects · fairness Deep learning · large language models

[Re] CrossWalk: Fairness-enhanced Node Representation Learning

Luca Pantea, Andrei-Eusebiu Blahovici

Poster

Social aspects · fairness Deep learning · large language models

[Re] Masked Autoencoders Are Small Scale Vision Learners: A Reproduction Under Resource Constraints

Athanasios Charisoudis, Simon Ekman von Huth, Emil Jansson

Poster

Deep learning · generative models and autoencoders 3D Vision

[Re] Pure Noise to the Rescue of Insufficient Data

Ryan Lee, Seungmin Lee

Poster

Sim-to-Real Text-to-Speech

Graph Clustering with Graph Neural Networks

Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller

Poster

Deep learning · graph neural networks Graph Neural Networks

Toolbox for Multimodal Learn (scikit-multimodallearn)

Dominique Benielli, Baptiste Bauvin, Sokol Koço, Riikka Huusari, Cécile Capponi, Hachem Kadri, François Laviolette

Poster

Multimodal LLMs Simulation for Driving

MMD Aggregated Two-Sample Test

Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton

Poster

Reasoning Models

Global Optimality and Finite Sample Analysis of Softmax Off-Policy Actor Critic under State Distribution Mismatch

Shangtong Zhang, Remi Tachet des Combes, Romain Laroche

Poster

Mixture of Experts Healthcare Policy Modeling

Concentration analysis of multivariate elliptic diffusions

Lukas Trottner, Cathrine Aeckerle-Willems, Claudia Strauch

Poster

Reasoning Models Mechanistic Interpretability

Sparse Graph Learning from Spatiotemporal Time Series

Andrea Cini, Daniele Zambon, Cesare Alippi

Poster

Reinforcement Learning Time Series

Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds

Didong Li, Wenpin Tang, Sudipto Banerjee

Poster

Deep learning · causal inference Inference Acceleration

Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics

Kamélia Daudel, Joe Benton, Yuyang Shi, Arnaud Doucet

Poster

Social aspects · accountability transparency and interpretability Inference Acceleration

Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data

Vaidotas Simkus, Benjamin Rhodes, Michael Gutmann

Poster

Deep learning · causal inference Inference Acceleration

An Empirical Investigation of the Role of Pre-training in Lifelong Learning

Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, Emma Strubell

Poster

Deep learning · large language models Mixture of Experts

Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data

Bingqing Hu, Bin Nan

Poster

Deep learning · graph neural networks Graph Neural Networks

Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start

Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo

Poster

theory->optimization Deep learning · optimization

Network Regression with Graph Laplacians

Yidong Zhou, Hans-Georg Müller

Poster

Deep learning · graph neural networks Graph Neural Networks

The Separation Capacity of Random Neural Networks

Sjoerd Dirksen, Martin Genzel, Laurent Jacques, Alexander Stollenwerk

Poster

Deep learning · graph neural networks Graph Neural Networks

NeurIPS 2023 Papers

Decision breakdown

Top 10 topics (click to filter)

Browse all papers