Learning Disentangled Joint Continuous and Discrete Representations. - Emilien Dupont (NeurIPS, 2018)

Joint-VAE: Learning Disentangled Joint Continuous and Discrete Representations. - Emilien Dupont (CoRR, 2018)

Probabilistic Semantic Inpainting with Pixel Constrained CNNs. - Emilien Dupont; Suhas Suresha (AISTATS, 2019)

Augmented Neural ODEs. - Emilien Dupont; Arnaud Doucet; Yee Whye Teh (NeurIPS, 2019)

Equivariant Neural Rendering. - Emilien Dupont; Miguel Bautista Martin; Alex Colburn; Aditya Sankar; Josh M. Susskind; Qi Shan (ICML, 2020)

STEER : Simple Temporal Regularization For Neural ODE. - Arnab Ghosh; Harkirat S. Behl; Emilien Dupont; Philip H. S. Torr; Vinay P. Namboodiri (NeurIPS, 2020)

Equivariant Neural Rendering. - Emilien Dupont; Miguel Ángel Bautista 001; Alex Colburn; Aditya Sankar; Carlos Guestrin; Josh M. Susskind; Qi Shan (CoRR, 2020)

STEER : Simple Temporal Regularization For Neural ODEs. - Arnab Ghosh; Harkirat Singh Behl; Emilien Dupont; Philip H. S. Torr; Vinay P. Namboodiri (CoRR, 2020)

LieTransformer: Equivariant Self-Attention for Lie Groups. - Michael J. Hutchinson; Charline Le Lan; Sheheryar Zaidi; Emilien Dupont; Yee Whye Teh; Hyunjik Kim (ICML, 2021)

COIN: COmpression with Implicit Neural representations. - Emilien Dupont; Adam Golinski; Milad Alizadeh; Yee Whye Teh; Arnaud Doucet (CoRR, 2021)

Generative Models as Distributions of Functions. - Emilien Dupont; Yee Whye Teh; Arnaud Doucet (AISTATS, 2022)

From data to functa: Your data point is a function and you can treat it like one. - Emilien Dupont; Hyunjik Kim; S. M. Ali Eslami; Danilo Jimenez Rezende; Dan Rosenbaum (ICML, 2022)

From data to functa: Your data point is a function and you should treat it like one. - Emilien Dupont; Hyunjik Kim; S. M. Ali Eslami; Danilo J. Rezende; Dan Rosenbaum (CoRR, 2022)

COIN++: Data Agnostic Neural Compression. - Emilien Dupont; Hrushikesh Loya; Milad Alizadeh; Adam Golinski; Yee Whye Teh; Arnaud Doucet (CoRR, 2022)

COIN++: Neural Compression Across Modalities. - Emilien Dupont; Hrushikesh Loya; Milad Alizadeh; Adam Golinski; Yee Whye Teh; Arnaud Doucet (Trans. Mach. Learn. Res., 2022)

Deep Stochastic Processes via Functional Markov Transition Operators. - Jin Xu 011; Emilien Dupont; Kaspar Märtens; Thomas Rainforth; Yee Whye Teh (NeurIPS, 2023)

Spatial Functa: Scaling Functa to ImageNet Classification and Generation. - Matthias Bauer 001; Emilien Dupont; Andy Brock; Dan Rosenbaum; Jonathan Schwarz; Hyunjik Kim (CoRR, 2023)

C3: High-Performance and Low-Complexity Neural Compression from a Single Image or Video. - Hyunjik Kim; Matthias Bauer 001; Lucas Theis; Jonathan Richard Schwarz; Emilien Dupont (CVPR, 2024)

Mathematical discoveries from program search with large language models. - Bernardino Romera-Paredes; Mohammadamin Barekatain; Alexander Novikov 001; Matej Balog; M. Pawan Kumar; Emilien Dupont; Francisco J. R. Ruiz; Jordan S. Ellenberg; Pengming Wang 001; Omar Fawzi; Pushmeet Kohli; Alhussein Fawzi (Nat., 2024)

Good, Cheap, and Fast: Overfitted Image Compression with Wasserstein Distortion. - Jona Ballé; Luca Versari; Emilien Dupont; Hyunjik Kim; Matthias Bauer (CVPR, 2025)

AlphaEvolve: A coding agent for scientific and algorithmic discovery. - Alexander Novikov 001; Ngân Vu; Marvin Eisenberger; Emilien Dupont; Po-Sen Huang; Adam Zsolt Wagner; Sergey Shirobokov; Borislav Kozlovskii; Francisco J. R. Ruiz; Abbas Mehrabian; M. Pawan Kumar; Abigail See; et al. (CoRR, 2025)