1 code implementation • 25 Oct 2023 • Aaron Lou, Chenlin Meng, Stefano Ermon
Experimentally, we test our Score Entropy Discrete Diffusion models (SEDD) on standard language modeling tasks.
1 code implementation • 10 Apr 2023 • Aaron Lou, Stefano Ermon
To incorporate data constraints in a principled manner, we present Reflected Diffusion Models, which instead reverse a reflected stochastic differential equation evolving on the support of the data.
Ranked #1 on Image Generation on CIFAR-10 (Inception score metric)
3 code implementations • NeurIPS 2020 • Aaron Lou, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa
To better conform to data geometry, recent deep generative modelling techniques adapt Euclidean constructions to non-Euclidean spaces.
1 code implementation • 29 Sep 2023 • Linqi Zhou, Aaron Lou, Samar Khanna, Stefano Ermon
However, for many applications such as image editing, the model input comes from a distribution that is not random noise.
2 code implementations • ICML 2020 • Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge Belongie, Ser-Nam Lim, Christopher De Sa
Recent advances in deep representation learning on Riemannian manifolds extend classical deep learning operations to better capture the geometry of the manifold.
2 code implementations • NeurIPS 2021 • Tolga Birdal, Aaron Lou, Leonidas Guibas, Umut Şimşekli
Disobeying the classical wisdom of statistical learning theory, modern deep neural networks generalize well even though they typically contain millions of parameters.
1 code implementation • NeurIPS 2021 • Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa
Tractably modelling distributions over manifolds has long been an important goal in the natural sciences.
no code implementations • 4 Dec 2018 • Horace He, Aaron Lou, Qingxuan Jiang, Isay Katsman, Serge Belongie, Ser-Nam Lim
Research has shown that widely used deep neural networks are vulnerable to carefully crafted adversarial perturbations.
no code implementations • 29 Sep 2021 • Aaron Lou, Maximilian Nickel, Mustafa Mukadam, Brandon Amos
We present Deep Riemannian Manifolds, a new class of neural network parameterized Riemannian manifolds that can represent and learn complex geometric structures.
no code implementations • 21 Nov 2023 • Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, Nikhil Naik
Large language models (LLMs) are fine-tuned using human comparison data with Reinforcement Learning from Human Feedback (RLHF) methods to make them better aligned with users' preferences.
no code implementations • 19 Jan 2024 • Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, Anima Anandkumar
Modeling the complex three-dimensional (3D) dynamics of relational systems is an important problem in the natural sciences, with applications ranging from molecular simulations to particle mechanics.