1 citations · 1 across the 8 of their papers we have counts for
9 papers
Langevin Flow Maps: Efficient Molecular Dynamics and Transition Path Sampling
Sam McCallum, Niklas Rindtorff, Alexander Tong +1
Molecular dynamics simulations proceed by integrating the Langevin equations over many small femtosecond timesteps. This poses a challenge for estimating ensemble properties and tr…
Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs New Metrics
Antonio Franca, Alexander Tong
Diffusion language models (DLMs) have emerged as the leading non-autoregressive alternative to language modeling. Because their likelihoods are intractable, generative perplexity (…
Autoregressive Boltzmann Generators
Danyal Rehman, Charlie B. Tan, Yoshua Bengio +2
Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics. This challenge has driven the development of Boltzmann Generato…
Why Are DMD Students Lazy? Understanding the Copying Behavior in Few-Step Distillation
Shucheng Li, Iolo Jones, Alexander Tong +1
Distribution Matching Distillation (DMD) compresses pretrained diffusion models into efficient few-step generators by aligning their noised distributions across all scales. In prin…
Strong Stochastic Flow Maps
Sam McCallum, Zander W. Blasingame, Timothy Herschell +3
Flow and diffusion models generate high-quality samples in many modalities; however, many network evaluations are required during inference due to numerical integration of an under…
Coupling Models for One-Step Discrete Generation
Fred Zhangzhi Peng, Avishek Joey Bose, Anru R. Zhang +1
Generative modeling over discrete structures underpins applications across deep learning, from biological sequence design and code generation to large language models, yet generati…
Don't Retrain, Align: Adapting Autoregressive LMs to Diffusion LMs via Representation Alignment
Fred Zhangzhi Peng, Alexis Fox, Anru R. Zhang +1
Diffusion language models (DLMs) have recently demonstrated capabilities that complement standard autoregressive (AR) models, particularly in non-sequential generation and bidirect…
Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction
Vincent Pauline, Tobias Höppe, Kirill Neklyudov +3
Although diffusion models now occupy a central place in generative modeling, introductory treatments commonly assume Euclidean data and seldom clarify their connection to discrete-…
Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen
Alessandro Palma, Till Richter, Hanyi Zhang +4
Generative modeling of single-cell RNA-seq data is crucial for tasks like trajectory inference, batch effect removal, and simulation of realistic cellular data. However, recent dee…