papers

Publications (7)

cs.CV2023

Learning Probabilistic Models from Generator Latent Spaces with Hat EBM

Mitch Hill, Erik Nijkamp, Jonathan Mitchell +2

This work proposes a method for using any generator network as the foundation of an Energy-Based Model (EBM). Our formulation posits that observed images are the sum of unobserved…

q-bio.PE2016

Distinguishing Convergence on Phylogenetic Networks

Jonathan Mitchell

We compare the phylogenetic tensors for various trees and networks for two, three and four taxa. If the probability spaces between one tree or network and another are not identical…

cs.LG2025

BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery

Peter St. John, Dejun Lin, Polina Binder +89

Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increas…

stat.ML2022

EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density Modeling

Mitch Hill, Jonathan Mitchell, Chu Chen +3

This work presents strategies to learn an Energy-Based Model (EBM) according to the desired length of its MCMC sampling trajectories. MCMC trajectories of different lengths corresp…

cs.CV2018

Bounding Box Embedding for Single Shot Person Instance Segmentation

Jacob Richeimer, Jonathan Mitchell

We present a bottom-up approach for the task of object instance segmentation using a single-shot model. The proposed model employs a fully convolutional network which is trained to…

cs.HC2025

Strategies to manage human factors in mixed reality helicopter pilot training: a systematic literature review

Antonio Perez, Avinash Singh, Jonathan Mitchell +1

Introduction: Mixed reality (MR) head-mounted displays (HMDs) may offer a cost-efficient, immersive alternative to conventional flight simulation displays, but cybersickness, visua…

stat.ML2021

Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models

Mitch Hill, Jonathan Mitchell, Song-Chun Zhu

The vulnerability of deep networks to adversarial attacks is a central problem for deep learning from the perspective of both cognition and security. The current most successful de…