96 citations · 111 across the 9 of their papers we have counts for
7 papers · 1 filter
Exact Global MCMC with Denoising Diffusion
Mitch Hill
This work shows that diffusion models learned with standard denoising loss can provide effective global MCMC proposals for complex high-dimensional target densities. The method is…
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…
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…
Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model
Erik Nijkamp, Mitch Hill, Song-Chun Zhu +1
This paper studies a curious phenomenon in learning energy-based model (EBM) using MCMC. In each learning iteration, we generate synthesized examples by running a non-convergent, n…
On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models
Erik Nijkamp, Mitch Hill, Tian Han +2
This study investigates the effects of Markov chain Monte Carlo (MCMC) sampling in unsupervised Maximum Likelihood (ML) learning. Our attention is restricted to the family of unnor…
Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model
Tian Han, Erik Nijkamp, Xiaolin Fang +3
This paper proposes the divergence triangle as a framework for joint training of generator model, energy-based model and inference model. The divergence triangle is a compact and s…
Building a Telescope to Look Into High-Dimensional Image Spaces
Mitch Hill, Erik Nijkamp, Song-Chun Zhu
An image pattern can be represented by a probability distribution whose density is concentrated on different low-dimensional subspaces in the high-dimensional image space. Such pro…