Neural estimation of dynamical systems = data-driven parameter estimation + strong maths modelling This is a fascinating topic, isn't it? I'm glad to contribute to this topic with great talents Michael Poli Luca Scimeca SanghyukChun arxiv.org/abs/2106.04165 (NeurIPS 2021) 1/

Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective Luca Scimeca, Seong Joon Oh, SanghyukChun, Michael Poli, Sangdoo Yun tl;dr: provide CNN entangles features, check which it uses for prediction. Color & ethnicity rules. arxiv.org/abs/2110.03095…

[ICLR 2022] Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective arxiv.org/abs/2110.03095 Luca Scimeca SanghyukChun Michael Poli Sangdoo Yun TL;DR Why would DNNs choose colour over shape as the cue for recognition? Answer below.

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Can we perform unbiased bayesian posterior inference with a diffusion model prior? We propose Relative Trajectory Balance (RTB) which allows us to directly optimize for this posterior model. We apply this to several tasks in image, language and control!🧵arxiv.org/abs/2405.20971

Resharing this thread now that our paper has been accepted to #NeurIPS! We propose a new (off-policy) objective "Relative Trajectory Balance" to train a diffusion prior with a reward function to obtain the Bayesian posterior. We can do KL regularized RLHF similar to LLMs now!

Come check out our neurips poster today! We will be at West Ballroom #7101 from 4:30pm - 7:30pm. Website: github.com/gfnorg/diffusi…

Is there a universal strategy to turn any generative model—GANs, VAEs, diffusion models, or flows—into a conditional sampler, or finetuned to optimize a reward function? Yes! Outsourced Diffusion Sampling (ODS) accepted to ICML Conference , does exactly that!

Come check out our poster this Wednesday at 4:30pm ICML Conference !! Happy to chat about diffusion, GFlowNets and RL!