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 @icmlconf , does exactly that!
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!
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!🧵https://t.co/h7elRgivcC
Labour has called on Liz Truss to step down as Prime Minister
Follows three Conservative MPs demanding her resignation yesterday
For more on this and other news visit https://t.co/NEDMP2vmWu
[ICLR 2022] Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective
https://t.co/pkZeWvs3sm
@ScimecaLuca@SanghyukChun@MichaelPoli6@oodgnas
TL;DR Why would DNNs choose colour over shape as the cue for recognition? Answer below.
Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective
Luca Scimeca, @coallaoh, @SanghyukChun, @MichaelPoli6, Sangdoo Yun
tl;dr: provide CNN entangles features, check which it uses for prediction. Color & ethnicity rules.
https://t.co/9UiRVKT2NR
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 @MichaelPoli6@ScimecaLuca@SanghyukChun
https://t.co/aAChhEf4hF (NeurIPS 2021)
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