How to prevent behavior cloning policies from drifting OOD on long horizon manipulation tasks?
Check out Latent Policy Barrier (LPB), a plug-and-play test-time optimization method that keeps BC policies in-distribution with no extra demo or fine-tuning: https://t.co/SM21Yd9dtp
Flow-matching implementation:
https://t.co/sP5DXLr4jI
Flow-matching is very similar to diffusion, but simplifies things. Noised images are linear interpolations between (data, noise) pairs, and the network predicts *velocity* of this trajectory.
@KL_Div@PengShichong Is there a name for the unconditional case?
- each real point has a vote
- generated points (candidates) go close to real points to win votes
x_fake = G(z), so generation is unconditional. Smells similar to generative topographic map.
Excited to share our latest work, which generates high-res images non-adversarially without the use of a discriminator. Benefits include: (1) no mode collapse and (2) no training instability.
Joint work with @PengShichong
Paper and Code: https://t.co/Np78VTDuRP
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Sad news. Horace Barlow, one of the great thinkers about the brain, has passed away. Born 8 Dec 1921, he saw almost a full century, and taught us about vision, perceptual inference, neural coding, learning, and more. #neuroscience#vision#NeuralNetworks
@seanbax It worries me that these custom tools, each hijacking and taking a piece from the AST, will not be composable. Or maybe they will be, but composing them requires so much expertise it will not be a pleasant experience.
Our work "Flow Contrastive Estimation of Energy-Based Models" (@RuiqiGao, @erik_nijkamp, @dpkingma, Z. Xu @andrewdai, YN Wu) at #NeurIPS2019:
https://t.co/KIsc7F1nJd
We show (1) joint learning of EBM & Glow, (2) correction of over-dispersion, (3) competitive semi-super cls.
I've always been fascinated by Bayesian Nonparametrics. I struggled to grasp those ideas directly from papers. Today, by chance, I found the best (imo) single reference for anyone interested: A gentle introduction by @yeewhye and Michael Jordan
https://t.co/tDlRHc10Md