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Luke Metz proslijedio/la je Tweet
1/5 In one my
@GoogleAI Residency projects we used CNNs to reparameterize structural optimization (w/@shoyer@jaschasd). Our approach worked best on 99/116 structures. I just finished a blog post with GIFs, visualizations, and links to code + Colab. https://greydanus.github.io/2019/12/15/neural-reparam/ …pic.twitter.com/3zwSJyxxtfPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Luke Metz proslijedio/la je Tweet
FixMatch: focusing on simplicity for semi-supervised learning and improving state of the art (CIFAR 94.9% with 250 labels, 88.6% with 40). https://arxiv.org/abs/2001.07685 Collaboration with Kihyuk Sohn,
@chunliang_tw@ZizhaoZhang Nicholas Carlini@ekindogus@Han_Zhang_@colinraffelpic.twitter.com/BmeYvpEHzX
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Luke Metz proslijedio/la je Tweet
I feel strange writing this at this time but I should tweet about papers at some point. This one is with the amazing @jo_historian--my first paper with a historian! I learned so much from her. People always talk about using ML for X, digital humanities etc https://arxiv.org/abs/1912.10389
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Luke Metz proslijedio/la je Tweet
MetaInit: Initializing learning by learning to initialize They propose a strategy to automatically identify good initial parameters, and show that deep architectures *without* batch norm or residual connections can be trained to get near SOTA results.
https://papers.nips.cc/paper/9427-metainit-initializing-learning-by-learning-to-initialize.pdf …pic.twitter.com/RnYc7YghDR
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Luke Metz proslijedio/la je Tweet
We're releasing "Dota 2 with Large Scale Deep Reinforcement Learning", a scientific paper analyzing our findings from our 3-year Dota project: https://openai.com/projects/five/ One highlight — we trained a new agent, Rerun, which has a 98% win rate vs the version that beat
@OGEsports.pic.twitter.com/1kWvXwBHHpHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Luke Metz proslijedio/la je Tweet
#neurips tips day 5 (h/t@chris_j_beckham)! Conferences are a parade of successes. Remember that for every impressive paper there are many (unpublished) ideas that didn't pan out. Take this opportunity to ask people about negative results!Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Luke Metz proslijedio/la je Tweet
Reminder: I’m going to be presenting this today! Come see the non-hexagon version of me, and ask me about inductive biases! (5:30-7:30 pm east exhibit hall b+c #188)https://twitter.com/hardmaru/status/1189340281046474752 …
0:33Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Luke Metz proslijedio/la je Tweet
Meta-learning has a peculiar, widespread problem that leads to terrible performance when faced with seemingly benign changes to the training set-up. We analyze this problem & provide a solution: https://arxiv.org/abs/1912.03820 w/
@Mingzhangyin,@georgejtucker, Zhou,@svlevinepic.twitter.com/yIIy9fEzqv
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Luke Metz proslijedio/la je Tweet
As promised, we have made the Text-To-Text Transfer Transformer (T5) models much easier to fine-tune for new tasks, and we just released a Colab notebook where you can try it yourself on a free TPU!
http://tiny.cc/t5-colab
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Luke Metz proslijedio/la je Tweet
Anything that can be implemented in JAX *will* be implemented in JAX. Here's a differentiable path tracer (and a tutorial!) Blog Post: https://blog.evjang.com/2019/11/jaxpt.html … Code: https://github.com/ericjang/pt-jax pic.twitter.com/KBHnCqka2e
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Luke Metz proslijedio/la je Tweet
Check out our study on the effects of inductive bias and model capacity in video prediction models. This is work with
@arkanathpathak@harinidkannan@doomie@quocleix@honglaklee Paper: https://arxiv.org/abs/1911.01655 Website: https://sites.google.com/view/videopredictioncapacity …pic.twitter.com/vzbseeYMr9Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Luke Metz proslijedio/la je Tweet
Meta Reinforcement Learning is good at adaptation to very similar environments. But can we meta-learn general RL algorithms? Our new approach MetaGenRL is able to. With
@vansteenkiste_s and@SchmidhuberAI Paper: https://arxiv.org/abs/1910.04098 Blog:http://louiskirsch.com/metagenrlHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Luke Metz proslijedio/la je Tweet
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction Rather than hardcoding forward prediction, we try to get agents to *learn* that they need to predict the future. Check out our
#NeurIPS2019 paper! https://learningtopredict.github.io https://arxiv.org/abs/1910.13038 pic.twitter.com/XMjNyeRBK2Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Luke Metz proslijedio/la je Tweet
Stoked to share a milestone project for all of us!
#NeurIPS2019 paper with@akshaykagrawal,@ShaneBarratt, S. Boyd, S. Diamond,@zicokolter: Differentiable Convex Optimization Layers Paper: http://web.stanford.edu/~boyd/papers/pdf/diff_cvxpy.pdf … Blog Post: https://locuslab.github.io/2019-10-28-cvxpylayers/ … Repo: https://github.com/cvxgrp/cvxpylayers …https://twitter.com/akshaykagrawal/status/1188845518962585600 …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Luke Metz proslijedio/la je Tweet
The Meta-World paper is now out! Includes an eval of 8 methods & 5 eval modes. https://arxiv.org/abs/1910.10897 We look forward to seeing how your new algorithms fare on the suite of 50 tasks. https://twitter.com/chelseabfinn/status/1171858992886206464 …pic.twitter.com/9bU9PdaouM
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Luke Metz proslijedio/la je Tweet
New paper! We perform a systematic study of transfer learning for NLP using a unified text-to-text model, then push the limits to achieve SoTA on GLUE, SuperGLUE, CNN/DM, and SQuAD. Paper: https://arxiv.org/abs/1910.10683 Code/models/data/etc: https://git.io/Je0cZ Summary
(1/14)pic.twitter.com/VP1nkkHefB
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Luke Metz proslijedio/la je Tweet
Happy to announce our paper on Generalized Inner Loop Meta Learning, aka Gimli (https://arxiv.org/abs/1910.01727 ), with
@brandondamos,@denisyarats, Phu Mon Htut, Artem Molchanov, Franziska Meier,@douwekiela,@kchonyc, and@soumithchintala. THREAD [1/6]pic.twitter.com/oBF53sBeuc
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Luke Metz proslijedio/la je Tweet
Unsupervised Doodling and Painting with Improved SPIRAL “Under the right circumstances, some aspects of human drawing can emerge from simulated embodiment, without the need for external supervision, imitation or social cues.” https://learning-to-paint.github.io pdf https://arxiv.org/abs/1910.01007 pic.twitter.com/BGWBYm6VGs
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Luke Metz proslijedio/la je Tweet
I'd like to organize a "manifesto track" at AI conferences where speakers come up and present their controversial, slightly unhinged views on how to achieve AGI. Experimental evidence is optional. - Numenta HTM - LeCake - https://openreview.net/pdf?id=SyxQ_TEFwS … - Non-Axiomatic Reasoning System
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Luke Metz proslijedio/la je Tweet
In this article, we develop some key intuitions around Temporal Difference learning and why it is such an effective tool in Reinforcement Learning. I hope the interactive diagrams that
@ch402 and I built are useful! https://twitter.com/distillpub/status/1178754455707082752 …pic.twitter.com/t5JUQV0n6r
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