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Prikvačeni tweet
Excited to share that I’m teaching a *new course* on multi-task & meta-learning! Topics incl. optimization-based meta-learning, lifelong learning, meta-RL , etc Slides & assignments being posted. Lecture videos to be publicly released after the course. http://cs330.stanford.edu pic.twitter.com/Gmve2kUvHW
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Chelsea Finn proslijedio/la je Tweet
To quote Hamming: "The great scientists often make this error. They fail to continue to plant the little acorns from which the mighty oak trees grow. They try to get the big thing right off." https://www.cs.virginia.edu/~robins/YouAndYourResearch.html …
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Excited to share PCGrad, a super simple & effective method for multi-task learning & multi-task RL: project conflicting gradients On Meta-World MT50, PCGrad can solve *2x* more tasks than prior methods https://arxiv.org/abs/2001.06782 w/ Tianhe Yu, S Kumar, Gupta,
@svlevine,@hausman_kpic.twitter.com/uTeUhULUTA
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We're organizing a workshop on 'Beyond "Tabula Rasa" in RL' (BeTR RL) at
@iclr_conf & looking forward to your submissions! Deadline: Feb 10 Invited speakers include@NandoDF, Ishita Dasgupta, Abhishek Gupta, Martha White, with@SchmidhuberAI as a panelist.https://twitter.com/LouisKirschAI/status/1216734205851447296 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Can robots learn about the world by observing humans? Learn to predict with both interaction & observation (of humans), then use the model to accomplish goals. http://arxiv.org/abs/1912.12773 http://sites.google.com/view/lpmfoai w. Schmeckpeper
@GRASPlab, Xie,@_oleh, Tian,@KostasPenn,@svlevinepic.twitter.com/2EeW02Dmy8Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Can we discover structure & meta-learn across it in unsegmented time series data? MOCA simultaneously detects changepoints & meta-learns across time for continuous adaptation Continuous Meta-Learning without Tasks https://arxiv.org/abs/1912.08866 w
@jmes_harrison, Sharma,@MarcoPavoneSUpic.twitter.com/fDMCd9MumVHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
I'll be discussing this work and other challenges in meta-learning at the
@NeurIPSConf Bayesian Deep Learning Workshop at 1:20 pm, West Exhibition Hall C. http://bayesiandeeplearning.org/ https://twitter.com/chelseabfinn/status/1204284213739933697 …
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Interested RL algorithms that learn to follow instructions, use hierarchical abstractions, and achieve compositional generalization? We investigate the many benefits of language in RL. We'll be at
@NeurIPSConf poster #197, at 10:45 am! w@yidingjiang@shaneguML Murphy@GoogleAI https://twitter.com/chelseabfinn/status/1141197084990697472 …pic.twitter.com/JDHnDBjk07
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Meta-RL relies heavily on manually-defined task distributions. CARML constructs curricula of tasks *without supervision* in the loop of meta-learning. https://arxiv.org/abs/1912.04226 On Weds, Allan Jabri will present a spotlight
@NeurIPSConf at 10:30 am in Hall A, poster #53 at 5:30 pm!pic.twitter.com/pvdD8t55yg
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Chelsea Finn proslijedio/la je Tweet
Can we distribute meta-RL, with local policy learners distilled into a centralized meta-policy? Find out about guided meta-policy search, which can solve complex meta-RL tasks! Russell Mendonca will present the work at NeurIPS at 4:45 pm in Ballroom A + B, poster #42 at 5:30 pmpic.twitter.com/lIaO5OYkao
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We're presenting our work on meta-learning with implicit differentiation
@NeurIPSConf Come find us at the Tuesday evening poster session #47, tomorrow 5:30-7:30 pm. https://twitter.com/chelseabfinn/status/1171597359953805313 …pic.twitter.com/T4FQbKSk2M
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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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Chelsea Finn proslijedio/la je Tweet
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@SuryaGanguli & I are co-organizing the next@StanfordHAI Conference Apr 1, 2020—no joke!—on the topic Triangulating Intelligence: Melding Neuroscience, Psychology, and AI. Botvinick—@YejinChoinka—@chelseabfinn—@AudeOliva—Tenenbaum—@dyamins—save the date!https://hai.stanford.edu/events/triangulating-intelligence-melding-neuroscience-psychology-and-ai …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Sudeep Dasari wrote an excellent blog post on our work on RoboNet, cross-posted on the SAIL blog: http://sail.stanford.edu/blog/robonet/ We imagine a future where robots share data *across* research labs, just like the rest of machine learning. https://twitter.com/svlevine/status/1199220226807590912 …pic.twitter.com/c77Sxl0Vqt
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Chelsea Finn proslijedio/la je Tweet
Delighted to share our Science article on making it easier ensure AI systems satisfy societal values. https://news.stanford.edu/2019/11/21/stanford-helps-train-ai-not-misbehave/?sf112920043=1 … Lead by former postdoc Phil Thomas, w/Castro da Silvam, Barto, Giguere, Brun.
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I'm on my way to
@Khipu_AI in Montevideo, Uruguay. Looking forward to giving a talk on ML for robots, joining the Women in AI panel, and meeting talented researchers! https://khipu.ai/ pic.twitter.com/RZDMMNThTQ
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Tired of your robot learning from scratch? We introduce RoboNet: a dataset that enables fine-tuning to new views, new envs, & entirely new robot platforms. https://robonet.wiki https://arxiv.org/abs/1910.11215 w/ Dasari
@febert8888 Tian@SurajNair_1 Bucher Schmeckpeper Singh@svlevinepic.twitter.com/BC82ZBx8YXHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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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An accessible blog post and nice visualizations by
@fhuszar on our NeurIPS paper on meta-learning with implicit gradients!https://twitter.com/fhuszar/status/1174691316829184003 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
New paper: Hierarchical visual foresight learns to generate visual subgoals to break down a goal into smaller pieces. Accomplishes long-horizon vision-based tasks, without *any* supervision. w/ Suraj Nair
@GoogleAI Paper: https://arxiv.org/abs/1909.05829 Code: https://github.com/google-research/google-research/tree/master/hierarchical_foresight …pic.twitter.com/y7U5BfKvAzHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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