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This is what I've been doing for quite some time. Recurrent Independent Mechanisms (RIMs) https://arxiv.org/abs/1909.10893 , joint work with Alex Lamb,
@shagunsodhani ,@jordanhoffmann,@svlevine,@bschoelkopf and Yoshua Bengio.@MILAMontreal Here's a short summary:Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Invited speakers include Yoshua Bengio,
@AlisonGopnik,@tobigerstenberg, Lars Buesing,@bschoelkopf,@eliasbareinboim, Christina Heinze-Deml.https://twitter.com/anirudhg9119/status/1215381163541520384 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
We are organizing a workshop on Causal learning for Decision Making at
@iclr_conf along with@rosemary_ke@DeepSpiker@theophaneweber, Jovana Mitrovic,@janexwang, Stefan and@csilviavr. https://sites.google.com/view/causal-learning-icrl2020/home …@MILAMontreal Consider submitting your work!Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Thanks! Previous work like Outrageously Large Neural Networks (https://arxiv.org/abs/1701.06538 ) as well as Sparse Attentive Backtracking (https://arxiv.org/abs/1809.03702 ) introduced have also used dynamic sparse attention.https://twitter.com/Thom_Wolf/status/1213592668644749314 …
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This paper got accepted at ICLR'20. Would not have been possible without
@shagunsodhani, Jonathan Binas,@xbpeng4,@svlevine and Yoshua Bengio.@MILAMontreal. More feedback is very much appreciated :)https://twitter.com/anirudhg9119/status/1143726116835942400 …
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See, also the followup to this led by
@rosemary_ke : ) https://arxiv.org/abs/1910.01075Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
This work also got accepted at
@iclr_conf. "A meta-transfer objective for learning to disentangle causal mechanisms" https://openreview.net/forum?id=ryxWIgBFPS … Led by Yoshua Bengio.@MILAMontrealhttps://twitter.com/anirudhg9119/status/1090826536775761922 …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
This paper got accepted at
@iclr_conf. Great work by@nasim_rahaman. Along with Steffen Wolf, Roman Remme, Yoshua Bengio.@MILAMontreal "Learning the Arrow of Time for Problems in Reinforcement Learning" https://openreview.net/forum?id=rylJkpEtwS …https://twitter.com/nasim_rahaman/status/1208132586939260935 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Two things which I really enjoy
@iclr_conf : 1. Going through reviews of accepted papers. 2. Going through papers which got "rejected", and understanding what other groups are doing ;)Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Lesson learned: If you have reviewers, who are not confident, then during the review process explicitly ask AC to assign a new reviewer. One of the papers got rejected with 3 weak accepts, and in discussion reviewers said they are satisfied but PCs overruled.
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Another reminder to read, Parallel Distributed Processing (PDP) book, https://dl.acm.org/citation.cfm?id=104279 … :)
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"Extending Machine Language Models toward Human-Level Language Understanding" by Jay McClelland, with
@jasonbaldridge,@FelixHill84, Maja Rudolph, Hinrich Schutze. https://arxiv.org/abs/1912.05877 is interesting read (Even if you aren't interested in language)Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Environments must allow sufficient causal systematically to allow transfer of information.
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I strongly agree with this. Data distribution should be growing (adding more tasks more time) , composite (combination of two or more sub-tasks) as well as diverse (various different tasks).https://twitter.com/santoroAI/status/1205574118898327552 …
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Yoshua Bengio talking about "why backprop is not biologically plausible"
@MILAMontreal@CIFAR_Newspic.twitter.com/zHPUjG7r2s
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1) Infobot: https://arxiv.org/abs/1901.10902 2) Sparse Attentive Backtracking https://arxiv.org/abs/1809.03702 3) Recurrent Independent Mechanisms https://arxiv.org/abs/1909.10893
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Lot of our work has been in combining system 1 and system 2, but system 2 is also implemented within the framework of gradient based learning, and incorporating such ideas seems to improve generalization.https://twitter.com/GaryMarcus/status/1201302003537723392 …
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Well, many researchers understand this and are actively trying to build systems which has these inductive biases.https://twitter.com/GaryMarcus/status/1200646264150323200 …
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Once this happens, the life-force sustaining the debate vanishes, and only shitstorms are left. (2/2)
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Civilized debates start off with certain common axioms shared by all participants. When they are not, and people rely on different or even opposite axioms, people end up talking right past each other, and soon someone will believe that the other has bad faith. (1/2)
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This is probably the most succinct summary https://arxiv.org/abs/1911.10500 of various ways in which causality could be useful for machine learning by
@bschoelkopf Highly recommended.Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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