DeepMind    @DeepMind   ·   10/13/2021
A unified way to discover and manipulate options: how the successor representation can be seen as a natural substrate for temporal abstraction in #ReinforcementLearning: New paper by @MarlosCMachado, @andre_s_barreto, and Doina Precup.
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Brad Neuberg    @bradneuberg   ·   9/26/2021
Domain Invariant Representation Learning with Domain Density Transformations: “We propose a method to learn a domain-invariant representation by enforcing the representation network to be invariant under all transformation functions among domains.“
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Ted Underwood    @Ted_Underwood   ·   6/3/2021
The annoying thing about topic modeling is that it surfaces any weird stuff in your dataset. So you run a giant topic model that takes 18 hours and then immediately discover "oh, I need to take out that weird stuff." Then wait another 18 hours, and discover ...
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Google AI    @GoogleAI   ·   8/4/2021
Natural speech often has disruptions and complexities that are difficult for #NLP models to understand. Today we introduce two benchmark datasets that challenge models on temporal reasoning (TimeDial) and contextual disfluencies (Disfl-QA). Details below:
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Sebastian Raschka    @rasbt   ·   10/4/2021
"Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies" ( Interesting take-aways re self-supervised representation learning from this work: (a) early stopping on the pre-text task benefits the downstream task, too [1/2]
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Brad Neuberg
Machine Learning Engineer at @planet. Research Affiliation at SETI & NASA FDL. Previously @ Dropbox and Google. Started coworking. More:

Antonin Raffin
Researcher in robotics and machine learning (Reinforcement Learning). Member of Stable-Baselines team: