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Sahil Garg proslijedio/la je Tweet
Anima Anandkumar's Caltech 2020 lectures on "Foundations of Machine Learning and Statistical Inference" are now available online. https://www.youtube.com/playlist?list=PLVNifWxslHCDlbyitaLLYBOAEPbmF1AHg …pic.twitter.com/kECXUeDv0r
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Sahil Garg proslijedio/la je Tweet
Teaching Deep Unsupervised Learning (2nd edition) at
@UCBerkeley this semester. You can follow along here: https://sites.google.com/view/berkeley-cs294-158-sp20/home … Instructor Team:@peterxichen ,@Aravind7694 ,@hojonathanho , Wilson Yan, Alex Li,@pabbeel YouTube, PDF, and Google Slides for ease of re-usepic.twitter.com/VTvffsEjHf
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Sahil Garg proslijedio/la je Tweet
Very happy to share a paper that student Arjun Seshadri and I have been working on for quite a while: "Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice" http://arxiv.org/abs/2001.07042 Feedback sought! Thread! 1/npic.twitter.com/3HfJeNtfwg
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Sahil Garg proslijedio/la je Tweet
Very excited by our research on perceptual robotics.
@StanfordSVL members and collaborators will present 6 research papers at this year’s#ICRA2020 I’m starting to feel like a legit robotics researcher ;)@jcniebles@silviocinguetta@yukez@animesh_garg@deanh_tw@eadelipic.twitter.com/2zDGFI8mQR
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Sahil Garg proslijedio/la je Tweet
Really exciting list of papers for
#MLSys20, at the intersection of machine learning and systems!https://twitter.com/atalwalkar/status/1220060498672214018 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Sahil Garg proslijedio/la je Tweet
Distinguished Scientist
@SriramRajamani leads Microsoft research efforts in India. He discusses the unique challenges and opportunities, addressing societal scale issues like healthcare, and rethinking what we know about underserved groups: https://aka.ms/AA71c6a#MSRPodcastHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Sahil Garg proslijedio/la je Tweet
I woke up thinking of a list of inconvenient truths. It's an exercise I like to do sometimes. This is what I came up with so far:
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Sahil Garg proslijedio/la je Tweet
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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Great analysis on compensation trends and what effects they play on career progression!
@chiprohttps://twitter.com/chipro/status/1219108602712952832 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Sahil Garg proslijedio/la je TweetHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Sahil Garg proslijedio/la je Tweet
Question: How long does a loop last? . . . For a while.
#DevJokeHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Sahil Garg proslijedio/la je Tweet
From
@AndrewYang to@ElonMusk, many worry about the significant economic future impacts of AI. Economist@jMateosGarcia predicts that instead, “the future of AI in the economy will resemble the Internet more than Skynet.”#ai#economicshttps://thegradient.pub/the-economics-of-ai-today/ …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Sahil Garg proslijedio/la je Tweet
I've started to upload the videos for the Neural Nets for NLP class here: https://www.youtube.com/playlist?list=PL8PYTP1V4I8CJ7nMxMC8aXv8WqKYwj-aJ … We'll be uploading the videos regularly throughout the rest of the semester, so please follow the playlist if you're interested.https://twitter.com/gneubig/status/1216792330273001472 …
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Sahil Garg proslijedio/la je Tweet
Text-based games provide a platform to train RL agents that generate goal-driven language. Jericho framework by
@mhauskn@rajammanabrolu@Cote_Marc &@ericxyuan provides benchmarks for scaling RL to combinatorially sized language action spaces: https://aka.ms/AA706nm#AAAI2020Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Sahil Garg proslijedio/la je Tweet
The Diversity-Innovation Paradox in Science: Why does greater diversity in research teams increase innovation but not directly reward minority scholars? New from
@BasHofstra,@viveksck, Sebastian Munoz-Najar Galvez, Bryan He,@jurafsky, and Dan McFarland. http://arxiv.org/abs/1909.02063 pic.twitter.com/05ebN7HORq
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Sahil Garg proslijedio/la je Tweet
How does deep learning perform DEEP learning? Microsoft and CMU researchers establish a principle called "backward feature correction" and explain how very deep neural networks can actually perform DEEP hierarchical learning efficiently: https://aka.ms/AA70ptc
@ZeyuanAllenZhuHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Sahil Garg proslijedio/la je Tweet
Researchers at Microsoft &
@Stanford developed an algorithmic approach to alleviate the imperfections of generative models via importance weighting. Learn how this approach can be used to boost any existing generative model: https://aka.ms/AA6z5vb#NeurIPS2019Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Sahil Garg proslijedio/la je Tweet
Happy to release NN4NLP-concepts! https://github.com/neulab/nn4nlp-concepts … It's a typology of important concepts that you should know to implement SOTA NLP models using neural nets: https://github.com/neulab/nn4nlp-concepts/blob/master/concepts.md … 1/3 We'll reference this in CMU CS11-747 this year, trying to maximize coverage. 1/3 https://twitter.com/gneubig/status/1216792330273001472 …pic.twitter.com/ILFeobZmPM
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Sahil Garg proslijedio/la je Tweet
What will the new age of computer-assisted learning and decision-making look like?
#AI expert@EmmaBrunskill talks with@Rbaltman on The Future of Everything. https://stanford.io/2FNX9FF#machinelearningHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Sahil Garg proslijedio/la je Tweet
What Does
#BERT Look At? An Analysis of BERT’s Attention#deeplearning https://nlp.stanford.edu/pubs/clark2019what.pdf …pic.twitter.com/QfZob48w5R
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