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AnimaAnandkumar's profile
Prof. Anima Anandkumar
Prof. Anima Anandkumar
Prof. Anima Anandkumar
@AnimaAnandkumar

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Prof. Anima Anandkumar

@AnimaAnandkumar

Director of #AI #research @nvidia, Bren #Professor @Caltech, Fmr Principal scientist @awscloud #Sloan fellow #Tensors Erdos #2 #dancer http://anima-ai.org 

Santa Clara, CA
tensorlab.cms.caltech.edu/users/anima/
Joined October 2014

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    1. Prof. Anima Anandkumar‏ @AnimaAnandkumar 7 Dec 2019
      • Report Tweet
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      Prof. Anima Anandkumar Retweeted Yann LeCun

      I disagree. It's difficult to understand and fix bias through #AI algorithms for #DeepLearning These are highly non-linear blackbox models. they amplify biases. many works show that trying to fix superficially is like putting lipstick on a pig. We need fresh thinkinghttps://twitter.com/ylecun/status/1203211859366576128 …

      Prof. Anima Anandkumar added,

      Yann LeCun @ylecun
      People are biased. Data is biased, in part because people are biased. Algorithms trained on biased data are biased. But learning algorithms themselves are not biased. Bias in data can be fixed. Bias in people is harder to fix. https://www.nytimes.com/2019/12/06/business/algorithm-bias-fix.html#click=https://t.co/lkLEpfDwF9 …
      33 replies 182 retweets 701 likes
      Show this thread
    2. Prof. Anima Anandkumar‏ @AnimaAnandkumar 7 Dec 2019
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      Prof. Anima Anandkumar Retweeted Prof. Anima Anandkumar

      In a recent work we report an intriguing finding to detect bias : assess hardness of different samples for #DeepLearning model. It's a first step. Using it to fix bias is much harderhttps://twitter.com/animaanandkumar/status/1203090855097057280 …

      Prof. Anima Anandkumar added,

      Prof. Anima Anandkumar @AnimaAnandkumar
      Intriguing measure of #bias in #DeepLearning We find angular distance as a robust + universal measure of hardness of a training example and corresponds with human ambiguity. @beidichen @animesh_garg @jankautz @NvidiaAI https://twitter.com/Deep__AI/status/1203028047651205120 …
      Show this thread
      3 replies 6 retweets 64 likes
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    3. Prof. Anima Anandkumar‏ @AnimaAnandkumar 7 Dec 2019
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      More importantly our work shows that #DeepLearning model is deciding which samples are hard for it to classify and introducing bias. Here I use bias to mean disparate treatment not statistical bias. Model leaves harder example with worse accuracy and more vulnerability to noise

      1 reply 2 retweets 42 likes
      Show this thread
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      Prof. Anima Anandkumar‏ @AnimaAnandkumar 8 Dec 2019
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      Replying to @patrick_s_smart

      We believe it is aleatoric uncertainty since it has statistically significant correlation with human selection frequency. Latter is good surrogate for aleatoric uncertainty. But we also find that angular measure improves with better models, e.g. resnet vs alexnet)

      3:17 PM - 8 Dec 2019
      • 2 Likes
      • Sudha Jamthe Patrick Slavenburg #ArtificialIntelligence
      0 replies 0 retweets 2 likes

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