Yichong Xu

11 posts

Yichong Xu

Yichong Xu

@xycking

Research Scientist @ Google DeepMind

Mountain View, CA Katılım Mayıs 2009
120 Takip Edilen129 Takipçiler
Yichong Xu
Yichong Xu@xycking·
@YiMaTweets A good theoretical paper not only tries to explain why the method works (which is usually not a complete explanation), but also proposes new methods/verifications according to the theory. I think that's essential for acceptance :)
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Yi Ma
Yi Ma@YiMaTweets·
Start to see quite a few "theoretical" papers on analyzing diffusion models. Folks, a good theory is supposed to lead and project, not to always trail as hindsight...
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Ziniu Hu
Ziniu Hu@acbuller·
Excited to receive the #SoCalNLP Best Paper Award for our paper "Empowering Language Models with Knowledge Graph Reasoning for Question Answering". The paper link is: arxiv.org/abs/2211.08380 Thanks to the organizers and all the great collaborators!
UC Santa Barbara NLP Group@ucsbNLP

Our @MegagonLabs Best Paper Award winner was "Empowering Language Models with Knowledge Graph Reasoning for Question Answering" by Ziniu Hu et al from UCLA! Paper link: arxiv.org/abs/2211.08380 Thank you to award sponsor @MegagonLabs for supporting our event! (4/4)

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Wenhao Yu
Wenhao Yu@wyu_nd·
🎉 New preprint! Generate rather than Retrieve: Large Language Models are Strong Context Generators. Our proposed method achieved new SoTA on open-domain QA! (1/5) Arxiv link: arxiv.org/abs/2209.10063
Wenhao Yu tweet media
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Rishabh Agarwal
Rishabh Agarwal@agarwl_·
Outstanding + People's choice Award: Integrating Rankings into Quantized Scores in Peer Review by folks from @mldcmu and @MSFTResearch.
Rishabh Agarwal tweet media
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Mariya Toneva
Mariya Toneva@mtoneva1·
New post on ML@CMU blog about building binary classification and regression models that require fewer directly labeled examples by taking advantage of comparisons. Written by Yichong Xu and edited by @SimonShaoleiDu!
Machine Learning Dept. at Carnegie Mellon@mldcmu

Building Machine Learning Models via Comparisons by Yichong Xu @mldcmu Nowadays most #machinelearning models predict labels from features... Can we do more? Can we learn a model without too many feature-label pairs? Learn more: mld.ai/623bf #ML #AI

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Keşfet

@YiMaTweets @mldcmu @MSFTResearch @SimonShaoleiDu @elonmusk @BarackObama @taylorswift13 @cristiano