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Zhiting Hu proslijedio/la je Tweet
Saturday, February 8, 2020 - 10:45 AM - 12:30 PM SA6Q: Modularizing Natural Language Processing Zhengzhong Liu, Zhiting Hu, and Eric Xing
#AAAI2020#AAAI20pic.twitter.com/2wmXt837jh
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Zhiting Hu proslijedio/la je Tweet
Video & slides for LIRE workshop @
#NeurIPS2019 are now up: https://sites.google.com/view/neurips2019lire/schedule … Check out the Talks and Panel by@RaiaHadsell@tommmitchell Jeff Bilmes@pabbeel@YejinChoinka Tom Griffiths & more. Thanks to all speakers & presenters for making the workshop a success!pic.twitter.com/2WvsHpEkyZ
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DON’T MISS the exciting panel by our fantastic speakers, 17:05@West 208+209! https://twitter.com/ZhitingHu/status/1205526113390231554 …pic.twitter.com/r3u1Lw9R8f
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Zhiting Hu proslijedio/la je Tweet
Now
@YejinChoinka at Learning with Rich Experience: Integration of Learning Paradigms room 208pic.twitter.com/eIjGEIba9W
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Zhiting Hu proslijedio/la je Tweet
#NeurIPS2019 Tom Mitchell - conversational ml https://sites.google.com/view/neurips2019lire …pic.twitter.com/HC5Fwvthz2
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Come join the
#NeurIPS2019 workshop on Learning with Rich Experience. Note the location: West 208+209. Look fwd to the super exciting talks by@RaiaHadsell@tommmitchell JeffBilmes@pabbeel@YejinChoinka & TomGriffiths, and the contributed presentations: https://sites.google.com/view/neurips2019lire/accepted-papers …pic.twitter.com/MzzeLvnep6
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Zhiting Hu proslijedio/la je Tweet
The
#AISummit#NewYork is very pleased to announce@rsalakhu, UPMC Professor of Computer Science at@CarnegieMellon & Director of#AI Research at@Apple, as a shortlisted nominee for#AIInnovatoroftheYear 2019
Cast your vote for Ruslan here → http://spr.ly/60161RO8I pic.twitter.com/IMPWDu3VVp
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Zhiting Hu proslijedio/la je Tweet
Code release for
#NeurIPS2019 paper on Learning Data Manipulation: Learning to augment and re-weight data in low data regime or in presence of imbalanced labels. https://arxiv.org/abs/1910.12795 Code: https://github.com/tanyuqian/learning-data-manipulation … via@ZhitingHu & Bowen Tan.Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Zhiting Hu proslijedio/la je Tweet
Professor Russ Salakhutdinov
@rsalakhu, at@mldcmu, is a nominee for the#AIInnovatoroftheYear 2019
. Help us by casting your vote at https://mld.ai/ai3d https://twitter.com/Business_AI/status/1197552504525017088 …
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Composable machine learning powered by
#Texar (https://asyml.io )https://twitter.com/odsc/status/1191454064023871489 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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The paper “transfers” an off-the-shelf _reward_ learning algorithm to learning _data_ manipulation It’s a powerful idea--transferring solutions to problems in one context to problems in another. Used in learning structured knowledge http://arxiv.org/abs/1806.09764 , improving GANs/VAEshttps://twitter.com/rsalakhu/status/1189708661851074560 …
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Zhiting Hu proslijedio/la je Tweet
#NeurIPS2019 paper on Learning Data Manipulation: Learning to augment and re-weight data for improved training, especially in low data regime or in presence of imbalanced labels. https://arxiv.org/abs/1910.12795 w/t Zhiting Hu, Bowen Tan et. al.Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Zhiting Hu proslijedio/la je Tweet
Introducing Texar-PyTorch: An open-source
#ML library integrating the best of#TensorFlow into#PyTorch https://hubs.ly/H0lk2Lt0#machinelearning#NLP#opensourceHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Read more about Texar-PyTorch features & how easily you can customize any of the above modules for your project, either you're an ML novice or expert More resources: http://asyml.io 5/5https://medium.com/p/d89c2c2d1d61
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On training part, Texar-PyTorch replicates high-level APIs of tf.Estimator and tf.keras.Model, but with greater flexibility + TensorBoard + hyperparameter tuning APIs 4/5pic.twitter.com/sFuxN6Q5WR
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On data part, Texar-PyTorch replicates best practice of http://tf.data for easy processing, batching, iterating + efficiency w/ buffered shuffling, caching, lazy-loading. It also replicates TFRecord to ingest arbitrary complex data dataset, eg, image+caption+label 3/5pic.twitter.com/0loznHPcnQ
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On model part, Texar-PyTorch replicates abundant TF modules & utils, including the excellent text generation ones. See the list of Texar modules (selected): 2/5pic.twitter.com/PM4OEabvMd
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Super excited to release Texar-PyTorch v0.1 https://github.com/asyml/texar-pytorch … An ML library integrating the best of TensorFlow into PyTorch - replicating many useful TF modules & designs to enhance PyTorch, incl. data, model & training. See how Texar-Pytorch builds a Conditional-GPT2 1/5pic.twitter.com/v209h2OKq0
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