This program gets a score of 162 in Atari Bowling, which is now the new state-of-art for Bowling beating RUDDER's previously held state-of-art of 108. I didn't have a lot of resources so I only did Bowling. I hope the community can continue investigating this new type of AI. pic.twitter.com/1T3O1AH5qu
— Sherjil Ozair (@sherjilozair) June 24, 2018
Detecting Sarcasm with Deep Convolutional Neural Networks https://t.co/C05UdMjCoe pic.twitter.com/q50x9QqXhD
— KDnuggets (@kdnuggets) June 24, 2018
GrCAN: Gradient Boost Convolutional Autoencoder with Neural Decision Forest. https://t.co/2eqxqwDfDX pic.twitter.com/XN7hWfLjPa
— arxiv (@arxiv_org) June 23, 2018
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations—learning a dependency graph to do deep transfer learning. Jake Zhao: “Perhaps this can also be seen as encoding some relational inductive bias into the machinery” 🤔 https://t.co/TkN7y3XDQb HT @ylecun
— Stanford NLP Group (@stanfordnlp) June 22, 2018
How can you teach a machine learning system with human language rather than “labels”? With a semantic parser & labeling functions! New #ACL2018 paper by @bradenjhancock @paroma_varma @stephtwang @bringmartino @percyliang & Chris Ré @HazyResearch https://t.co/HMTdY5i5TZ #NLProc pic.twitter.com/VUsUdF2RjM
— Stanford NLP Group (@stanfordnlp) June 21, 2018
Learning Intrinsic Image Decomposition from Watching Videos https://t.co/CfAKDumeo5
— Nando de Freitas (@NandoDF) June 21, 2018
Very excited about our latest preprint: https://t.co/ViXaxMq8RZ, joint work with @arimorcos and Samy Bengio. We apply Canonical Correlation (CCA) to study the representational similarity between memorizing and generalizing networks, and also examine the training dynamics of RNNs. https://t.co/5QmRxml0in
— Maithra Raghu (@maithra_raghu) June 21, 2018
In our most recent collaboration with Google Brain, we measure the similarity between neural network representations to provide insights into generalisation and the training dynamics of RNNs. @arimorcos @maithra_raghu
— DeepMind (@DeepMindAI) June 21, 2018
Blog: https://t.co/7qXQFkBMKI
Paper: https://t.co/G9ErnfJruG
We're excited to announce the launch of Microsoft Research Open Data! This single, cloud-hosted location offers datasets representing many years of data curation and research efforts by Microsoft. Learn how it works: https://t.co/f4LhMxWlKF
— Microsoft Research (@MSFTResearch) June 21, 2018
Non-local Neural Networks: https://t.co/pu49vycxp9 #deeplearning #computervision pic.twitter.com/AUPZOc4qSV
— Learn PyTorch (@learnpytorch) June 21, 2018
New #ACL2018 paper on StructVAE, semi-supervised learning with structured latent variables: https://t.co/4vQieQk91p
— Graham Neubig (@gneubig) June 21, 2018
A new shift-reduce method for seq2tree neural models, semantic parsing results robust to small data, and nice analysis of why semi-supervised learning works! pic.twitter.com/9TKSkctOzC
We won the Best Paper Award at the #CVPR2018 Deep Learning for Visual SLAM workshop (https://t.co/QhujldJKMY) for our paper on
— Devendra Chaplot (@dchaplot) June 21, 2018
Global Pose Estimation with an Attention-based Recurrent Network: https://t.co/H1lyIZahbb!! Congratulations to my co-authors Emilio, Jian and @rsalakhu. https://t.co/4OSny3lX05
