New paper: "Accounting for the Neglected Dimensions of AI Progress": https://t.co/uvD4sU0uPr
— Miles Brundage (@Miles_Brundage) June 5, 2018
Joint work with Fernando Martínez-Plumed, Shahar Avin, Allan Dafoe, Seán Ó hÉigeartaigh, and José Hernández-Orallo.
(1/n) pic.twitter.com/d0MPukms3Y
To coincide with the talk and the paper, we are releasing a new set of ELMo models of various sizes/amounts of data. Download at https://t.co/dOpuNmUeHy - seamless integration with #allennlp and the original tensorflow code. #naacl2018
— Mark Neumann (@MarkNeumannnn) June 4, 2018
From a paper I'm reading: "we have also created a docker container that reproduces all the experiments and figures in this paper". Very nice! A high bar, but I think eventually this should be the norm in machine learning conferences too.
— Dumitru Erhan (@doomie) June 4, 2018
"Learning a Latent Space of Multitrack Measures," Simon et al.: https://t.co/tlN2UPJdr4
— Miles Brundage (@Miles_Brundage) June 4, 2018
Samples: https://t.co/MwT4mMRfEp
The interpolation from Billie Jean to Don't Stop Believin' is quite something, makes me think a competent/"creative" AI DJ/remix isn't long off...
I wrote a post explaining "Yes, but Did It Work?: Evaluating Variational Inference" by Yao et al. https://t.co/fQYYYo4ibc (ICML 2018), mostly for my own understanding https://t.co/z2OfSsAxb6
— Stephanie Hyland (@_hylandSL) June 3, 2018
Learned a lot about LSTM behavior -- in very different ways -- from two excellent @acl2018 papers: Sharp Nearby, Fuzzy Far Away... by @ukhndlwl, He He, Peng Qi, and @jurafsky, and LSTM as Dynamically Computed... by @omerlevy_ , @kentonctlee, @nfitz, @lukezettlemoyer.
— John Hewitt (@johnhewtt) June 2, 2018
Peyman Passban, Qun Liu, Andy Way (presented by @seb_ruder ): Improving Character-based Decoding Using Target-Side Morphological Information for Neural Machine Translation https://t.co/Cx2PuxNgSd #NAACL2018
— Yuval Pinter (@yuvalpi) June 2, 2018
Distributional regression forests on arXiv https://t.co/JWmbr4n2jh #rstats #DataScience
— R-bloggers (@Rbloggers) June 2, 2018
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting #acl2018
— ML Review (@ml_review) June 2, 2018
By @YenChunChen4
Fast summarization model that selects salient sentences and then rewrites them abstractively (i.e., compresses and paraphrases)https://t.co/kpX41Wbqgq pic.twitter.com/ZokX15wCui
Reward design in RL is a bit of a mystical art. We can start to demystify it using control as inference, and also learn rewards from data using variational inverse control with events (VICE): https://t.co/VmUeMLg4PN
— Sergey Levine (@svlevine) June 1, 2018
w/ Justin Fu, @avisingh599 Dibya Ghosh, Larry Yang
Really cool work from the Brunskill lab at Stanford on using model-based RL! https://t.co/s7bbugQq01
— Karan Goel (@krandiash) June 1, 2018
Our recent paper with Vineet Gupta and @philipmlong https://t.co/DB1MUv2O5i
— Hanie Sedghi (@HanieSedghi) June 1, 2018
