machine learning – Page 54 – Andrew Fairless, Ph.D.

What I Read: start deploying

By Andrew Fairless on January 6, 2021December 22, 2020

https://towardsdatascience.com/stop-training-more-models-start-deploying-them-using-webassembly-49a3f178569e Stop training more models, start deploying them (using WebAssembly)We are hardly living up to the promises of AI in healthcare. It’s not because of our training, it’s because ofContinue readingWhat I Read: start deploying

What I Read: Progress of Natural Language Processing

By Andrew Fairless on January 5, 2021December 22, 2020

https://blog.exxactcorp.com/the-unreasonable-progress-of-deep-neural-networks-in-natural-language-processing-nlp/ Deep LearningThe Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP)Marketing, June 2, 2020 0 14 min read “With the advent of pre-trained generalized language models, weContinue readingWhat I Read: Progress of Natural Language Processing

What I Read: making machine learning actually useful

By Andrew Fairless on January 5, 2021December 22, 2020

https://www.shreya-shankar.com/making-ml-work/ Shreya ShankarReflecting on a year of making machine learning actually usefulJune 29, 2020 in #machine learning · 11 min read “…ML research as a field puts too much emphasisContinue readingWhat I Read: making machine learning actually useful

What I Read: Symbolic Models from Deep Learning

By Andrew Fairless on January 4, 2021December 22, 2020

https://astroautomata.com/paper/symbolic-neural-nets/ Discovering Symbolic Models from Deep Learning with Inductive BiasesPaper Code Interactive NotebookPaper authors: Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, Shirley Ho “…when doesContinue readingWhat I Read: Symbolic Models from Deep Learning

What I Read: Who Is Responsible When Autonomous Systems Fail?

By Andrew Fairless on January 4, 2021December 22, 2020

https://www.cigionline.org/articles/who-responsible-when-autonomous-systems-fail Who Is Responsible When Autonomous Systems Fail?Madeleine Clare ElishJune 15, 2020 “…conceptions of legal liability and responsibility did not adequately keep pace with advances in technology… while the controlContinue readingWhat I Read: Who Is Responsible When Autonomous Systems Fail?

What I Read: GPT-3, a Giant Step for NLP

By Andrew Fairless on January 3, 2021December 22, 2020

https://anotherdatum.com/gpt-3.html GPT-3, a Giant Step for Deep Learning And NLPPosted on 03 June 2020 “…OpenAI announced a new successor to their Language Model (LM) – GPT-3. This is the largestContinue readingWhat I Read: GPT-3, a Giant Step for NLP

What I Read: Nitpicking ML Technical Debt

By Andrew Fairless on January 2, 2021December 22, 2020

https://matthewmcateer.me/blog/machine-learning-technical-debt/ Nitpicking Machine Learning Technical DebtRevisiting a resurging NeurIPS 2015 paper (and 25 best practices more relevant than that for 2020)May 10, 2020 “Now the Tech Debt Paper is makingContinue readingWhat I Read: Nitpicking ML Technical Debt

What I Read: Differentiable Reasoning over Text

By Andrew Fairless on January 1, 2021December 22, 2020

https://blog.ml.cmu.edu/2020/05/15/differentiable-reasoning-over-text/ Differentiable Reasoning over TextAuthorsBhuwan DhingraPublishedMay 15, 2020 “Modern search engines not only retrieve a list of pages relevant to a query but often try to directly answer our questionsContinue readingWhat I Read: Differentiable Reasoning over Text

What I Read: Flows for simultaneous manifold learning and density estimation

By Andrew Fairless on December 31, 2020December 20, 2020

https://arxiv.org/abs/2003.13913 Flows for simultaneous manifold learning and density estimationJohann Brehmer, Kyle Cranmer “We introduce manifold-learning flows (M-flows), a new class of generative models that simultaneously learn the data manifold asContinue readingWhat I Read: Flows for simultaneous manifold learning and density estimation

What I Read: Learning Neural Causal Models

By Andrew Fairless on December 30, 2020December 20, 2020

https://arxiv.org/abs/1910.01075Learning Neural Causal Models from Unknown InterventionsNan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio “We present a new framework for meta-learning causal modelsContinue readingWhat I Read: Learning Neural Causal Models

Tag: machine learning