Underrated ML Sara Hooker & Sean Hooker

We conclude season one of Underrated ML by having Stephen Merity on as our guest. Stephen has worked at various institutions such as MetaMind and Salesforce ohana, Google Sydney, Freelancer.com, the Schwa Lab at the University of Sydney, the team at Grok Learning, the non-profit Common Crawl, and IACS @ Harvard. He also holds a Bachelor of Information Technology from the University of Sydney and a Master of Science in Computational Science and Engineering from Harvard University.

In this weeks episode we talk about the current influences of hardware in the field of Deep Learning research, baseline models, strongly typed RNNs and Alan Turings paper on the chemical basis of morphogenesis.

Underrated ML Twitter: https://twitter.com/underrated_ml
Stephen Merity Twitter: https://twitter.com/Smerity

Please let us know who you thought presented the most underrated paper in the form below: https://forms.gle/97MgHvTkXgdB41TC8

Links to the papers:
“The Chemical Basis of Morphogenesis” - https://www.dna.caltech.edu/courses/cs191/paperscs191/turing.pdf
"Strongly-Typed Recurrent Neural Networks” - https://arxiv.org/abs/1602.02218
"Quasi-Recurrent Neural Networks" - https://arxiv.org/abs/1611.01576

"An Analysis of Neural Language Modelling at Multiple Scales" - https://arxiv.org/abs/1803.08240
Additional Links:
Aleatory architecture / hysteresis: Why Birds Are The World's Best EngineersNear decomposability: Near decomposability and the speed of evolution / The Architecture of ComplexityGoogle's All Our N-gram are Belong to You from 2006

This week we are joined by Sebastian Ruder. He is a research scientist at DeepMind, London. He has also worked at a variety of institutions such as AYLIEN, Microsoft, IBM's Extreme Blue, Google Summer of Code, and SAP. These experiences were completed in tangent with his studies which included studying Computational Linguistics at the University of Heidelberg, Germany and at Trinity College, Dublin before undertaking a PhD in Natural Language Processing and Deep Learning at the Insight Research Centre for Data Analytics.

This week we discuss language independence and diversity in natural language processing whilst also taking a look at the attempts to identify material properties from images.

As discussed in the podcast if you would like to donate to the current campaign of "CREATE DONATE EDUCATE" which supports Stop Hate UK then please find the link below:
https://www.shorturl.at/glmsz
Please also find additional links to help support black colleagues in the area of research;
Black in AI twitter account: https://twitter.com/black_in_ai
Mentoring and proofreading sign-up to support our Black colleagues in research: https://twitter.com/le_roux_nicolas/status/1267896907621433344?s=20

Underrated ML Twitter: https://twitter.com/underrated_ml
Sebastian Ruder Twitter: https://twitter.com/seb_ruder

Please let us know who you thought presented the most underrated paper in the form below: https://forms.gle/97MgHvTkXgdB41TC8

Links to the papers:
“On Achieving and Evaluating Language-Independence in NLP” - https://journals.linguisticsociety.org/elanguage/lilt/article/view/2624.html
"The State and Fate of Linguistic Diversity and Inclusion in the NLP World” - https://arxiv.org/abs/2004.09095
"Recognizing Material Properties from Images" - https://arxiv.org/pdf/1801.03127.pdf
Additional Links:
Student perspectives on applying to NLP PhD programs: https://blog.nelsonliu.me/2019/10/24/student-perspectives-on-applying-to-nlp-phd-programs/Tim Dettmer's post on how to pick your grad school: https://timdettmers.com/2020/03/10/how-to-pick-your-grad-school/Rachel Thomas' blog post on why you should blog: https://medium.com/@racheltho/why-you-yes-you-should-blog-7d2544ac1045Emily Bender's The Gradient article: https://thegradient.pub/the-benderrule-on-naming-the-languages-we-study-and-why-it-matters/Paper on order-sensitive vs order-free methods: https://www.aclweb.org/anthology/N19-1253.pdf"Exploring the Origins and Prevalence of Texture Bias in Convolutional Neural Networks": https://arxiv.org/abs/1911.09071Sebastian's website where you can find all his blog posts: https://ruder.io/

This week we are joined by Kyunghyun Cho. He is an associate professor of computer science and data science at New York University, a research scientist at Facebook AI Research and a CIFAR Associate Fellow. On top of this he also co-chaired the recent ICLR 2020 virtual conference.
We talk about a variety of topics in this weeks episode including the recent ICLR conference, energy functions, shortcut learning and the roles popularized Deep Learning research areas play in answering the question “What is Intelligence?”.
Underrated ML Twitter: https://twitter.com/underrated_ml
Kyunghyun Cho Twitter: https://twitter.com/kchonyc?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor
Please let us know who you thought presented the most underrated paper in the form below:
https://forms.gle/97MgHvTkXgdB41TC8
Links to the papers:
“Shortcut Learning in Deep Neural Networks” - https://arxiv.org/pdf/2004.07780.pdf
"Bayesian Deep Learning and a Probabilistic Perspective of Generalization” - https://arxiv.org/abs/2002.08791
"Classifier-agnostic saliency map extraction" - https://arxiv.org/abs/1805.08249
“Deep Energy Estimator Networks” - https://arxiv.org/abs/1805.08306
“End-to-End Learning for Structured Prediction Energy Networks” - https://arxiv.org/abs/1703.05667
“On approximating nabla f with neural networks” - https://arxiv.org/abs/1910.12744
“Adversarial NLI: A New Benchmark for Natural Language Understanding“ - https://arxiv.org/abs/1910.14599
“Learning the Difference that Makes a Difference with Counterfactually-Augmented Data” - https://arxiv.org/abs/1909.12434
“Learning Concepts with Energy Functions” - https://openai.com/blog/learning-concepts-with-energy-functions/

This week we are joined by Ari Morcos. Ari is a research scientist at Facebook AI Research (FAIR) in Menlo Park working on understanding the mechanisms underlying neural network computation and function, and using these insights to build machine learning systems more intelligently. In particular, he has worked on a variety of topics, including understanding the lottery ticket hypothesis, self-supervised learning, the mechanisms underlying common regularizers, and the properties predictive of generalization, as well as methods to compare representations across networks, the role of single units in computation, and on strategies to measure abstraction in neural network representations. Previously, he worked at DeepMind in London.

Ari earned his PhD working with Chris Harvey at Harvard University. For his thesis, he developed methods to understand how neuronal circuits perform the computations necessary for complex behaviour. In particular, his research focused on how parietal cortex contributes to evidence accumulation decision-making.

In this episode, we discuss the importance of certain layers within neural networks.

Underrated ML Twitter: https://twitter.com/underrated_ml
Naila Murray Twitter: https://twitter.com/arimorcos

Please let us know who you thought presented the most underrated paper in the form below: https://forms.gle/97MgHvTkXgdB41TC8

Link to the paper:
"Are All Layers Created Equal?" [paper]

This week we are joined by Naila Murray. Naila obtained a B.Sc. in Electrical Engineering from Princeton University in 2007. In 2012, she received her PhD from the Universitat Autonoma de Barcelona, in affiliation with the Computer Vision Center. She joined NAVER LABS Europe (then Xerox Research Centre Europe) in January 2013, working on topics including fine-grained visual categorization, image retrieval, and visual attention. From 2015 to 2019 she led the computer vision team at NLE. She currently serves as NLE's director of science. She serves/served as area chair for ICLR 2018, ICCV 2019, ICLR 2019, CVPR 2020, ECCV 2020, and programme chair for ICLR 2021. Her research interests include representation learning and multi-modal search.

We discuss using sparse pairwise comparisons to learn a ranking function that is robust to outliers. We also take a look at using generative models in order to utilise once inaccessible datasets.

Underrated ML Twitter: https://twitter.com/underrated_ml
Naila Murray Twitter: https://twitter.com/NailaMurray

Please let us know who you thought presented the most underrated paper in the form below: https://forms.gle/97MgHvTkXgdB41TC8

Links to the papers:
"Interestingness Prediction by Robust Learning to Rank" [paper]
"Generative Models for Effective ML on Private Decentralized datasets" - [paper]

This week we take a look at the need for pooling layers within CNNs as well as discussing the regularization of CNNs using large-scale neuroscience data.

We are also very pleased to have Rosanne Liu join us on the show. Rosanne is a senior research scientist and a founding member of Uber AI. She is interested in making neural networks a better place and also currently runs a deep learning reading group called "Deep Learning: Classics and Trends".

Rosanne Liu Twitter: https://twitter.com/savvyrl?lang=en

Please let us know who you thought presented the most underrated paper in the form below:

https://forms.gle/97MgHvTkXgdB41TC8

Also let us know any suggestions for future papers or guests:

https://docs.google.com/forms/d/e/1FAIpQLSeWoZnImRHXy8MTeBhKA4bxRPVVnVXAUb0bLIP0bQpiTwX6uA/viewform

Links to the papers:

"Learning From Brains How to Regularize Machines" - https://arxiv.org/pdf/1911.05072.pdf
"Pooling is neither necessary nor sufficient for appropriate deformation stability in CNNs - https://arxiv.org/pdf/1804.04438.pdf
"Plug and play language models: A simple approach to controlled text generation" - https://arxiv.org/pdf/1912.02164.pdf