CILVR

Talks at TTIC presents Naomi Saphra (@nsaphra) of NYU (@CILVRatNYU) with a talk titled "Interpreting Training." Please join us on Monday, February 27 at 11:30 am CT in Room 530 or via Panopto: buff.ly/3xINPhu

Fantastic lineup! Submission deadline: September 18 #ML4PS2021

our own @KyleCranmer is a keynote speaker at @aistats_conf !

Courant Institute and Radiology at NYU are hiring postdocs in the area of learning unified representations of patients using healthcare data from multiple modalities (imaging, EHRs, sensors). An exciting opportunity to make real-world impact. Details at: apply.interfolio.com/85305

One of the coolest ideas I spent time on this last year considers a weird way to specify confounders. In most traditional causal estimation, the confounders and interventions of interest are different columns in a dataset of pre-outcome variables.

for the 3rd year in a row, we are carbon-neutral. thanks @sleepinyourhat for leading the effort!

I had a wonderful time chatting with @krrish94 and @duckietown_coo at the @MontrealRobots Robot Learning seminar yesterday! Talk is on recorded here: youtu.be/rPrqCPJc6wU

Translation equivariance has imbued CNNs with powerful generalization abilities. Our #NeurIPS2020 paper shows how to *learn* symmetries -- rotations, translations, scalings, shears -- from training data alone! arxiv.org/abs/2010.11882 w/ @g_benton_, @Pavel_Izmailov, @m_finzi. 1/9

A new NLP toolkit from @CILVRatNYU is now online with an accompanying blog post: wp.nyu.edu/cilvr/2020/10/…. check it out!

new blog post on evaluating the quality of representation!

tagging @KocijanVid!

A new blog post! <Influence Functions Do Not Seem to Predict Usefulness in NLP Transfer Learning> by Vid Kocijan and @sleepinyourhat. We value negative findings as much as shiny results and strive to share them which would've otherwise been hidden. wp.nyu.edu/cilvr/2020/08/…

Stochastic Weight Averaging (SWA) is a simple procedure that improves generalization in deep learning over Stochastic Gradient Descent (SGD). PyTorch 1.6 now includes SWA natively. Learn more from @Pavel_Izmailov, @andrewgwils and Vincent: pytorch.org/blog/pytorch-1…

I'm so excited to be presenting on "Bayesian Deep Learning" and "How do we build models that learn and generalize?" at the @SMILESkoltech Summer School. 10 am ET tomorrow+Mon, livestreamed on YouTube! Functional kernel learning will make an appearance. youtube.com/watch?v=i5PEMt…

New work in visual imitation learning! We use commodity reacher-grabbers to both collect demonstrations in the wild, and as an end-effector for robotic manipulation. This means no domain shift b/n the demonstrations and the robot's observations! arxiv.org/abs/2008.04899 1/5

Very excited to share our newest paper combining machine learning & physics. We develop normalizing flows that impose the elaborate of symmetry groups you find in fundamental particle physics. It's a beautiful mix of math, ML, and physics arxiv.org/abs/2008.05456

I had a wonderful experience presenting the ICML tutorial on Bayesian deep learning and probabilistic model construction. Thank you all for your encouraging comments. The video is now online! youtube.com/watch?v=E1qhGw…

The #ICML2020 tutorial on Bayesian Deep Learning and Probabilistic Model Construction is tomorrow! July 13, 11 am - 2 pm and 9 pm - 12 am EDT icml.cc/virtual/2020/t… See you there!

@chhaviyadav_ @nyuniversity thanks for the suggestion, and we'll look for a better solution to aggregate these info into one place in the future.

@chhaviyadav_ @nyuniversity because we don't have a CILVR-wide system to keep the website up-to-date with any dynamic info, we keep only the minimal info on the group webpage and refer people to individual faculty pages for more up-to-date, dynamic info. for instance, see cims.nyu.edu/~bruna/group/

hello world! this is a twitter handle of the machine learning lab, CILVR, at @nyuniversity. see our homepage at cilvr.nyu.edu and in particular our newly created blog at wp.nyu.edu/cilvr/cilvr-bl…