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Events Archive

Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control
We introduce Symplectic ODE-Net (SymODEN), a deep learning framework which can infer the dynamics of a physical system, given by an ordinary differential equation (ODE), from observed state trajectories.
Location: CSML Classroom 103
Speaker(s):
Desmond Zhong
Tags: ML for the Sciences

Foundations of Deep Learning with PyTorch

Foundations of Deep Learning with PyTorch

Of the many deep learning frameworks, PyTorch has largely emerged as the first choice for researchers. This workshop will show participants how to implement and train common network architectures in PyTorch. Special topics will be included as time permits. Participants should have some knowledge of Python, NumPy and deep learning theory.

Location: 138 Lewis Science Library
Speaker(s):
Alfredo Canziani
NYU Computer Science Department and Courant Institute
Tags: Research Computing Workshop

Preference Modeling with Context-Dependent Salient Features

Preference Modeling with Context-Dependent Salient Features

This talk considers the preference modeling problem and addresses the fact that pairwise comparison data often reflects irrational choice, e.g. intransitivity. Our key observation is that two items compared in isolation from other items may be compared based on only a salient subset of features.

Location: 214 Fine Hall
Speaker(s):
LAURA BALZANO
UNIVERSITY OF MICHIGAN
Preference Modeling with Context-Dependent Salient Features
Tags: CSML Seminar Series

Massively Parallel Evolutionary Computation for Empowering Electoral Reform

Massively Parallel Evolutionary Computation for Empowering Electoral Reform
Important insights into redistricting can be gained by formulating and analyzing the problem with a Markov Chain Monte Carlo framework that utilizes optimization heuristics to inform transition proposals.
Location: Corwin Hall 127
Speaker(s):
Wendy Tam Cho
UIUC, Politics/Computer Science
Tags: CSML Seminar Series

Deep convolutional neural networks for multi-scale time-series classification in fusion devices

Deep convolutional neural networks for multi-scale time-series classification in fusion devices

The multi-scale, mutli-physics nature of fusion plasmas makes predicting plasma events challenging. Recent advances in deep convolutional neural network architectures (CNN) utilizing dilated convolutions enable accurate predictions on sequences which have long-range, multi-scale characteristics, such as the time-series generated by diagnostic...

Speaker(s):
Michael Churchill
Princeton Plasma Physics Lab
Tags: ML for the Sciences

Wearable Brain-Machine Interface Architectures for Neurocognitive Stress

Wearable Brain-Machine Interface Architectures for Neurocognitive Stress
The human body responds to neurocognitive stress in multiple ways through its autonomic nervous system. Changes in skin conductance measurements indicate sudomotor nerve activity, and could be used in inferring the underlying autonomic nervous system stimulation. We model skin conductance measurements using a state-space model with sparse...
Location: B205 Engineering Quadrangle
Speaker(s):
Rose Faghih
University of Houston
Tags: CSML Seminar Series

Scalable Semidefinite Programming

Scalable Semidefinite Programming
Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking potential for data science applications. This talk develops new provably correct algorithms for solving large SDP problems by economizing on both the storage and the arithmetic costs. We present two methods: one based on sketching, and the other on...
Location: 214 Fine Hall
Speaker(s):
Madeleine Udell
Cornell University
Tags: CSML Seminar Series

Targeted Machine Learning for Causal Inference

Targeted Machine Learning for Causal Inference
We review targeted minimum loss estimation (TMLE), which provides a general template for the construction of asymptotically efficient plug-in estimators of a target estimand for infinite dimensional models.
Location: 399 Julis Romo Rabinowitz
Speaker(s):
Mark van der Laan
UC Berkeley, Biostatistics/Statistics
Tags: CSML Seminar Series

Generalized Lagrangian Networks

Generalized Lagrangian Networks

Even though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics. How might we endow them with better inductive biases? Recent work (Greydanus et al. 2019) proposed Hamiltonian Neural Networks (HNNs) which can learn the Hamiltonian of a physical system from data using a neural network.

Location: 26 Prospect Ave, Classroom 103
Speaker(s):
Sam Greydanus
Google AI
Tags: ML for the Sciences

Solving Inverse Problems with Data-driven Priors

Solving Inverse Problems with Data-driven Priors

I will present a Bayesian machine learning architecture that combines a physically motivated parameterization and an analytic error model for the likelihood with a deep generative model providing a powerful data-driven prior for complex signals.

Location: 26 Prospect Ave, Classroom 103
Speaker(s):
Peter Melchior
Princeton University
Tags: ML for the Sciences

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