Gongguo Tang; Department of Electrical, Computer, and Energy Engineering; University of Colorado Boulder Geometry and algorithm for some nonconvex optimizations Great progress has been made in the past few years in our understanding of nonconvex optimizations. In this talk, I will share with you three of our works in this...
Samy Wu Fung, Department of Applied Mathematics and Statistics, Colorado School of Mines Efficient Training of Infinite-depth Neural Networks via Jacobian-free Backpropagation A promising trend in deep learning replaces fixed depth models by approximations of the limit as network depth approaches infinity. This approach uses a portion of network weights...
Pratyush Tiwary; Department of Chemistry & Biochemistry and Institute for Physical Science and Technology; University of Maryland From atoms to emergent dynamics (with help from statistical physics and artificial intelligence) ABSTRACT: The ability to rapidly learn from high-dimensional data to make reliable predictions about the future of a given system...
Anindya De, Department of Computer and Information Science, University of Pennsylvania Testing noisy linear functions for sparsity Consider the following basic problem in sparse linear regression -- an algorithm gets labeled samples of the form (x, + \eps) where w is an unknown n-dimensional vector, x is drawn from a...
Stephen Becker, Department of Applied Mathematics, University of Colorado Boulder Stochastic Subspace Descent: Stochastic gradient-free optimization, with applications to PDE-constrained optimization We describe and analyze a family of algorithms that generalize block-coordinate descent, where we assume one can take directional derivatives (for low-precision optimization, this can be approximated with finite...
Zhihui Zhu, Department of Electrical and Computer Engineering, University of Denver Provable Nonsmooth Nonconvex Approaches for Low-Dimensional Models As technological advances in fields such as the Internet, medicine, finance, and remote sensing have produced larger and more complex data sets, we are faced with the challenge of efficiently and effectively...
Amir Ajalloeian; Department of Electrical, Computer, and Energy Engineering; University of Colorado Boulder Inexact Online Proximal-gradient Method for Time-varying Convex Optimization This paper considers an online proximal-gradient method to track the minimizers of a composite convex function that may continuously evolve over time. The online proximal-gradient method is "inexact,'' in...
Sriram Sankaranarayanan, Department of Computer Science, University of Colorado Boulder Reasoning about Neural Feedback Systems Data-driven components such as feedforward neural networks are increasingly being used in critical safety systems such as autonomous vehicles and closed-loop medical devices. Neural networks compute nonlinear functions. Relatively tiny networks present enormous challenges for...
Mohsen Imadi; Department of Computer Science and Engineering; University of California, San Diego Towards Learning with Brain Efficiency Modern computing systems are plagued with significant issues in efficiently performing learning tasks. In this talk, I will present a new brain-inspired computing architecture. It supports a wide range of learning tasks...
Alec Dunton, Department of Applied Mathematics, University of Colorado Boulder Learning a kernel matrix for nonlinear dimensionality reduction (Weinberger et. al. 2004) We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps...
