# Seminars

## Upcoming Seminars

No upcoming events found.

## Previous Seminars

### The One World Seminar on the Mathematics of Machine Learning

### Leveraging Dataset Symmetries in Neural Network Prediction

Scientists and engineers are increasingly applying deep neural networks (DNNs) to modelling and design of complex systems. While the flexibility of DNNs makes them an attractive tool, it also makes their solutions difficult to interpret and their predictive capability difficult to quantify.

### Function Approximation via Sparse Random Fourier Features

### AI Meets Large-scale Sensing: preserving and exploiting structure of the real world to enhance machine perception

### Finite Width, Large Depth Neural Networks as Perturbatively Solvable Models

Abstract: Deep neural networks are often considered to be complicated "black boxes," for which a systematic analysis is not only out of reach but potentially impossible. In this talk, which is based on ongoing joint work with Dan Roberts and Sho Yaida, I will make the opposite claim. Namely, that deep neural networks at initialization are...

### Computational Optics for Control and Readout of Neural Activity

### Optimization Inspired Deep Architectures for Multiview 3D

### Deep Networks from First Principles

### Breaking the Sample Size Barrier in Statistical Inference and Reinforcement Learning

A proliferation of emerging data science applications require efficient extraction of information from complex data. The unprecedented scale of relevant features, however, often overwhelms the volume of available samples, which dramatically complicates statistical inference and decision making.

### HEE Seminar- Taylor Faucett-UCI-Physics Learning from Machines Learning

Machine Learning methods are extremely powerful but often function as black-box problem solvers, providing improved performance at the expense of clarity. Our work describes a new machine learning approach which translates the strategy of a deep neural network into simple functions that are meaningful and intelligible to the physicist, without...