We propose a new structured pruning framework for compressing Deep Neura...
Using an amalgamation of techniques from classical radar, computer visio...
Numerous physical systems are described by ordinary or partial different...
We propose an asymmetric affinity score for representing the complexity ...
Audio signals are often represented as spectrograms and treated as 2D im...
We formulate a Fréchet-type asymmetric distance between tasks based on
F...
In this paper, we propose a neural architecture search framework based o...
The design of handcrafted neural networks requires a lot of time and
res...
Recent advances in time series classification have largely focused on me...
We introduce Projected Latent Markov Chain Monte Carlo (PL-MCMC), a tech...
In this paper, we study two important problems in the automated design o...
In this work, we introduce a new procedure for applying Restricted Boltz...
Neural architecture search (NAS), or automated design of neural network
...
Recently, Generative Adversarial Networks (GANs) have emerged as a popul...
In this paper, we study the general problem of optimizing a convex funct...
We consider the problem of reconstructing signals and images from period...
We consider the demixing problem of two (or more) structured high-dimens...
We study the problem of learning latent variables in Gaussian graphical
...
We consider the problem of estimation of a low-rank matrix from a limite...
Random sinusoidal features are a popular approach for speeding up
kernel...
We consider the demixing problem of two (or more) high-dimensional vecto...
We study the problem of demixing a pair of sparse signals from noisy,
no...