research
∙
01/26/2022
On The Energy Statistics of Feature Maps in Pruning of Neural Networks with Skip-Connections
We propose a new structured pruning framework for compressing Deep Neura...
share
research
∙
01/26/2022
Toward Data-Driven STAP Radar
Using an amalgamation of techniques from classical radar, computer visio...
research
∙
11/26/2021
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions
Numerous physical systems are described by ordinary or partial different...
research
∙
10/05/2021
Task Affinity with Maximum Bipartite Matching in Few-Shot Learning
We propose an asymmetric affinity score for representing the complexity ...
research
∙
05/31/2021
Towards Explainable Convolutional Features for Music Audio Modeling
Audio signals are often represented as spectrograms and treated as 2D im...
research
∙
03/23/2021
Neural Architecture Search From Fréchet Task Distance
We formulate a Fréchet-type asymmetric distance between tasks based on F...
research
∙
02/27/2021
Neural Architecture Search From Task Similarity Measure
In this paper, we propose a neural architecture search framework based o...
research
∙
10/27/2020
Task-Aware Neural Architecture Search
The design of handcrafted neural networks requires a lot of time and res...
research
∙
07/13/2020
An Interpretable Baseline for Time Series Classification Without Intensive Learning
Recent advances in time series classification have largely focused on me...
research
∙
07/13/2020
Projected Latent Markov Chain Monte Carlo: Conditional Inference with Normalizing Flows
We introduce Projected Latent Markov Chain Monte Carlo (PL-MCMC), a tech...
research
∙
07/07/2020
Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery
In this paper, we study two important problems in the automated design o...
research
∙
10/21/2019
Perception-Distortion Trade-off with Restricted Boltzmann Machines
In this work, we introduce a new procedure for applying Restricted Boltz...
research
∙
06/07/2019
One-Shot Neural Architecture Search via Compressive Sensing
Neural architecture search (NAS), or automated design of neural network ...
research
∙
02/12/2019
Learning Generative Models of Structured Signals from Their Superposition Using GANs with Application to Denoising and Demixing
Recently, Generative Adversarial Networks (GANs) have emerged as a popul...
research
∙
12/08/2017
Fast Low-Rank Matrix Estimation without the Condition Number
In this paper, we study the general problem of optimizing a convex funct...
research
∙
09/29/2017
Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging
We consider the problem of reconstructing signals and images from period...
research
∙
08/08/2017
Demixing Structured Superposition Signals from Periodic and Aperiodic Nonlinear Observations
We consider the demixing problem of two (or more) structured high-dimens...
research
∙
06/27/2017
Fast Algorithms for Learning Latent Variables in Graphical Models
We study the problem of learning latent variables in Gaussian graphical ...
research
∙
05/21/2017
Improved Algorithms for Matrix Recovery from Rank-One Projections
We consider the problem of estimation of a low-rank matrix from a limite...
research
∙
01/23/2017
Stable Recovery Of Sparse Vectors From Random Sinusoidal Feature Maps
Random sinusoidal features are a popular approach for speeding up kernel...
research
∙
01/23/2017
Iterative Thresholding for Demixing Structured Superpositions in High Dimensions
We consider the demixing problem of two (or more) high-dimensional vecto...
research
∙
08/03/2016
