Mohammadreza Soltani | DeepAI

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∙ 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...

0 Mohammadreza Soltani, et al. ∙

∙ 01/26/2022

Toward Data-Driven STAP Radar

Using an amalgamation of techniques from classical radar, computer visio...

0 Shyam Venkatasubramanian, et al. ∙

∙ 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...

0 Juncheng Dong, et al. ∙

∙ 10/05/2021

Task Affinity with Maximum Bipartite Matching in Few-Shot Learning

We propose an asymmetric affinity score for representing the complexity ...

0 Cat P. Le, et al. ∙

∙ 05/31/2021

Towards Explainable Convolutional Features for Music Audio Modeling

Audio signals are often represented as spectrograms and treated as 2D im...

32 Anna K. Yanchenko, et al. ∙

∙ 03/23/2021

Neural Architecture Search From Fréchet Task Distance

We formulate a Fréchet-type asymmetric distance between tasks based on F...

0 Cat P. Le, et al. ∙

∙ 02/27/2021

Neural Architecture Search From Task Similarity Measure

In this paper, we propose a neural architecture search framework based o...

0 Cat P. Le, et al. ∙

∙ 10/27/2020

Task-Aware Neural Architecture Search

The design of handcrafted neural networks requires a lot of time and res...

0 Cat P. Le, et al. ∙

∙ 07/13/2020

An Interpretable Baseline for Time Series Classification Without Intensive Learning

Recent advances in time series classification have largely focused on me...

0 Robert J. Ravier, et al. ∙

∙ 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...

0 Chris Cannella, et al. ∙

∙ 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...

40 Minsu Cho, et al. ∙

∙ 10/21/2019

Perception-Distortion Trade-off with Restricted Boltzmann Machines

In this work, we introduce a new procedure for applying Restricted Boltz...

0 Chris Cannella, et al. ∙

∙ 06/07/2019

One-Shot Neural Architecture Search via Compressive Sensing

Neural architecture search (NAS), or automated design of neural network ...

0 Minsu Cho, et al. ∙

∙ 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...

0 Mohammadreza Soltani, et al. ∙

∙ 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...

0 Mohammadreza Soltani, et al. ∙

∙ 09/29/2017

Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging

We consider the problem of reconstructing signals and images from period...

0 Mohammadreza Soltani, et al. ∙

∙ 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...

0 Mohammadreza Soltani, et al. ∙

∙ 06/27/2017

Fast Algorithms for Learning Latent Variables in Graphical Models

We study the problem of learning latent variables in Gaussian graphical ...

0 Mohammadreza Soltani, et al. ∙

∙ 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...

0 Mohammadreza Soltani, et al. ∙

∙ 01/23/2017

Stable Recovery Of Sparse Vectors From Random Sinusoidal Feature Maps

Random sinusoidal features are a popular approach for speeding up kernel...

0 Mohammadreza Soltani, et al. ∙

∙ 01/23/2017

Iterative Thresholding for Demixing Structured Superpositions in High Dimensions

We consider the demixing problem of two (or more) high-dimensional vecto...

0 Mohammadreza Soltani, et al. ∙

∙ 08/03/2016

Fast Algorithms for Demixing Sparse Signals from Nonlinear Observations

We study the problem of demixing a pair of sparse signals from noisy, no...

0 Mohammadreza Soltani, et al. ∙

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