521 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Rutgers, The State University of New JerseyUS164 papers
- Fermi National Accelerator LaboratoryUS45 papers
- California Institute of TechnologyUS44 papers
- Carnegie Mellon UniversityUS37 papers
- University of California, Santa BarbaraUS37 papers
- University of ChicagoUS37 papers
- University of Minnesota SystemUS36 papers
- Centro de Investigaciones Energéticas, Medioambientales y TecnológicasES35 papers
- Unidades Centrales Científico-TécnicasES35 papers
- University of California, RiversideUS35 papers
- University of California, DavisUS34 papers
- Florida Department of StateUS33 papers
9 papers · 1 filter
HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks
Jinqi Xiao, Chengming Zhang, Yu Gong +5
Low-rank compression is an important model compression strategy for obtaining compact neural network models. In general, because the rank values directly determine the model comple…
A hybrid model-based and learning-based approach for classification using limited number of training samples
Alireza Nooraiepour, Waheed U. Bajwa, Narayan B. Mandayam
The fundamental task of classification given a limited number of training data samples is considered for physical systems with known parametric statistical models. The standalone l…
Differentially Private Accelerated Optimization Algorithms
Nurdan Kuru, Ş. İlker Birbil, Mert Gurbuzbalaban +1
We present two classes of differentially private optimization algorithms derived from the well-known accelerated first-order methods. The first algorithm is inspired by Polyak's he…
Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations
Geoffrey Fox, Shantenu Jha
We present a taxonomy of research on Machine Learning (ML) applied to enhance simulations together with a catalog of some activities. We cover eight patterns for the link of ML to…
Kernel-Based Training of Generative Networks
Kalliopi Basioti, George V. Moustakides, Emmanouil Z. Psarakis
Generative adversarial networks (GANs) are designed with the help of min-max optimization problems that are solved with stochastic gradient-type algorithms which are known to be no…
Dropout is a special case of the stochastic delta rule: faster and more accurate deep learning
Noah Frazier-Logue, Stephen José Hanson
Multi-layer neural networks have lead to remarkable performance on many kinds of benchmark tasks in text, speech and image processing. Nonlinear parameter estimation in hierarchica…
Task-Relevant Object Discovery and Categorization for Playing First-person Shooter Games
Junchi Liang, Abdeslam Boularias
We consider the problem of learning to play first-person shooter (FPS) video games using raw screen images as observations and keyboard inputs as actions. The high-dimensionality o…
High-dimensional Joint Sparsity Random Effects Model for Multi-task Learning
Krishnakumar Balasubramanian, Kai Yu, Tong Zhang
Joint sparsity regularization in multi-task learning has attracted much attention in recent years. The traditional convex formulation employs the group Lasso relaxation to achieve…
Sparse Online Learning via Truncated Gradient
John Langford, Lihong Li, Tong Zhang
We propose a general method called truncated gradient to induce sparsity in the weights of online learning algorithms with convex loss functions. This method has several essential…