Andrew Anderson | DeepAI

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∙ 05/04/2022

Domino Saliency Metrics: Improving Existing Channel Saliency Metrics with Structural Information

Channel pruning is used to reduce the number of weights in a Convolution...

0 Kaveena Persand, et al. ∙

∙ 01/25/2022

Winograd Convolution for Deep Neural Networks: Efficient Point Selection

Convolutional neural networks (CNNs) have dramatically improved the accu...

0 Syed Asad Alam, et al. ∙

∙ 08/02/2021

Understanding Diversity in Human-AI Data: What Cognitive Style Disaggregation Reveals

Artificial Intelligence (AI) is becoming more pervasive through all leve...

0 Andrew Anderson, et al. ∙

∙ 05/21/2020

TASO: Time and Space Optimization for Memory-Constrained DNN Inference

Convolutional neural networks (CNNs) are used in many embedded applicati...

10 Yuan Wen, et al. ∙

∙ 04/03/2020

Composition of Saliency Metrics for Channel Pruning with a Myopic Oracle

The computation and memory needed for Convolutional Neural Network (CNN)...

0 Kaveena Persand, et al. ∙

∙ 01/09/2020

Performance-Oriented Neural Architecture Search

Hardware-Software Co-Design is a highly successful strategy for improvin...

0 Andrew Anderson, et al. ∙

∙ 06/11/2019

A Taxonomy of Channel Pruning Signals in CNNs

Convolutional neural networks (CNNs) are widely used for classification ...

0 Kaveena Persand, et al. ∙

∙ 03/22/2019

Explaining Reinforcement Learning to Mere Mortals: An Empirical Study

We present a user study to investigate the impact of explanations on non...

0 Andrew Anderson, et al. ∙

∙ 09/27/2018

Scalar Arithmetic Multiple Data: Customizable Precision for Deep Neural Networks

Quantization of weights and activations in Deep Neural Networks (DNNs) i...

0 Andrew Anderson, et al. ∙

∙ 03/29/2018

Error Analysis and Improving the Accuracy of Winograd Convolution for Deep Neural Networks

Modern deep neural networks (DNNs) spend a large amount of their executi...

0 Barbara Barabasz, et al. ∙

∙ 03/29/2018

Improving accuracy of Winograd convolution for DNNs

Modern deep neural networks (DNNs) spend a large amount of their executi...

0 Barbara Barabasz, et al. ∙

∙ 11/21/2017

Toward Foraging for Understanding of StarCraft Agents: An Empirical Study

Assessing and understanding intelligent agents is a difficult task for u...

0 Sean Penney, et al. ∙

∙ 11/19/2017

How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Games

How should an AI-based explanation system explain an agent's complex beh...

0 Jonathan Dodge, et al. ∙

∙ 10/03/2017

Optimal DNN Primitive Selection with Partitioned Boolean Quadratic Programming

Deep Neural Networks (DNNs) require very large amounts of computation bo...

0 Andrew Anderson, et al. ∙

∙ 09/08/2017

Low-memory GEMM-based convolution algorithms for deep neural networks

Deep neural networks (DNNs) require very large amounts of computation bo...

0 Andrew Anderson, et al. ∙

∙ 04/06/2017

Parallel Multi Channel Convolution using General Matrix Multiplication

Convolutional neural networks (CNNs) have emerged as one of the most suc...

0 Aravind Vasudevan, et al. ∙

∙ 01/26/2016

Vectorization of Multibyte Floating Point Data Formats

We propose a scheme for reduced-precision representation of floating poi...

0 Andrew Anderson, et al. ∙

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