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01/26/2023
PerfSAGE: Generalized Inference Performance Predictor for Arbitrary Deep Learning Models on Edge Devices
The ability to accurately predict deep neural network (DNN) inference pe...
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08/17/2022
Restructurable Activation Networks
Is it possible to restructure the non-linear activation functions in a d...
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02/28/2022
Magnitude-aware Probabilistic Speaker Embeddings
Recently, hyperspherical embeddings have established themselves as a dom...
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01/15/2022
UDC: Unified DNAS for Compressible TinyML Models
Emerging Internet-of-things (IoT) applications are driving deployment of...
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10/21/2020
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers
Executing machine learning workloads locally on resource constrained mic...
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05/22/2020
MANGO: A Python Library for Parallel Hyperparameter Tuning
Tuning hyperparameters for machine learning algorithms is a tedious task...
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05/20/2020
TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids
Modern speech enhancement algorithms achieve remarkable noise suppressio...
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10/04/2019
Pushing the limits of RNN Compression
Recurrent Neural Networks (RNN) can be difficult to deploy on resource c...
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06/07/2019
Compressing RNNs for IoT devices by 15-38x using Kronecker Products
Recurrent Neural Networks (RNN) can be large and compute-intensive, maki...
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05/28/2019
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers
The vast majority of processors in the world are actually microcontrolle...
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04/10/2018
Multimodal Sparse Bayesian Dictionary Learning
The purpose of this paper is to address the problem of learning dictiona...
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02/05/2018
Re-Weighted Learning for Sparsifying Deep Neural Networks
This paper addresses the topic of sparsifying deep neural networks (DNN'...
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03/30/2017
Relevance Subject Machine: A Novel Person Re-identification Framework
We propose a novel method called the Relevance Subject Machine (RSM) to ...
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04/07/2016
A Unified Framework for Sparse Non-Negative Least Squares using Multiplicative Updates and the Non-Negative Matrix Factorization Problem
We study the sparse non-negative least squares (S-NNLS) problem. S-NNLS ...
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01/22/2016