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6 papers · 1 filter
Mosaic: Composite Projection Pruning for Resource-efficient LLMs
Bailey J. Eccles, Leon Wong, Blesson Varghese
Extensive compute and memory requirements limit the deployment of large language models (LLMs) on any hardware. Compression methods, such as pruning, can reduce model size, which i…
Rapid Deployment of DNNs for Edge Computing via Structured Pruning at Initialization
Bailey J. Eccles, Leon Wong, Blesson Varghese
Edge machine learning (ML) enables localized processing of data on devices and is underpinned by deep neural networks (DNNs). However, DNNs cannot be easily run on devices due to t…
DNNShifter: An Efficient DNN Pruning System for Edge Computing
Bailey J. Eccles, Philip Rodgers, Peter Kilpatrick +2
Deep neural networks (DNNs) underpin many machine learning applications. Production quality DNN models achieve high inference accuracy by training millions of DNN parameters which…
PiRank: Scalable Learning To Rank via Differentiable Sorting
Robin Swezey, Aditya Grover, Bruno Charron +1
A key challenge with machine learning approaches for ranking is the gap between the performance metrics of interest and the surrogate loss functions that can be optimized with grad…
Learning Classifiers on Positive and Unlabeled Data with Policy Gradient
Tianyu Li, Chien-Chih Wang, Yukun Ma +4
Existing algorithms aiming to learn a binary classifier from positive (P) and unlabeled (U) data generally require estimating the class prior or label noises ahead of building a cl…
A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation
Ramin Raziperchikolaei, Harish S. Bhat
We propose and analyze a block coordinate descent proximal algorithm (BCD-prox) for simultaneous filtering and parameter estimation of ODE models. As we show on ODE systems with up…