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07/17/2023
Evaluating and Enhancing Robustness of Deep Recommendation Systems Against Hardware Errors
Deep recommendation systems (DRS) heavily depend on specialized HPC hard...
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04/21/2023
PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
It is widely acknowledged that large models have the potential to delive...
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12/20/2021
Fast and Green Computing with Graphics Processing Units for solving Sparse Linear Systems
In this paper, we aim to introduce a new perspective when comparing high...
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09/28/2021
Power Consumption Analysis of Parallel Algorithms on GPUs
Due to their highly parallel multi-cores architecture, GPUs are being in...
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04/14/2021
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition
We improve the scalability of Branch and Bound (BaB) algorithms for form...
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02/24/2020
Lagrangian Decomposition for Neural Network Verification
A fundamental component of neural network verification is the computatio...
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12/03/2019
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Deep learning frameworks have often focused on either usability or speed...
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05/23/2018
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials
Dense conditional random fields (CRFs) with Gaussian pairwise potentials...
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06/01/2017
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
Variational autoencoders (VAEs) learn representations of data by jointly...
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12/01/2016
Playing Doom with SLAM-Augmented Deep Reinforcement Learning
A number of recent approaches to policy learning in 2D game domains have...
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11/29/2016
Efficient Linear Programming for Dense CRFs
The fully connected conditional random field (CRF) with Gaussian pairwis...
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11/22/2016
Inducing Interpretable Representations with Variational Autoencoders
We develop a framework for incorporating structured graphical models in ...
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08/22/2016
