16.2k citations · 16.4k across the 15 of their papers we have counts for
16 papers
Evaluating and Enhancing Robustness of Deep Recommendation Systems Against Hardware Errors
Dongning Ma, Xun Jiao, Fred Lin +5
Deep recommendation systems (DRS) heavily depend on specialized HPC hardware and accelerators to optimize energy, efficiency, and recommendation quality. Despite the growing number…
PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
Yanli Zhao, Andrew Gu, Rohan Varma +15
It is widely acknowledged that large models have the potential to deliver superior performance across a broad range of domains. Despite the remarkable progress made in the field of…
Fast and Green Computing with Graphics Processing Units for solving Sparse Linear Systems
Abal-Kassim Cheik Ahamed, Alban Desmaison, Frederic Magoules
In this paper, we aim to introduce a new perspective when comparing highly parallelized algorithms on GPU: the energy consumption of the GPU. We give an analysis of the performance…
Power Consumption Analysis of Parallel Algorithms on GPUs
Frédéric Magoulès, Abal-Kassim Cheik Ahamed, Alban Desmaison +4
Due to their highly parallel multi-cores architecture, GPUs are being increasingly used in a wide range of computationally intensive applications. Compared to CPUs, GPUs can achiev…
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition
Alessandro De Palma, Rudy Bunel, Alban Desmaison +4
We improve the scalability of Branch and Bound (BaB) algorithms for formally proving input-output properties of neural networks. First, we propose novel bounding algorithms based o…
Lagrangian Decomposition for Neural Network Verification
Rudy Bunel, Alessandro De Palma, Alban Desmaison +4
A fundamental component of neural network verification is the computation of bounds on the values their outputs can take. Previous methods have either used off-the-shelf solvers, d…
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa +18
Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible…
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials
Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan +5
Dense conditional random fields (CRFs) have become a popular framework for modelling several problems in computer vision such as stereo correspondence and multi-class semantic segm…
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
N. Siddharth, Brooks Paige, Jan-Willem van de Meent +5
Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the dat…
Learning to superoptimize programs - Workshop Version
Rudy Bunel, Alban Desmaison, M. Pawan Kumar +2
Superoptimization requires the estimation of the best program for a given computational task. In order to deal with large programs, superoptimization techniques perform a stochasti…
Playing Doom with SLAM-Augmented Deep Reinforcement Learning
Shehroze Bhatti, Alban Desmaison, Ondrej Miksik +3
A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions. However when employed in complex 3D enviro…
Inducing Interpretable Representations with Variational Autoencoders
N. Siddharth, Brooks Paige, Alban Desmaison +5
We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representation…
Efficient Linear Programming for Dense CRFs
Thalaiyasingam Ajanthan, Alban Desmaison, Rudy Bunel +3
The fully connected conditional random field (CRF) with Gaussian pairwise potentials has proven popular and effective for multi-class semantic segmentation. While the energy of a d…
Learning to superoptimize programs
Rudy Bunel, Alban Desmaison, M. Pawan Kumar +2
Code super-optimization is the task of transforming any given program to a more efficient version while preserving its input-output behaviour. In some sense, it is similar to the p…
Efficient Continuous Relaxations for Dense CRF
Alban Desmaison, Rudy Bunel, Pushmeet Kohli +2
Dense conditional random fields (CRF) with Gaussian pairwise potentials have emerged as a popular framework for several computer vision applications such as stereo correspondence a…
Adaptive Neural Compilation
Rudy Bunel, Alban Desmaison, Pushmeet Kohli +2
This paper proposes an adaptive neural-compilation framework to address the problem of efficient program learning. Traditional code optimisation strategies used in compilers are ba…