Graph neural network training and data tiering
Graph Neural Networks (GNNs) have shown success in learning from graph-structured data,
with applications to fraud detection, recommendation, and knowledge graph reasoning …
with applications to fraud detection, recommendation, and knowledge graph reasoning …
Baechi: fast device placement of machine learning graphs
Machine Learning graphs (or models) can be challenging or impossible to train when either
devices have limited memory, or the models are large. Splitting the model graph across …
devices have limited memory, or the models are large. Splitting the model graph across …
CoProver: A recommender system for proof construction
Abstract Interactive Theorem Provers (ITPs) are an indispensable tool in the arsenal of
formal method experts as a platform for construction and (formal) verification of proofs. The …
formal method experts as a platform for construction and (formal) verification of proofs. The …
Multi-feature vision transformer via self-supervised representation learning for improvement of covid-19 diagnosis
X Qi, DJ Foran, JL Nosher, I Hacihaliloglu - Workshop on Medical Image …, 2022 - Springer
The role of chest X-ray (CXR) imaging, due to being more cost-effective, widely available,
and having a faster acquisition time compared to CT, has evolved during the COVID-19 …
and having a faster acquisition time compared to CT, has evolved during the COVID-19 …
SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores
The ever-growing complexity of reinforcement learning (RL) tasks demands a distributed
system to efficiently generate and process a massive amount of data. However, existing …
system to efficiently generate and process a massive amount of data. However, existing …
Communication-Efficient Distributed Deep Learning via Federated Dynamic Averaging
M Theologitis, G Frangias, G Anestis… - arXiv preprint arXiv …, 2024 - arxiv.org
Driven by the ever-growing volume and decentralized nature of data, coupled with the
escalating size of modern models, distributed deep learning (DDL) has been entrenched as …
escalating size of modern models, distributed deep learning (DDL) has been entrenched as …
DistSim: A performance model of large-scale hybrid distributed DNN training
With the ever-increasing computational demand of DNN training workloads, distributed
training has been widely adopted. A combination of data, model and pipeline parallelism …
training has been widely adopted. A combination of data, model and pipeline parallelism …
TAPS: Topology-Aware Intra-Operator Parallelism Strategy Searching Algorithm for Deep Neural Networks
TAPS is a Topology-Aware intra-operator Parallelism strategy Searching algorithm that
generates intra-operator parallelism strategies by considering both intra-node and inter …
generates intra-operator parallelism strategies by considering both intra-node and inter …