A new distributed framework for graph transformer training auto-selects parallel strategies and optimizes sparse operations to deliver up to 6x speedup on 8 GPUs and 78% memory reduction.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
verdicts
UNVERDICTED 4representative citing papers
A teacher-student model with co-saliency network and growing-probability occlusion simulator outperforms prior methods on four occluded person re-identification benchmarks.
A method for adjoint differentiation of stencil loops that preserves their structure and parallelizability via combined AD and loop transformations, released as the PerforAD tool with seismic and CFD test cases.
Generalized ML models trained on past sales data forecast demand for new fashion items from their attributes, with experiments across neural architectures and loss functions showing robust performance.
citing papers explorer
-
Scalable and Adaptive Parallel Training of Graph Transformer on Large Graphs
A new distributed framework for graph transformer training auto-selects parallel strategies and optimizes sparse operations to deliver up to 6x speedup on 8 GPUs and 78% memory reduction.
-
A Novel Teacher-Student Learning Framework For Occluded Person Re-Identification
A teacher-student model with co-saliency network and growing-probability occlusion simulator outperforms prior methods on four occluded person re-identification benchmarks.
-
Automatic Differentiation for Adjoint Stencil Loops
A method for adjoint differentiation of stencil loops that preserves their structure and parallelizability via combined AD and loop transformations, released as the PerforAD tool with seismic and CFD test cases.
-
Fashion Retail: Forecasting Demand for New Items
Generalized ML models trained on past sales data forecast demand for new fashion items from their attributes, with experiments across neural architectures and loss functions showing robust performance.