PipeWeave predicts GPU kernel performance with 6.1% average error and end-to-end inference with 8.5% error by feeding analytical pipeline features into ML, cutting prior method errors by 4-7x across 11 GPUs.
Attention is all you need,
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
A federated learning framework fuses solar irradiance and PV generation data via co-attention, uses prototype alignment for imbalance, and aggregates models across communities to detect generation fraud while preserving privacy.
Autoregressive transformer decoders suppress OFDM interference in FM radio signals to restore intelligible speech with low latency on GPUs like Jetson AGX Orin.
Empowered t-FCW graph representation provides a unified non-parametric and interpretable method for point cloud analysis with high efficiency on ModelNet40 classification.
Apertus, a 70B open multilingual foundation model, was pre-trained on the Alps supercomputer, with details on adapting HPC infrastructure into a resilient ML platform.
citing papers explorer
-
PipeWeave: Synergizing Analytical and Learning Models for Unified GPU Performance Prediction
PipeWeave predicts GPU kernel performance with 6.1% average error and end-to-end inference with 8.5% error by feeding analytical pipeline features into ML, cutting prior method errors by 4-7x across 11 GPUs.
-
Privacy-Preserving Generation Fraud Detection for Distributed Photovoltaic Systems: A Solar Irradiance-Fused Federated Learning Framework
A federated learning framework fuses solar irradiance and PV generation data via co-attention, uses prototype alignment for imbalance, and aggregates models across communities to detect generation fraud while preserving privacy.
-
Applied AI-Enhanced RF Interference Rejection
Autoregressive transformer decoders suppress OFDM interference in FM radio signals to restore intelligible speech with low latency on GPUs like Jetson AGX Orin.
-
A Unified Non-Parametric and Interpretable Point Cloud Analysis via t-FCW Graph Representation
Empowered t-FCW graph representation provides a unified non-parametric and interpretable method for point cloud analysis with high efficiency on ModelNet40 classification.
-
An Engineering Journey Training Large Language Models at Scale on Alps: The Apertus Experience
Apertus, a 70B open multilingual foundation model, was pre-trained on the Alps supercomputer, with details on adapting HPC infrastructure into a resilient ML platform.