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06/09/2023
Efficient GNN Explanation via Learning Removal-based Attribution
As Graph Neural Networks (GNNs) have been widely used in real-world appl...
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05/17/2023
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt
Large Language Models (LLMs), armed with billions of parameters, exhibit...
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05/03/2023
Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models
Sharding a large machine learning model across multiple devices to balan...
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02/07/2023
Efficient XAI Techniques: A Taxonomic Survey
Recently, there has been a growing demand for the deployment of Explaina...
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01/02/2023
ChameleMon: Shifting Measurement Attention as Network State Changes
Flow-level network measurement is critical to many network applications....
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10/19/2022
RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations
The training of graph neural networks (GNNs) is extremely time consuming...
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10/14/2022
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
Large-scale graph training is a notoriously challenging problem for grap...
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08/30/2021
Adaptive Label Smoothing To Regularize Large-Scale Graph Training
Graph neural networks (GNNs), which learn the node representations by re...
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03/26/2021
DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization
Human-designed data augmentation strategies have been replaced by automa...
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10/25/2020
Towards Interaction Detection Using Topological Analysis on Neural Networks
Detecting statistical interactions between input features is a crucial a...
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06/26/2020
AutoRec: An Automated Recommender System
Realistic recommender systems are often required to adapt to ever-changi...
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06/15/2020
