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05/20/2023
Dynamic Gradient Balancing for Enhanced Adversarial Attacks on Multi-Task Models
Multi-task learning (MTL) creates a single machine learning model called...
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04/13/2023
Structured Pruning for Multi-Task Deep Neural Networks
Although multi-task deep neural network (DNN) models have computation an...
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02/01/2023
Automatically Marginalized MCMC in Probabilistic Programming
Hamiltonian Monte Carlo (HMC) is a powerful algorithm to sample latent v...
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11/28/2022
Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing
Personalization in Federated Learning (FL) aims to modify a collaborativ...
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10/08/2022
Break the Wall Between Homophily and Heterophily for Graph Representation Learning
Homophily and heterophily are intrinsic properties of graphs that descri...
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05/25/2022
Improving Subgraph Representation Learning via Multi-View Augmentation
Subgraph representation learning based on Graph Neural Network (GNN) has...
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03/10/2022
A Tree-Structured Multi-Task Model Recommender
Tree-structured multi-task architectures have been employed to jointly t...
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11/19/2021
Toward Compact Parameter Representations for Architecture-Agnostic Neural Network Compression
This paper investigates deep neural network (DNN) compression from the p...
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11/18/2021
COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression
Training wide and deep neural networks (DNNs) require large amounts of s...
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10/25/2021
AutoMTL: A Programming Framework for Automated Multi-Task Learning
Multi-task learning (MTL) jointly learns a set of tasks. It is a promisi...
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07/23/2021
Rethinking Hard-Parameter Sharing in Multi-Task Learning
Hard parameter sharing in multi-task learning (MTL) allows tasks to shar...
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05/05/2021
Scalable Graph Neural Network Training: The Case for Sampling
Graph Neural Networks (GNNs) are a new and increasingly popular family o...
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02/10/2021
NumaPerf: Predictive and Full NUMA Profiling
Parallel applications are extremely challenging to achieve the optimal p...
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12/16/2020
SID-NISM: A Self-supervised Low-light Image Enhancement Framework
When capturing images in low-light conditions, the images often suffer f...
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11/05/2019
Post-Training 4-bit Quantization on Embedding Tables
Continuous representations have been widely adopted in recommender syste...
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10/31/2019
In-Place Zero-Space Memory Protection for CNN
Convolutional Neural Networks (CNN) are being actively explored for safe...
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01/18/2017