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07/24/2023
HiHGNN: Accelerating HGNNs through Parallelism and Data Reusability Exploitation
Heterogeneous graph neural networks (HGNNs) have emerged as powerful alg...
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07/15/2022
Multi-node Acceleration for Large-scale GCNs
Limited by the memory capacity and compute power, singe-node graph convo...
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02/23/2022
Alleviating Datapath Conflicts and Design Centralization in Graph Analytics Acceleration
Previous graph analytics accelerators have achieved great improvement on...
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02/09/2022
Model Architecture Adaption for Bayesian Neural Networks
Bayesian Neural Networks (BNNs) offer a mathematically grounded framewor...
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12/22/2021
Fine-grained Multi-Modal Self-Supervised Learning
Multi-Modal Self-Supervised Learning from videos has been shown to impro...
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11/02/2020
Pairwise Relations Discriminator for Unsupervised Raven's Progressive Matrices
Abstract reasoning is a key indicator of intelligence. The ability to hy...
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09/19/2020
Learned Low Precision Graph Neural Networks
Deep Graph Neural Networks (GNNs) show promising performance on a range ...
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08/04/2020
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
During the COVID-19 pandemic, rapid and accurate triage of patients at t...
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07/14/2020
Top-Related Meta-Learning Method for Few-Shot Detection
Many meta-learning methods which depend on a large amount of data and mo...
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06/19/2020
Abstract Diagrammatic Reasoning with Multiplex Graph Networks
Abstract reasoning, particularly in the visual domain, is a complex huma...
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06/15/2020
Generalisable Relational Reasoning With Comparators in Low-Dimensional Manifolds
While modern deep neural architectures generalise well when test data is...
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03/21/2020
Probabilistic Dual Network Architecture Search on Graphs
We present the first differentiable Network Architecture Search (NAS) fo...
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07/24/2019
Mixed-Supervised Dual-Network for Medical Image Segmentation
Deep learning-based medical image segmentation models usually require la...
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04/04/2019
A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning
Few-shot learning aims to learn classifiers for new classes with only a ...
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03/14/2019
Unsupervised and interpretable scene discovery with Discrete-Attend-Infer-Repeat
In this work we present Discrete Attend Infer Repeat (Discrete-AIR), a R...
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11/21/2018
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measuremen...
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10/23/2017
A Survey of Model Compression and Acceleration for Deep Neural Networks
Deep convolutional neural networks (CNNs) have recently achieved great s...
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10/01/2016