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08/06/2023
Communication-Free Distributed GNN Training with Vertex Cut
Training Graph Neural Networks (GNNs) on real-world graphs consisting of...
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03/14/2023
AutoTransfer: AutoML with Knowledge Transfer – An Application to Graph Neural Networks
AutoML has demonstrated remarkable success in finding an effective neura...
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03/14/2023
Relational Multi-Task Learning: Modeling Relations between Data and Tasks
A key assumption in multi-task learning is that at the inference time th...
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10/26/2022
TuneUp: A Training Strategy for Improving Generalization of Graph Neural Networks
Despite many advances in Graph Neural Networks (GNNs), their training st...
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06/15/2022
Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator
Subsurface simulations use computational models to predict the flow of f...
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06/07/2022
Learning Backward Compatible Embeddings
Embeddings, low-dimensional vector representation of objects, are fundam...
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02/06/2021
Open-World Semi-Supervised Learning
Supervised and semi-supervised learning methods have been traditionally ...
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11/15/2020
Coresets for Robust Training of Neural Networks against Noisy Labels
Modern neural networks have the capacity to overfit noisy labels frequen...
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07/14/2020
Concept Learners for Generalizable Few-Shot Learning
Developing algorithms that are able to generalize to a novel task given ...
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06/29/2020
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization
Real-world large-scale datasets are heteroskedastic and imbalanced – lab...
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10/03/2019
Learning Temporal Action Proposals With Fewer Labels
Temporal action proposals are a common module in action detection pipeli...
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08/18/2019
Delving Deep Into Hybrid Annotations for 3D Human Recovery in the Wild
Though much progress has been achieved in single-image 3D human recovery...
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06/27/2019
Few-Shot Video Classification via Temporal Alignment
There is a growing interest in learning a model which could recognize no...
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06/18/2019
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
Deep learning algorithms can fare poorly when the training dataset suffe...
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05/11/2019
Disentangling Content and Style via Unsupervised Geometry Distillation
It is challenging to disentangle an object into two orthogonal spaces of...
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04/21/2019
TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation aims at learning a mapping betwe...
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11/01/2018
CariGANs: Unpaired Photo-to-Caricature Translation
Facial caricature is an art form of drawing faces in an exaggerated way ...
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09/10/2018
DNN Dataflow Choice Is Overrated
Many DNN accelerators have been proposed and built using different micro...
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03/02/2018
Pose-Robust Face Recognition via Deep Residual Equivariant Mapping
Face recognition achieves exceptional success thanks to the emergence of...
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07/13/2017
