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08/28/2023
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
Deep Neural Networks (DNNs) have been a large driver and enabler for AI ...
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05/04/2023
Cuttlefish: Low-Rank Model Training without All the Tuning
Recent research has shown that training low-rank neural networks can eff...
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03/28/2023
Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification
Whole Slide Image (WSI) classification remains a challenge due to their ...
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03/08/2023
Memory-adaptive Depth-wise Heterogenous Federated Learning
Federated learning is a promising paradigm that allows multiple clients ...
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02/08/2023
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
The canonical formulation of federated learning treats it as a distribut...
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01/06/2023
Does compressing activations help model parallel training?
Large-scale Transformer models are known for their exceptional performan...
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10/26/2022
Super-Resolution Based Patch-Free 3D Medical Image Segmentation with Self-Supervised Guidance
High resolution (HR) 3D medical image segmentation plays an important ro...
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07/28/2022
CubeMLP: An MLP-based Model for Multimodal Sentiment Analysis and Depression Estimation
Multimodal sentiment analysis and depression estimation are two importan...
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03/17/2022
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation
Federated Learning (FL) on knowledge graphs (KGs) has yet to be as well ...
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02/24/2022
Rare Gems: Finding Lottery Tickets at Initialization
It has been widely observed that large neural networks can be pruned to ...
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12/13/2021
Hformer: Hybrid CNN-Transformer for Fringe Order Prediction in Phase Unwrapping of Fringe Projection
Recently, deep learning has attracted more and more attention in phase u...
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11/08/2021
Mixed Transformer U-Net For Medical Image Segmentation
Though U-Net has achieved tremendous success in medical image segmentati...
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10/04/2021
Solon: Communication-efficient Byzantine-resilient Distributed Training via Redundant Gradients
There has been a growing need to provide Byzantine-resilience in distrib...
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08/16/2021
Memtrade: A Disaggregated-Memory Marketplace for Public Clouds
We present Memtrade, the first memory disaggregation system for public c...
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03/05/2021
Pufferfish: Communication-efficient Models At No Extra Cost
To mitigate communication overheads in distributed model training, sever...
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02/28/2021
On the Utility of Gradient Compression in Distributed Training Systems
Rapid growth in data sets and the scale of neural network architectures ...
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02/25/2021
BPF for storage: an exokernel-inspired approach
The overhead of the kernel storage path accounts for half of the access ...
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10/29/2020
Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification
Distributed model training suffers from communication bottlenecks due to...
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07/09/2020
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
Due to its decentralized nature, Federated Learning (FL) lends itself to...
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02/15/2020
Federated Learning with Matched Averaging
Federated learning allows edge devices to collaboratively learn a shared...
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07/29/2019
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation
To improve the resilience of distributed training to worst-case, or Byza...
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01/28/2019
ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding
We present ErasureHead, a new approach for distributed gradient descent ...
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06/11/2018
ATOMO: Communication-efficient Learning via Atomic Sparsification
Distributed model training suffers from communication overheads due to f...
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06/11/2018