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06/16/2023
ZeRO++: Extremely Efficient Collective Communication for Giant Model Training
Zero Redundancy Optimizer (ZeRO) has been used to train a wide range of ...
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05/22/2023
Bridging the Gap Between End-to-end and Non-End-to-end Multi-Object Tracking
Existing end-to-end Multi-Object Tracking (e2e-MOT) methods have not sur...
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12/07/2022
Multiple Object Tracking Challenge Technical Report for Team MT_IoT
This is a brief technical report of our proposed method for Multiple-Obj...
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11/28/2022
PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud
The interaction and dimension of points are two important axes in design...
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09/03/2022
Sion: Elastic Serverless Cloud Storage
Cloud object storage such as AWS S3 is cost-effective and highly elastic...
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08/01/2022
AI Augmented Edge and Fog Computing: Trends and Challenges
In recent years, the landscape of computing paradigms has witnessed a gr...
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07/29/2022
BiFeat: Supercharge GNN Training via Graph Feature Quantization
Graph Neural Networks (GNNs) is a promising approach for applications wi...
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07/14/2022
NASRec: Weight Sharing Neural Architecture Search for Recommender Systems
The rise of deep neural networks provides an important driver in optimiz...
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05/04/2022
SMLT: A Serverless Framework for Scalable and Adaptive Machine Learning Design and Training
In today's production machine learning (ML) systems, models are continuo...
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05/04/2021
Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX
With the advancement of machine learning (ML) and its growing awareness,...
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02/27/2021
The Age of Correlated Features in Supervised Learning based Forecasting
In this paper, we analyze the impact of information freshness on supervi...
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02/01/2021
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning
Data heterogeneity has been identified as one of the key features in fed...
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11/30/2020
ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition
Scale variance among different sizes of body parts and objects is a chal...
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07/08/2020
NASGEM: Neural Architecture Search via Graph Embedding Method
Neural Architecture Search (NAS) automates and prospers the design of ne...
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04/20/2020
Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification
Modern deep neural networks (DNNs) often require high memory consumption...
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01/28/2020
InfiniCache: Exploiting Ephemeral Serverless Functions to Build a Cost-Effective Memory Cache
Internet-scale web applications are becoming increasingly storage-intens...
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01/25/2020
TiFL: A Tier-based Federated Learning System
Federated Learning (FL) enables learning a shared model across many clie...
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01/05/2020
Distributed Nonblocking Commit Protocols for Many-Party Cross-Blockchain Transactions
The interoperability across multiple blockchains would play a critical r...
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11/21/2019
AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture
Resource is an important constraint when deploying Deep Neural Networks ...
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07/07/2019
EPNAS: Efficient Progressive Neural Architecture Search
In this paper, we propose Efficient Progressive Neural Architecture Sear...
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06/19/2019
SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures
Designing neural architectures for edge devices is subject to constraint...
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06/07/2019
AutoGrow: Automatic Layer Growing in Deep Convolutional Networks
We propose AutoGrow to automate depth discovery in Deep Neural Networks ...
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05/06/2019
BlockLite: A Lightweight Emulator for Public Blockchains
Blockchain is an enabler of many emerging decentralized applications in ...
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11/27/2018
LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning
Distributed learning systems have enabled training large-scale models ov...
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10/20/2018
Differentiable Fine-grained Quantization for Deep Neural Network Compression
Neural networks have shown great performance in cognitive tasks. When de...
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05/21/2018
SmoothOut: Smoothing Out Sharp Minima for Generalization in Large-Batch Deep Learning
In distributed deep learning, a large batch size in Stochastic Gradient ...
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05/22/2017
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
High network communication cost for synchronizing gradients and paramete...
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02/14/2012
