b'Tian Guo'

research

∙ 07/17/2023

Toward Scalable and Controllable AR Experimentation

To understand how well a proposed augmented reality (AR) solution works,...

0 Ashkan Ganj, et al. ∙

research

∙ 07/09/2023

Carbon-Efficient Neural Architecture Search

This work presents a novel approach to neural architecture search (NAS) ...

0 Yiyang Zhao, et al. ∙

research

∙ 06/21/2023

Opportunities of Renewable Energy Powered DNN Inference

With the proliferation of the adoption of renewable energy in powering d...

0 Seyed Morteza Nabavinejad, et al. ∙

research

∙ 02/22/2023

Learning Mixture Structure on Multi-Source Time Series for Probabilistic Forecasting

In many data-driven applications, collecting data from different sources...

0 Tian Guo, et al. ∙

research

∙ 01/15/2023

LitAR: Visually Coherent Lighting for Mobile Augmented Reality

An accurate understanding of omnidirectional environment lighting is cru...

0 Yiqin Zhao, et al. ∙

research

∙ 01/15/2023

Multi-Camera Lighting Estimation for Photorealistic Front-Facing Mobile Augmented Reality

Lighting understanding plays an important role in virtual object composi...

0 Yiqin Zhao, et al. ∙

research

∙ 07/07/2022

Privacy-preserving Reflection Rendering for Augmented Reality

Many augmented reality (AR) applications rely on omnidirectional environ...

0 Yiqin Zhao, et al. ∙

research

∙ 04/28/2022

FuncPipe: A Pipelined Serverless Framework for Fast and Cost-efficient Training of Deep Learning Models

Training deep learning (DL) models has become a norm. With the emergence...

0 Yunzhuo Liu, et al. ∙

research

∙ 10/07/2021

Multi-objective Optimization by Learning Space Partitions

In contrast to single-objective optimization (SOO), multi-objective opti...

18 Yiyang Zhao, et al. ∙

research

∙ 08/19/2021

On the Future of Cloud Engineering

Ever since the commercial offerings of the Cloud started appearing in 20...

0 David Bermbach, et al. ∙

research

∙ 08/13/2021

Quantifying and Improving Performance of Distributed Deep Learning with Cloud Storage

Cloud computing provides a powerful yet low-cost environment for distrib...

0 Nicholas Krichevsky, et al. ∙

research

∙ 06/16/2021

Enabling Sustainable Clouds: The Case for Virtualizing the Energy System

Cloud platforms' growing energy demand and carbon emissions are raising ...

0 Noman Bashir, et al. ∙

research

∙ 05/30/2021

Xihe: A 3D Vision-based Lighting Estimation Framework for Mobile Augmented Reality

Omnidirectional lighting provides the foundation for achieving spatially...

0 Yiqin Zhao, et al. ∙

research

∙ 04/30/2021

Memory-Efficient Deep Learning Inference in Trusted Execution Environments

This study identifies and proposes techniques to alleviate two key bottl...

19 Jean-Baptiste Truong, et al. ∙

research

∙ 04/16/2021

Sync-Switch: Hybrid Parameter Synchronization for Distributed Deep Learning

Stochastic Gradient Descent (SGD) has become the de facto way to train d...

17 Shijian Li, et al. ∙

research

∙ 06/11/2020

Few-shot Neural Architecture Search

To improve the search efficiency for Neural Architecture Search (NAS), O...

0 Yiyang Zhao, et al. ∙

research

∙ 05/05/2020

ESG2Risk: A Deep Learning Framework from ESG News to Stock Volatility Prediction

Incorporating environmental, social, and governance (ESG) considerations...

0 Tian Guo, et al. ∙

research

∙ 04/07/2020

Characterizing and Modeling Distributed Training with Transient Cloud GPU Servers

Cloud GPU servers have become the de facto way for deep learning practit...

0 Shijian Li, et al. ∙

research

∙ 03/30/2020

PointAR: Efficient Lighting Estimation for Mobile Augmented Reality

We propose an efficient lighting estimation pipeline that is suitable to...

0 Yiqin Zhao, et al. ∙

research

∙ 02/16/2020

MDInference: Balancing Inference Accuracy and Latency for Mobile Applications

Deep Neural Networks (DNNs) are allowing mobile devices to incorporate a...

0 Samuel S. Ogden, et al. ∙

research

∙ 02/16/2020

MDInference: Balancing Inference Accuracy andLatency for Mobile Applications

Deep Neural Networks (DNNs) are allowing mobile devices to incorporate a...

0 Samuel S. Ogden, et al. ∙

research

∙ 12/05/2019

Perseus: Characterizing Performance and Cost of Multi-Tenant Serving for CNN Models

Deep learning models are increasingly used for end-user applications, su...

0 Matthew LeMay, et al. ∙

research

∙ 09/10/2019

Characterizing the Deep Neural Networks Inference Performance of Mobile Applications

Today's mobile applications are increasingly leveraging deep neural netw...

0 Samuel S. Ogden, et al. ∙

research

∙ 09/04/2019

ModiPick: SLA-aware Accuracy Optimization For Mobile Deep Inference

Mobile applications are increasingly leveraging complex deep learning mo...

0 Samuel S. Ogden, et al. ∙

research

∙ 08/28/2019

Confidential Deep Learning: Executing Proprietary Models on Untrusted Devices

Performing deep learning on end-user devices provides fast offline infer...

0 Peter M. VanNostrand, et al. ∙

research

∙ 07/03/2019

CloudCoaster: Transient-aware Bursty Datacenter Workload Scheduling

Today's clusters often have to divide resources among a diverse set of j...

0 Samuel S. Ogden, et al. ∙

research

∙ 05/28/2019

Exploring Interpretable LSTM Neural Networks over Multi-Variable Data

For recurrent neural networks trained on time series with target and exo...

0 Tian Guo, et al. ∙

research

∙ 05/24/2019

Statistical embedding for directed graphs

We propose a novel statistical node embedding of directed graphs, which ...

0 Thorben Funke, et al. ∙

research

∙ 03/27/2019

Sensing Social Media Signals for Cryptocurrency News

The ability to track and monitor relevant and important news in real-tim...

0 Johannes Beck, et al. ∙

research

∙ 02/28/2019

Speeding up Deep Learning with Transient Servers

Distributed training frameworks, like TensorFlow, have been proposed as ...

0 Shijian Li, et al. ∙

research

∙ 06/17/2018

Multi-variable LSTM neural network for autoregressive exogenous model

In this paper, we propose multi-variable LSTM capable of accurate foreca...

0 Tian Guo, et al. ∙

research

∙ 04/14/2018

An interpretable LSTM neural network for autoregressive exogenous model

In this paper, we propose an interpretable LSTM recurrent neural network...

0 Tian Guo, et al. ∙

research

∙ 02/12/2018

Predicting short-term Bitcoin price fluctuations from buy and sell orders

Bitcoin is the first decentralized digital cryptocurrency, which has sho...

0 Tian Guo, et al. ∙

research

∙ 07/14/2017

Cloud-based or On-device: An Empirical Study of Mobile Deep Inference

Modern mobile applications are benefiting significantly from the advance...

0 Tian Guo, et al. ∙

research

∙ 07/14/2017

Towards Efficient Deep Inference for Mobile Applications

Mobile applications are benefiting significantly from the advancement in...

0 Tian Guo, et al. ∙

Tian Guo

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