Yanzhao Wu | DeepAI

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∙ 09/16/2023

Rethinking Learning Rate Tuning in the Era of Large Language Models

Large Language Models (LLMs) represent the recent success of deep learni...

0 Hongpeng Jin, et al. ∙

∙ 09/06/2023

Fast and Resource-Efficient Object Tracking on Edge Devices: A Measurement Study

Object tracking is an important functionality of edge video analytic sys...

0 Sanjana Vijay Ganesh, et al. ∙

∙ 05/10/2023

Securing Distributed SGD against Gradient Leakage Threats

This paper presents a holistic approach to gradient leakage resilient di...

0 Wenqi Wei, et al. ∙

∙ 03/21/2023

STDLens: Model Hijacking-Resilient Federated Learning for Object Detection

Federated Learning (FL) has been gaining popularity as a collaborative l...

0 Ka-Ho Chow, et al. ∙

∙ 01/15/2023

EENet: Learning to Early Exit for Adaptive Inference

Budgeted adaptive inference with early exits is an emerging technique to...

0 Fatih Ilhan, et al. ∙

∙ 10/24/2022

Selecting and Composing Learning Rate Policies for Deep Neural Networks

The choice of learning rate (LR) functions and policies has evolved from...

0 Yanzhao Wu, et al. ∙

∙ 10/22/2021

Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering

This paper introduces a two-phase deep feature engineering framework for...

10 Zhongwei Xie, et al. ∙

∙ 08/02/2021

Learning TFIDF Enhanced Joint Embedding for Recipe-Image Cross-Modal Retrieval Service

It is widely acknowledged that learning joint embeddings of recipes with...

8 Zhongwei Xie, et al. ∙

∙ 07/27/2021

Parallel Detection for Efficient Video Analytics at the Edge

Deep Neural Network (DNN) trained object detectors are widely deployed i...

0 Yanzhao Wu, et al. ∙

∙ 07/02/2021

Gradient-Leakage Resilient Federated Learning

Federated learning(FL) is an emerging distributed learning paradigm with...

0 Wenqi Wei, et al. ∙

∙ 04/25/2021

RDMAbox : Optimizing RDMA for Memory Intensive Workloads

We present RDMAbox, a set of low level RDMA opti-mizations that provide ...

0 Juhyun Bae, et al. ∙

∙ 10/20/2020

Promoting High Diversity Ensemble Learning with EnsembleBench

Ensemble learning is gaining renewed interests in recent years. This pap...

0 Yanzhao Wu, et al. ∙

∙ 08/03/2020

Efficient Orchestration of Host and Remote Shared Memory for Memory Intensive Workloads

Since very few contributions to the development of an unified memory orc...

0 Juhyun Bae, et al. ∙

∙ 07/11/2020

Understanding Object Detection Through An Adversarial Lens

Deep neural networks based object detection models have revolutionized c...

12 Ka-Ho Chow, et al. ∙

∙ 04/22/2020

A Framework for Evaluating Gradient Leakage Attacks in Federated Learning

Federated learning (FL) is an emerging distributed machine learning fram...

5 Wenqi Wei, et al. ∙

∙ 04/09/2020

TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems

The rapid growth of real-time huge data capturing has pushed the deep le...

3 Ka-Ho Chow, et al. ∙

∙ 10/01/2019

Cross-Layer Strategic Ensemble Defense Against Adversarial Examples

Deep neural network (DNN) has demonstrated its success in multiple domai...

9 Wenqi Wei, et al. ∙

∙ 08/29/2019

Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness

Ensemble learning is a methodology that integrates multiple DNN learners...

0 Ling Liu, et al. ∙

∙ 08/21/2019

Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks

Deep neural networks (DNNs) have demonstrated impressive performance on ...

0 Ka-Ho Chow, et al. ∙

∙ 08/18/2019

Demystifying Learning Rate Polices for High Accuracy Training of Deep Neural Networks

Learning Rate (LR) is an important hyper-parameter to tune for effective...

0 Yanzhao Wu, et al. ∙

∙ 10/29/2018

A Comparative Measurement Study of Deep Learning as a Service Framework

Big data powered Deep Learning (DL) and its applications have blossomed ...

0 Yanzhao Wu, et al. ∙

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