b'Pu Zhao'

research

∙ 08/18/2023

WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

Large language models (LLMs), such as GPT-4, have shown remarkable perfo...

0 Haipeng Luo, et al. ∙

research

∙ 08/03/2023

Diffusion-based Time Series Data Imputation for Microsoft 365

Reliability is extremely important for large-scale cloud systems like Mi...

0 Fangkai Yang, et al. ∙

research

∙ 08/01/2023

Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction

Learning from positive and unlabeled data is known as positive-unlabeled...

0 Zhangchi Zhu, et al. ∙

research

∙ 06/14/2023

WizardCoder: Empowering Code Large Language Models with Evol-Instruct

Code Large Language Models (Code LLMs), such as StarCoder, have demonstr...

0 Ziyang Luo, et al. ∙

research

∙ 05/29/2023

Assess and Summarize: Improve Outage Understanding with Large Language Models

Cloud systems have become increasingly popular in recent years due to th...

0 Pengxiang Jin, et al. ∙

research

∙ 05/19/2023

Introspective Tips: Large Language Model for In-Context Decision Making

The emergence of large language models (LLMs) has substantially influenc...

1 Liting Chen, et al. ∙

research

∙ 05/19/2023

Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

Large Language Model (LLM) has gained popularity and achieved remarkable...

0 Zezhong Wang, et al. ∙

research

∙ 05/08/2023

Augmented Large Language Models with Parametric Knowledge Guiding

Large Language Models (LLMs) have significantly advanced natural languag...

0 Ziyang Luo, et al. ∙

research

∙ 04/24/2023

WizardLM: Empowering Large Language Models to Follow Complex Instructions

Training large language models (LLM) with open-domain instruction follow...

0 Can Xu, et al. ∙

research

∙ 02/06/2023

LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Retrieval

Image-text retrieval (ITR) is a task to retrieve the relevant images/tex...

0 Ziyang Luo, et al. ∙

research

∙ 12/09/2022

All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management

During the deployment of deep neural networks (DNNs) on edge devices, ma...

0 Yifan Gong, et al. ∙

research

∙ 11/10/2022

MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation

Responding with multi-modal content has been recognized as an essential ...

0 Jiazhan Feng, et al. ∙

research

∙ 10/08/2022

Advancing Model Pruning via Bi-level Optimization

The deployment constraints in practical applications necessitate the pru...

9 Yihua Zhang, et al. ∙

research

∙ 06/02/2022

Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization

Neural architecture search (NAS) and network pruning are widely studied ...

0 Yanyu Li, et al. ∙

research

∙ 05/14/2021

High-Robustness, Low-Transferability Fingerprinting of Neural Networks

This paper proposes Characteristic Examples for effectively fingerprinti...

0 Siyue Wang, et al. ∙

research

∙ 09/29/2020

Learned Fine-Tuner for Incongruous Few-Shot Learning

Model-agnostic meta-learning (MAML) effectively meta-learns an initializ...

0 Pu Zhao, et al. ∙

research

∙ 04/30/2020

Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness

Mode connectivity provides novel geometric insights on analyzing loss la...

7 Pu Zhao, et al. ∙

research

∙ 04/22/2020

Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization

High-end mobile platforms rapidly serve as primary computing devices for...

0 Wei Niu, et al. ∙

research

∙ 03/24/2020

Robust Beamforming Design for Sum Secrecy Rate Optimization in MU-MISO Networks

This paper studies the beamforming design problem of a multi-user downli...

0 Pu Zhao, et al. ∙

research

∙ 03/13/2020

A Privacy-Preserving DNN Pruning and Mobile Acceleration Framework

To facilitate the deployment of deep neural networks (DNNs) on resource-...

0 Zheng Zhan, et al. ∙

research

∙ 02/26/2020

Defending against Backdoor Attack on Deep Neural Networks

Although deep neural networks (DNNs) have achieved a great success in va...

0 Hao Cheng, et al. ∙

research

∙ 02/18/2020

Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent

Despite the great achievements of the modern deep neural networks (DNNs)...

25 Pu Zhao, et al. ∙

research

∙ 01/23/2020

BLK-REW: A Unified Block-based DNN Pruning Framework using Reweighted Regularization Method

Accelerating DNN execution on various resource-limited computing platfor...

7 Xiaolong Ma, et al. ∙

research

∙ 07/26/2019

On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method

Robust machine learning is currently one of the most prominent topics wh...

6 Pu Zhao, et al. ∙

research

∙ 05/28/2019

Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks

Despite the great achievements of deep neural networks (DNNs), the vulne...

0 Pu Zhao, et al. ∙

research

∙ 04/03/2019

Interpreting Adversarial Examples by Activation Promotion and Suppression

It is widely known that convolutional neural networks (CNNs) are vulnera...

0 Kaidi Xu, et al. ∙

research

∙ 09/13/2018

Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks

Deep neural networks (DNNs) are known vulnerable to adversarial attacks....

0 Siyue Wang, et al. ∙

research

∙ 08/05/2018

Structured Adversarial Attack: Towards General Implementation and Better Interpretability

When generating adversarial examples to attack deep neural networks (DNN...

16 Kaidi Xu, et al. ∙

research

∙ 04/09/2018

An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks

Deep neural networks (DNNs) are known vulnerable to adversarial attacks....

0 Pu Zhao, et al. ∙

Pu Zhao

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