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...
share
research
∙
08/03/2023
Diffusion-based Time Series Data Imputation for Microsoft 365
Reliability is extremely important for large-scale cloud systems like Mi...
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...
research
∙
06/14/2023
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Code Large Language Models (Code LLMs), such as StarCoder, have demonstr...
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...
research
∙
05/19/2023
Introspective Tips: Large Language Model for In-Context Decision Making
The emergence of large language models (LLMs) has substantially influenc...
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...
research
∙
05/08/2023
Augmented Large Language Models with Parametric Knowledge Guiding
Large Language Models (LLMs) have significantly advanced natural languag...
research
∙
04/24/2023
WizardLM: Empowering Large Language Models to Follow Complex Instructions
Training large language models (LLM) with open-domain instruction follow...
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...
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...
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 ...
research
∙
10/08/2022
Advancing Model Pruning via Bi-level Optimization
The deployment constraints in practical applications necessitate the pru...
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 ...
research
∙
05/14/2021
High-Robustness, Low-Transferability Fingerprinting of Neural Networks
This paper proposes Characteristic Examples for effectively fingerprinti...
research
∙
09/29/2020
Learned Fine-Tuner for Incongruous Few-Shot Learning
Model-agnostic meta-learning (MAML) effectively meta-learns an initializ...
research
∙
04/30/2020
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
Mode connectivity provides novel geometric insights on analyzing loss la...
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...
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...
research
∙
03/13/2020
A Privacy-Preserving DNN Pruning and Mobile Acceleration Framework
To facilitate the deployment of deep neural networks (DNNs) on resource-...
research
∙
02/26/2020
Defending against Backdoor Attack on Deep Neural Networks
Although deep neural networks (DNNs) have achieved a great success in va...
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)...
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...
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...
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...
research
∙
04/03/2019
Interpreting Adversarial Examples by Activation Promotion and Suppression
It is widely known that convolutional neural networks (CNNs) are vulnera...
research
∙
09/13/2018
Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks
Deep neural networks (DNNs) are known vulnerable to adversarial attacks....
research
∙
08/05/2018
Structured Adversarial Attack: Towards General Implementation and Better Interpretability
When generating adversarial examples to attack deep neural networks (DNN...
research
∙
04/09/2018
