Search Results for author:
Found 47 papers, 18 papers with code
An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks
In the literature, the added distortions are usually measured by L0, L1, L2, and L infinity norms, namely, L0, L1, L2, and L infinity attacks, respectively.
Structured Adversarial Attack: Towards General Implementation and Better Interpretability
When generating adversarial examples to attack deep neural networks (DNNs), Lp norm of the added perturbation is usually used to measure the similarity between original image and adversarial example.
Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks
Based on the observations of the effect of test dropout rate on test accuracy and attack success rate, we propose a defensive dropout algorithm to determine an optimal test dropout rate given the neural network model and the attacker's strategy for generating adversarial examples. We also investigate the mechanism behind the outstanding defense effects achieved by the proposed defensive dropout.
Interpreting Adversarial Examples by Activation Promotion and Suppression
It is widely known that convolutional neural networks (CNNs) are vulnerable to adversarial examples: images with imperceptible perturbations crafted to fool classifiers.
Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks
Despite the great achievements of deep neural networks (DNNs), the vulnerability of state-of-the-art DNNs raises security concerns of DNNs in many application domains requiring high reliability. We propose the fault sneaking attack on DNNs, where the adversary aims to misclassify certain input images into any target labels by modifying the DNN parameters.
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 which could potentially help shaping a future of advanced AI platforms that not only perform well in average cases but also in worst cases or adverse situations.
BLK-REW: A Unified Block-based DNN Pruning Framework using Reweighted Regularization Method
Accelerating DNN execution on various resource-limited computing platforms has been a long-standing problem.
Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent
Despite the great achievements of the modern deep neural networks (DNNs), the vulnerability/robustness of state-of-the-art DNNs raises security concerns in many application domains requiring high reliability.
Defending against Backdoor Attack on Deep Neural Networks
Although deep neural networks (DNNs) have achieved a great success in various computer vision tasks, it is recently found that they are vulnerable to adversarial attacks.
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 a wide range of Deep Neural Network (DNN) applications.
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
In this work, we propose to employ mode connectivity in loss landscapes to study the adversarial robustness of deep neural networks, and provide novel methods for improving this robustness.
Real-Time Execution of Large-scale Language Models on Mobile
Our framework can guarantee the identified model to meet both resource and real-time specifications of mobile devices, thus achieving real-time execution of large transformer-based models like BERT variants.
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations
The resulting scheme for meta-learning a UAP generator (i) has better performance (50% higher ASR) than baselines such as Projected Gradient Descent, (ii) has better performance (37% faster) than the vanilla L2O and MAML frameworks (when applicable), and (iii) is able to simultaneously handle UAP generation for different victim models and image data sources.
NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration
With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed.
Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device
3D object detection is an important task, especially in the autonomous driving application domain.
High-Robustness, Low-Transferability Fingerprinting of Neural Networks
This paper proposes Characteristic Examples for effectively fingerprinting deep neural networks, featuring high-robustness to the base model against model pruning as well as low-transferability to unassociated models.
A Compression-Compilation Framework for On-mobile Real-time BERT Applications
In this paper, we propose a compression-compilation co-design framework that can guarantee the identified model to meet both resource and real-time specifications of mobile devices.
Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search
Object detection plays an important role in self-driving cars for security development.
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search
Though recent years have witnessed remarkable progress in single image super-resolution (SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep learning methods are confronted with the computation and memory consumption issues in practice, especially for resource-limited platforms such as mobile devices.
Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration
Weight pruning is an effective model compression technique to tackle the challenges of achieving real-time deep neural network (DNN) inference on mobile devices.
Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization
By combining the structural reparameterization and PaS, we successfully searched out a new family of VGG-like and lightweight networks, which enable the flexibility of arbitrary width with respect to each layer instead of each stage.
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution
Instead of measuring the speed on mobile devices at each iteration during the search process, a speed model incorporated with compiler optimizations is leveraged to predict the inference latency of the SR block with various width configurations for faster convergence.
Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed
Recently, Diffenderfer and Kailkhura proposed a new paradigm for learning compact yet highly accurate binary neural networks simply by pruning and quantizing randomly weighted full precision neural networks.
Advancing Model Pruning via Bi-level Optimization
To reduce the computation overhead, various efficient 'one-shot' pruning methods have been developed, but these schemes are usually unable to find winning tickets as good as IMP.
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation
First, it is the largest multi-modal conversation dataset by the number of dialogues by 88x.
Ranked #2 on
Multimodal Intent Recognition
on MMDialog
All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management
By re-configuring the model to the corresponding pruning ratio for a specific execution frequency (and voltage), we are able to achieve stable inference speed, i. e., keeping the difference in speed performance under various execution frequencies as small as possible.
Pruning Parameterization With Bi-Level Optimization for Efficient Semantic Segmentation on the Edge
With the ever-increasing popularity of edge devices, it is necessary to implement real-time segmentation on the edge for autonomous driving and many other applications.
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Sparse Retrieval
To address this issue, we propose a novel sparse retrieval paradigm for ITR that exploits sparse representations in the vocabulary space for images and texts.
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Retrieval
The conventional dense retrieval paradigm relies on encoding images and texts into dense representations using dual-stream encoders, however, it faces challenges with low retrieval speed in large-scale retrieval scenarios.
Less is More: Data Pruning for Faster Adversarial Training
Deep neural networks (DNNs) are sensitive to adversarial examples, resulting in fragile and unreliable performance in the real world.
WizardLM: Empowering Large Language Models to Follow Complex Instructions
In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans.
Augmented Large Language Models with Parametric Knowledge Guiding
We demonstrate that our PKG framework can enhance the performance of "black-box" LLMs on a range of domain knowledge-intensive tasks that require factual (+7. 9%), tabular (+11. 9%), medical (+3. 0%), and multimodal (+8. 1%) knowledge.
Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering
Large Language Model (LLM) has gained popularity and achieved remarkable results in open-domain tasks, but its performance in real industrial domain-specific scenarios is average due to its lack of specific domain knowledge.
Introspective Tips: Large Language Model for In-Context Decision Making
The emergence of large language models (LLMs) has substantially influenced natural language processing, demonstrating exceptional results across various tasks.
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Moreover, our model even outperforms the largest closed LLMs, Anthropic's Claude and Google's Bard, on HumanEval and HumanEval+.
Ranked #3 on
Code Generation
on CodeContests
(Test Set pass@1 metric)
Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction
To mitigate the impact of label uncertainty and improve the robustness of learning with positive and unlabeled data, we propose a new robust PU learning method with a training strategy motivated by the nature of human learning: easy cases should be learned first.
Diffusion-based Time Series Data Imputation for Microsoft 365
However, they suffer from poor data quality like data missing in model training and prediction, which limits the performance.
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
Through extensive experiments on two mathematical reasoning benchmarks, namely GSM8k and MATH, we reveal the extraordinary capabilities of our model.
Ranked #49 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Self-Guard: Empower the LLM to Safeguard Itself
Currently, there are two main approaches to address jailbreak attacks: safety training and safeguards.
TaskWeaver: A Code-First Agent Framework
TaskWeaver provides support for rich data structures, flexible plugin usage, and dynamic plugin selection, and leverages LLM coding capabilities for complex logic.
Why does Prediction Accuracy Decrease over Time? Uncertain Positive Learning for Cloud Failure Prediction
Using two real-world datasets of disk failure prediction and conducting node prediction experiments in Microsoft Azure, which is a top-tier cloud provider that serves millions of users, we demonstrate Uptake can significantly improve the failure prediction accuracy by 5% on average.
Contrastive Learning with Negative Sampling Correction
To correct the negative sampling bias, we propose a novel contrastive learning method named Positive-Unlabeled Contrastive Learning (PUCL).
Detection and Recovery Against Deep Neural Network Fault Injection Attacks Based on Contrastive Learning
Deep Neural Network (DNN) models when implemented on executing devices as the inference engines are susceptible to Fault Injection Attacks (FIAs) that manipulate model parameters to disrupt inference execution with disastrous performance.
EdgeQAT: Entropy and Distribution Guided Quantization-Aware Training for the Acceleration of Lightweight LLMs on the Edge
In this paper, we propose EdgeQAT, the Entropy and Distribution Guided QAT for the optimization of lightweight LLMs to achieve inference acceleration on Edge devices.
Nissist: An Incident Mitigation Copilot based on Troubleshooting Guides
Effective incident management is pivotal for the smooth operation of enterprises-level cloud services.
DiffClass: Diffusion-Based Class Incremental Learning
On top of that, Exemplar-free Class Incremental Learning is even more challenging due to forbidden access to previous task data.
InstructGIE: Towards Generalizable Image Editing
In response to this challenge, our study introduces a novel image editing framework with enhanced generalization robustness by boosting in-context learning capability and unifying language instruction.
