Search Results for author:
Found 32 papers, 13 papers with code
TroubleLLM: Align to Red Team Expert
Not only are these methods labor-intensive and require large budget costs, but the controllability of test prompt generation is lacking for the specific testing domain of LLM applications.
Explainable Text Classification Techniques in Legal Document Review: Locating Rationales without Using Human Annotated Training Text Snippets
While interesting, manually annotating training text snippets is not generally practical during a legal document review.
Structure Invariant Transformation for better Adversarial Transferability
In this work, we find that the existing input transformation based attacks transform the input image globally, resulting in limited diversity of the transformed images.
A Multi-scale Generalized Shrinkage Threshold Network for Image Blind Deblurring in Remote Sensing
Furthermore, we propose a deep proximal mapping module in the image domain, which combines a generalized shrinkage threshold with a multi-scale prior feature extraction block.
PRISTA-Net: Deep Iterative Shrinkage Thresholding Network for Coded Diffraction Patterns Phase Retrieval
This network utilizes a learnable nonlinear transformation to address the proximal-point mapping sub-problem associated with the sparse priors, and an attention mechanism to focus on phase information containing image edges, textures, and structures.
Physics-Informed DeepMRI: Bridging the Gap from Heat Diffusion to k-Space Interpolation
In order to enhance interpretability and overcome the acceleration limitations, this paper introduces an interpretable framework that unifies both $k$-space interpolation techniques and image-domain methods, grounded in the physical principles of heat diffusion equations.
Backpropagation Path Search On Adversarial Transferability
Transfer-based attackers craft adversarial examples against surrogate models and transfer them to victim models deployed in the black-box situation.
Nest-DGIL: Nesterov-optimized Deep Geometric Incremental Learning for CS Image Reconstruction
In the image restoration step, a cascade geometric incremental learning module is designed to compensate for missing texture information from different geometric spectral decomposition domains.
A Bi-variant Variational Model for Diffeomorphic Image Registration with Relaxed Jacobian Determinant Constraints
In this paper, we propose a new bi-variant diffeomorphic image registration model that introduces a soft constraint on the Jacobian equation $\det(\nabla\bm{\varphi}(\bm{x})) = f(\bm{x}) > 0$.
Performance Issue Identification in Cloud Systems with Relational-Temporal Anomaly Detection
Performance issues permeate large-scale cloud service systems, which can lead to huge revenue losses.
On the Robustness of Latent Diffusion Models
Therefore, in this paper, we aim to analyze the robustness of latent diffusion models more thoroughly.
DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities
Vulnerability detection is a critical problem in software security and attracts growing attention both from academia and industry.
Validating Multimedia Content Moderation Software via Semantic Fusion
To this end, content moderation software has been widely deployed on these platforms to detect and blocks toxic content.
Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization
However, the variance of the back-propagated gradients in intermediate blocks of ViTs may still be large, which may make the generated adversarial samples focus on some model-specific features and get stuck in poor local optima.
Improving the Transferability of Adversarial Samples by Path-Augmented Method
However, such methods selected the image augmentation path heuristically and may augment images that are semantics-inconsistent with the target images, which harms the transferability of the generated adversarial samples.
MTTM: Metamorphic Testing for Textual Content Moderation Software
In addition, we leverage the test cases generated by MTTM to retrain the model we explored, which largely improves model robustness (0% to 5. 9% EFR) while maintaining the accuracy on the original test set.
FAS-UNet: A Novel FAS-driven Unet to Learn Variational Image Segmentation
Based on the variational theory and FAS algorithm, we first design a feature extraction sub-network (FAS-Solution module) to solve the model-driven nonlinear systems, where a skip-connection is employed to fuse the multi-scale features.
An Interpretable MRI Reconstruction Network with Two-grid-cycle Correction and Geometric Prior Distillation
Although existing deep learning compressed-sensing-based Magnetic Resonance Imaging (CS-MRI) methods have achieved considerably impressive performance, explainability and generalizability continue to be challenging for such methods since the transition from mathematical analysis to network design not always natural enough, often most of them are not flexible enough to handle multi-sampling-ratio reconstruction assignments.
AEON: A Method for Automatic Evaluation of NLP Test Cases
However, in practice, many of the generated test cases fail to preserve similar semantic meaning and are unnatural (e. g., grammar errors), which leads to a high false alarm rate and unnatural test cases.
Improving Adversarial Transferability via Neuron Attribution-Based Attacks
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples.
A unifying framework for $n$-dimensional quasi-conformal mappings
With the advancement of computer technology, there is a surge of interest in effective mapping methods for objects in higher-dimensional spaces.
Deep Geometric Distillation Network for Compressive Sensing MRI
Compressed sensing (CS) is an efficient method to reconstruct MR image from small sampled data in $k$-space and accelerate the acquisition of MRI.
A Joint Intensity-Neuromorphic Event Imaging System for Resource Constrained Devices
The detection and tracking of objects in the scene are done on the distorted data at the host.
CNN Application in Detection of Privileged Documents in Legal Document Review
As data volumes increase, legal counsel normally employs methods to reduce the number of documents requiring review while balancing the need to ensure the protection of privileged information.
Empirical Comparisons of CNN with Other Learning Algorithms for Text Classification in Legal Document Review
For each data set, classification models were trained with different training sample sizes using different learning algorithms.
A Framework for Explainable Text Classification in Legal Document Review
This paper describes a framework for explainable text classification as a valuable tool in legal services: for enhancing the quality and efficiency of legal document review and for assisting in locating responsive snippets within responsive documents.
Evaluation of Seed Set Selection Approaches and Active Learning Strategies in Predictive Coding
In this study, we use extensive experimentation to examine the impact of popular seed set selection strategies in active learning, within a predictive coding exercise, and evaluate different active learning strategies against well-researched continuous active learning strategies for the purpose of determining efficient training methods for classifying large populations quickly and precisely.
An Empirical Study of the Application of Machine Learning and Keyword Terms Methodologies to Privilege-Document Review Projects in Legal Matters
Overly-inclusive keyword searching can also be problematic, because even while it drives up costs, it also can cast `too far of a net' and thus produce unreliable results. To overcome these weaknesses of keyword searching, legal teams are using a new method to target privileged information called predictive modeling.
Explainable Text Classification in Legal Document Review A Case Study of Explainable Predictive Coding
In these scenarios, if predictive coding can be used to locate these responsive snippets, then attorneys could easily evaluate the model's document classification decision.
Empirical Evaluations of Active Learning Strategies in Legal Document Review
One type of machine learning, text classification, is now regularly applied in the legal matters involving voluminous document populations because it can reduce the time and expense associated with the review of those documents.
Empirical Evaluations of Preprocessing Parameters' Impact on Predictive Coding's Effectiveness
Predictive coding, once used in only a small fraction of legal and business matters, is now widely deployed to quickly cull through increasingly vast amounts of data and reduce the need for costly and inefficient human document review.
A Total Fractional-Order Variation Model for Image Restoration with Non-homogeneous Boundary Conditions and its Numerical Solution
In this paper we analyze and test a fractional-order derivative based total $\alpha$-order variation model, which can outperform the currently popular high order regularization models.
