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Found 14 papers, 3 papers with code
Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning
The VEM attack focuses on identifying and perturbing the most vulnerable edges in each layer and leveraging an optimization-based approach to maximize the attack's impact.
DuoLift-GAN:Reconstructing CT from Single-view and Biplanar X-Rays with Generative Adversarial Networks
Computed tomography (CT) provides highly detailed three-dimensional (3D) medical images but is costly, time-consuming, and often inaccessible in intraoperative settings (Organization et al. 2011).
Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosis
Our top-performing model achieves Dice scores of 0. 882, 0. 840, and 0. 926, and Hausdorff Distance (95%) scores of 15. 324, 37. 518, and 13. 971 for enhancing tumor, tumor core, and whole tumor, respectively, in the validation phase.
A Survey of Uncertainty Estimation in LLMs: Theory Meets Practice
As large language models (LLMs) continue to evolve, understanding and quantifying the uncertainty in their predictions is critical for enhancing application credibility.
Cloud-Based Federation Framework and Prototype for Open, Scalable, and Shared Access to NextG and IoT Testbeds
In this work, we present a new federation framework for UnionLabs, an innovative cloud-based resource-sharing infrastructure designed for next-generation (NextG) and Internet of Things (IoT) over-the-air (OTA) experiments.
On the Effects of Modeling on the Sim-to-Real Transfer Gap in Twinning the POWDER Platform
Digital Twin (DT) technology is expected to play a pivotal role in NextG wireless systems.
Structure-preserving Image Translation for Depth Estimation in Colonoscopy Video
Monocular depth estimation in colonoscopy video aims to overcome the unusual lighting properties of the colonoscopic environment.
Large Language Model Watermark Stealing With Mixed Integer Programming
The Large Language Model (LLM) watermark is a newly emerging technique that shows promise in addressing concerns surrounding LLM copyright, monitoring AI-generated text, and preventing its misuse.
Surgical-LVLM: Learning to Adapt Large Vision-Language Model for Grounded Visual Question Answering in Robotic Surgery
Recent advancements in Surgical Visual Question Answering (Surgical-VQA) and related region grounding have shown great promise for robotic and medical applications, addressing the critical need for automated methods in personalized surgical mentorship.
TeleChat Technical Report
Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe.
Masked Language Model Based Textual Adversarial Example Detection
To explore how to use the masked language model in adversarial detection, we propose a novel textual adversarial example detection method, namely Masked Language Model-based Detection (MLMD), which can produce clearly distinguishable signals between normal examples and adversarial examples by exploring the changes in manifolds induced by the masked language model.
Evaluating Membership Inference Through Adversarial Robustness
The usage of deep learning is being escalated in many applications.
Self-Supervised Adversarial Example Detection by Disentangled Representation
To alleviate this problem, we explore how to detect adversarial examples with disentangled label/semantic features under the autoencoder structure.
The Modeling of SDL Aiming at Knowledge Acquisition in Automatic Driving
In this paper we proposed an ultimate theory to solve the multi-target control problem through its introduction to the machine learning framework in automatic driving, which explored the implementation of excellent drivers' knowledge acquisition.
