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Found 21 papers, 5 papers with code
Evaluation of ChatGPT-Generated Medical Responses: A Systematic Review and Meta-Analysis
Large language models such as ChatGPT are increasingly explored in medical domains.
GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems
In this paper, we propose an optimization-based quantized FL algorithm, which can appropriately fit a general edge computing system with uniform or nonuniform computing and communication resources at the workers.
Trustworthy Multi-phase Liver Tumor Segmentation via Evidence-based Uncertainty
Multi-phase liver contrast-enhanced computed tomography (CECT) images convey the complementary multi-phase information for liver tumor segmentation (LiTS), which are crucial to assist the diagnosis of liver cancer clinically.
Optimization and Optimizers for Adversarial Robustness
Taking advantage of PWCF and other existing numerical algorithms, we further explore the distinct patterns in the solutions found for solving these optimization problems using various combinations of losses, perturbation models, and optimization algorithms.
Imbalanced Classification in Medical Imaging via Regrouping
We propose performing imbalanced classification by regrouping majority classes into small classes so that we turn the problem into balanced multiclass classification.
Optimization for Robustness Evaluation beyond $\ell_p$ Metrics
Empirical evaluation of deep learning models against adversarial attacks entails solving nontrivial constrained optimization problems.
Optimization-based Block Coordinate Gradient Coding for Mitigating Partial Stragglers in Distributed Learning
To reduce computational complexity, we first transform each to an equivalent but much simpler discrete problem with N\llL variables representing the partition of the L coordinates into N blocks, each with identical redundancy.
PointAttN: You Only Need Attention for Point Cloud Completion
To solve the problems, we leverage the cross-attention and self-attention mechanisms to design novel neural network for processing point cloud in a per-point manner to eliminate kNNs.
An Optimization Framework for Federated Edge Learning
To explore the full potential of FL in such an edge computing system, we first present a general FL algorithm, namely GenQSGD, parameterized by the numbers of global and local iterations, mini-batch size, and step size sequence.
Energy-efficient Cooperative Offloading for Edge Computing-enabled Vehicular Networks
Edge computing technology has great potential to improve various computation-intensive applications in vehicular networks by providing sufficient computation resources for vehicles.
Optimization-Based GenQSGD for Federated Edge Learning
Then, we optimize the algorithm parameters to minimize the energy cost under the time constraint and convergence error constraint.
Sample-based and Feature-based Federated Learning for Unconstrained and Constrained Nonconvex Optimization via Mini-batch SSCA
We show that the proposed FL algorithms converge to stationary points and Karush-Kuhn-Tucker (KKT) points of the respective unconstrained and constrained nonconvex problems, respectively.
Sample-based Federated Learning via Mini-batch SSCA
In this paper, we investigate unconstrained and constrained sample-based federated optimization, respectively.
Graph Attention Tracking
We propose to establish part-to-part correspondence between the target and the search region with a complete bipartite graph, and apply the graph attention mechanism to propagate target information from the template feature to the search feature.
Jointly Sparse Signal Recovery and Support Recovery via Deep Learning with Applications in MIMO-based Grant-Free Random Access
Recent key applications include channel estimation and device activity detection in MIMO-based grant-free random access which is proposed to support massive machine-type communications (mMTC) for Internet of Things (IoT).
Improving auto-encoder novelty detection using channel attention and entropy minimization
Novelty detection is a important research area which mainly solves the classification problem of inliers which usually consists of normal samples and outliers composed of abnormal samples.
SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking
The proposed framework SiamCAR consists of two simple subnetworks: one Siamese subnetwork for feature extraction and one classification-regression subnetwork for bounding box prediction.
Statistical Analysis of Stationary Solutions of Coupled Nonconvex Nonsmooth Empirical Risk Minimization
This paper has two main goals: (a) establish several statistical properties---consistency, asymptotic distributions, and convergence rates---of stationary solutions and values of a class of coupled nonconvex and nonsmoothempirical risk minimization problems, and (b) validate these properties by a noisy amplitude-based phase retrieval problem, the latter being of much topical interest. Derived from available data via sampling, these empirical risk minimization problems are the computational workhorse of a population risk model which involves the minimization of an expected value of a random functional.
Estimation of Individualized Decision Rules Based on an Optimized Covariate-Dependent Equivalent of Random Outcomes
Recent exploration of optimal individualized decision rules (IDRs) for patients in precision medicine has attracted a lot of attention due to the heterogeneous responses of patients to different treatments.
Clustering by Orthogonal NMF Model and Non-Convex Penalty Optimization
We then apply a non-convex penalty (NCP) approach to add them to the objective as penalty terms, leading to a problem that is efficiently solvable.
End-to-end feature fusion siamese network for adaptive visual tracking
Both features and the channel weights are utilized in a template generation layer to generate a discriminative template.
