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Found 16 papers, 2 papers with code
Style Transfer: From Stitching to Neural Networks
This article compares two style transfer methods in image processing: the traditional method, which synthesizes new images by stitching together small patches from existing images, and a modern machine learning-based approach that uses a segmentation network to isolate foreground objects and apply style transfer solely to the background.
Quantitative Evaluation of the Saliency Map for Alzheimer's Disease Classifier with Anatomical Segmentation
In order to leverage the fact that the brain volume shrinkage happens in AD patients during disease progression, we define a new evaluation metric, brain volume change score (VCS), by computing the average Pearson correlation of the brain volume changes and the saliency values of a model in different brain regions for each patient.
Matrix Denoising with Doubly Heteroscedastic Noise: Fundamental Limits and Optimal Spectral Methods
We study the matrix denoising problem of estimating the singular vectors of a rank-$1$ signal corrupted by noise with both column and row correlations.
FedASTA: Federated adaptive spatial-temporal attention for traffic flow prediction
Federated learning (FL) has been proposed as a framework to enable model training across distributed devices without sharing original data which reduce privacy concern.
Application of RESNET50 Convolution Neural Network for the Extraction of Optical Parameters in Scattering Media
Estimation of the optical properties of scattering media such as tissue is important in diagnostics as well as in the development of techniques to image deeper.
On the Communication Complexity of Decentralized Bilevel Optimization
To the best of our knowledge, this is the first time such favorable theoretical results have been achieved with mild assumptions in the heterogeneous setting.
Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing
Our main result is a precise asymptotic characterization of the performance of spectral estimators.
Can ChatGPT Pass An Introductory Level Functional Language Programming Course?
The recent introduction of ChatGPT has drawn significant attention from both industry and academia due to its impressive capabilities in solving a diverse range of tasks, including language translation, text summarization, and computer programming.
Can Decentralized Stochastic Minimax Optimization Algorithms Converge Linearly for Finite-Sum Nonconvex-Nonconcave Problems?
This motivates us to study decentralized minimax optimization algorithms for the nonconvex-nonconcave problem.
Federated Compositional Deep AUC Maximization
Federated learning has attracted increasing attention due to the promise of balancing privacy and large-scale learning; numerous approaches have been proposed.
RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment
Utilizing a reward model and a sufficient number of samples, our approach selects the high-quality samples, discarding those that exhibit undesired behavior, and subsequently enhancing the model by fine-tuning on these filtered samples.
Precise Asymptotics for Spectral Methods in Mixed Generalized Linear Models
In a mixed generalized linear model, the objective is to learn multiple signals from unlabeled observations: each sample comes from exactly one signal, but it is not known which one.
Environment Sensing Considering the Occlusion Effect: A Multi-View Approach
In this paper, we consider the problem of sensing the environment within a wireless cellular framework.
Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models
In this model, an estimator observes $n$ samples of a $d$-dimensional parameter vector $\theta_{*}\in\mathbb{R}^{d}$, multiplied by a random sign $ S_i $ ($1\le i\le n$), and corrupted by isotropic standard Gaussian noise.
Zero-Error Communication over Adversarial MACs
In particular, the treatment of marginal confusability does not follow from the point-to-point results by Wang et al. Our achievability results follow from random coding with expurgation.
Information Theory Information Theory
MG-GCN: Fast and Effective Learning with Mix-grained Aggregators for Training Large Graph Convolutional Networks
To tackle the dilemma between accuracy and efficiency, we propose to use aggregators with different granularities to gather neighborhood information in different layers.
