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Found 9 papers, 5 papers with code
Chasing Better Deep Image Priors between Over- and Under-parameterization
Besides, we also extend LIP to compressive sensing image reconstruction, where a pre-trained GAN generator is used as the prior (in contrast to untrained DIP or deep decoder), and confirm its validity in this setting too.
Using Crank-Nikolson Scheme to Solve the Korteweg-de Vries (KdV) Equation
The Korteweg-de Vries (KdV) equation is a fundamental partial differential equation that models wave propagation in shallow water and other dispersive media.
FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction
Objective: Mitigating algorithmic disparities is a critical challenge in healthcare research, where ensuring equity and fairness is paramount.
Bound Tightening Network for Robust Crowd Counting
Crowd Counting is a fundamental topic, aiming to estimate the number of individuals in the crowded images or videos fed from surveillance cameras.
GraphEval2000: Benchmarking and Improving Large Language Models on Graph Datasets
To address this gap, we introduce GraphEval2000, the first comprehensive graph dataset, comprising 40 graph data structure problems along with 2000 test cases.
Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data
We applied our approach to sites with heterogeneous survival data originating from emergency departments in Singapore and the United States.
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches
Our goal was to bridge the gap by presenting the first comprehensive comparison of FL frameworks from both engineering and statistical domains.
Lottery Image Prior
Drawing inspirations from the recently prosperous research on lottery ticket hypothesis (LTH), we conjecture and study a novel “lottery image prior” (LIP), stated as: given an (untrained or trained) DNN-based image prior, it will have a sparse subnetwork that can be training in isolation, to match the original DNN’s performance when being applied as a prior to various image inverse problems.
Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting
Crowd counting has drawn much attention due to its importance in safety-critical surveillance systems.
