Summary of Dual-criterion Model Aggregation in Federated Learning: Balancing Data Quantity and Quality, by Haizhou Zhang et al.
Dual-Criterion Model Aggregation in Federated Learning: Balancing Data Quantity and Qualityby Haizhou Zhang, Xianjia Yu,…
Dual-Criterion Model Aggregation in Federated Learning: Balancing Data Quantity and Qualityby Haizhou Zhang, Xianjia Yu,…
Machines and Mathematical Mutations: Using GNNs to Characterize Quiver Mutation Classesby Jesse He, Helen Jenne,…
Quantifying Knowledge Distillation Using Partial Information Decompositionby Pasan Dissanayake, Faisal Hamman, Barproda Halder, Ilia Sucholutsky,…
Model Stealing for Any Low-Rank Language Modelby Allen Liu, Ankur MoitraFirst submitted to arxiv on:…
Accident Impact Prediction based on a deep convolutional and recurrent neural network modelby Pouyan Sajadi,…
Exogenous Randomness Empowering Random Forestsby Tianxing Mei, Yingying Fan, Jinchi LvFirst submitted to arxiv on:…
Score-based generative diffusion with “active” correlated noise sourcesby Alexandra Lamtyugina, Agnish Kumar Behera, Aditya Nandy,…
Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learningby…
Exploring Variational Autoencoders for Medical Image Generation: A Comprehensive Studyby Khadija Rais, Mohamed Amroune, Abdelmadjid…
Identifying Differential Patient Care Through Inverse Intent Inferenceby Hyewon Jeong, Siddharth Nayak, Taylor Killian, Sanjat…