3 papers
cs.LG2024
Activations Through Extensions: A Framework To Boost Performance Of Neural Networks
Chandramouli Kamanchi, Sumanta Mukherjee, Kameshwaran Sampath +4
Activation functions are non-linearities in neural networks that allow them to learn complex mapping between inputs and outputs. Typical choices for activation functions are ReLU,…
cs.LG2024
Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis
Tsuyoshi Idé, Dzung T. Phan, Rudy Raymond
This paper presents two methodological advancements in decentralized multi-task learning under privacy constraints, aiming to pave the way for future developments in next-generatio…
cs.LG2023
An End-to-End Time Series Model for Simultaneous Imputation and Forecast
Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo +4
Time series forecasting using historical data has been an interesting and challenging topic, especially when the data is corrupted by missing values. In many industrial problem, it…