From the 1 of 9 linked papers with an AI index.
9 papers
Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance
Pei-Hsuan Hsia, Lars H. Heyen, Arvid Weyrauch +4
The paper investigates using a Student's t likelihood instead of a Gaussian in Bayesian neural networks and finds it improves predictive performance and sometimes reduces training…
Beyond Backpropagation: Optimization with Multi-Tangent Forward Gradients
Katharina Flügel, Daniel Coquelin, Marie Weiel +3
The gradients used to train neural networks are typically computed using backpropagation. While an efficient way to obtain exact gradients, backpropagation is computationally expen…
Feed-Forward Optimization With Delayed Feedback for Neural Network Training
Katharina Flügel, Daniel Coquelin, Marie Weiel +3
Backpropagation has long been criticized for being biologically implausible due to its reliance on concepts that are not viable in natural learning processes. Two core issues are t…
Exploring Federated Learning for Thermal Urban Feature Segmentation -- A Comparison of Centralized and Decentralized Approaches
Leonhard Duda, Khadijeh Alibabaei, Elena Vollmer +11
Federated Learning (FL) is an approach for training a shared Machine Learning (ML) model with distributed training data and multiple participants. FL allows bypassing limitations o…
pyGinkgo: A Sparse Linear Algebra Operator Framework for Python
Keshvi Tuteja, Gregor Olenik, Roman Mishchuk +5
Sparse linear algebra is a cornerstone of many scientific computing and machine learning applications. Python has become a popular choice for these applications due to its simplici…
Energy Consumption in Parallel Neural Network Training
Philipp Huber, David Li, Juan Pedro Gutiérrez Hermosillo Muriedas +4
The increasing demand for computational resources of training neural networks leads to a concerning growth in energy consumption. While parallelization has enabled upscaling model…