3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.LG2024
AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability
Stefan Meisenbacher, Kaleb Phipps, Oskar Taubert +4
Optimizing smart grid operations relies on critical decision-making informed by uncertainty quantification, making probabilistic forecasting a vital tool. Designing such forecastin…