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Giorgia Dellaferrera

4 accepted papers

2024

Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization

Ravi Francesco Srinivasan, Francesca Mignacco, Martino Sorbaro, Maria Refinetti, Avi Cooper, Gabriel Kreiman +1
ICLR 2024poster

"Forward-only" algorithms, which train neural networks while avoiding a backward pass, have recently gained attention as a way of solving the biologically unrealistic aspects of backpropagation. Here, we first address compelling challenges related to the "forward-only" rules, which include reducing…

Cited by 21Source ↗PDF ↗Scholar ↗
2022

Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass

Giorgia Dellaferrera, Gabriel Kreiman
ICML 2022spotlight

Supervised learning in artificial neural networks typically relies on backpropagation, where the weights are updated based on the error-function gradients and sequentially propagated from the output layer to the input layer. Although this approach has proven effective in a wide domain of application…

Cited by 62Source ↗PDF ↗code ↗Scholar ↗
2020

A Bin Encoding Training of a Spiking Neural Network Based Voice Activity Detection

Giorgia Dellaferrera, Flavio Martinelli, Milos Cernak
ICASSP 2020accepted

Advances of deep learning for Artificial Neural Networks (ANNs) have led to significant improvements in the performance of digital signal processing systems implemented on digital chips. Although recent progress in low-power chips is remarkable, neuromorphic chips that run Spiking Neural Networks (S…

Cited by 0Source ↗Scholar ↗
2020

Spiking Neural Networks Trained With Backpropagation for Low Power Neuromorphic Implementation of Voice Activity Detection

Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak
ICASSP 2020accepted

Recent advances in Voice Activity Detection (VAD) are driven by artificial and Recurrent Neural Networks (RNNs), however, using a VAD system in battery-operated devices requires further power efficiency. This can be achieved by neuromorphic hardware, which enables Spiking Neural Networks (SNNs) to p…

Cited by 0Source ↗Scholar ↗