8 citations · 22 across the 29 of their papers we have counts for
9 papers · 1 filter
Regime-aware financial volatility forecasting via in-context learning
Saba Asaad, Shayan Mohajer Hamidi, Ali Bereyhi
This work introduces a regime-aware in-context learning framework that leverages large language models (LLMs) for financial volatility forecasting under nonstationary market condit…
Revisiting GAN with Bayes-Optimal Discrimination
Mohammadreza Tavasoli Naeini, Ali Bereyhi, Morteza Noshad +2
We propose an alternative to the standard GAN training approach, in which the discriminator is a binary classifier trained by cross-entropy to distinguish real samples from generat…
Coding for Computation: Efficient Compression of Neural Networks for Reconfigurable Hardware
Hans Rosenberger, Rodrigo Fischer, Johanna S. Fröhlich +2
As state of the art neural networks (NNs) continue to grow in size, their resource-efficient implementation becomes ever more important. In this paper, we introduce a compression s…
Universal Training of Neural Networks to Achieve Bayes Optimal Classification Accuracy
Mohammadreza Tavasoli Naeini, Ali Bereyhi, Morteza Noshad +2
This work invokes the notion of -divergence to introduce a novel upper bound on the Bayes error rate of a general classification task. We show that the proposed bound can be com…
Over-the-Air Fair Federated Learning via Multi-Objective Optimization
Shayan Mohajer Hamidi, Ali Bereyhi, Saba Asaad +1
In federated learning (FL), heterogeneity among the local dataset distributions of clients can result in unsatisfactory performance for some, leading to an unfair model. To address…
Regularized Top-: A Bayesian Framework for Gradient Sparsification
Ali Bereyhi, Ben Liang, Gary Boudreau +1
Error accumulation is effective for gradient sparsification in distributed settings: initially-unselected gradient entries are eventually selected as their accumulated error exceed…