Anastasia Borovykh | DeepAI

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∙ 02/01/2023

Privacy Risk for anisotropic Langevin dynamics using relative entropy bounds

The privacy preserving properties of Langevin dynamics with additive iso...

0 Anastasia Borovykh, et al. ∙

∙ 05/28/2021

Quantifying Information Leakage from Gradients

Sharing deep neural networks' gradients instead of training data could f...

0 Fan Mo, et al. ∙

∙ 05/25/2021

Honest-but-Curious Nets: Sensitive Attributes of Private Inputs can be Secretly Coded into the Entropy of Classifiers' Outputs

It is known that deep neural networks, trained for the classification of...

23 Mohammad Malekzadeh, et al. ∙

∙ 10/17/2020

Layer-wise Characterization of Latent Information Leakage in Federated Learning

Training a deep neural network (DNN) via federated learning allows parti...

0 Fan Mo, et al. ∙

∙ 07/15/2020

On stochastic mirror descent with interacting particles: convergence properties and variance reduction

An open problem in optimization with noisy information is the computatio...

0 Anastasia Borovykh, et al. ∙

∙ 02/14/2020

Optimally weighted loss functions for solving PDEs with Neural Networks

Recent works have shown that deep neural networks can be employed to sol...

0 Remco van der Meer, et al. ∙

∙ 01/31/2020

On Calibration Neural Networks for extracting implied information from American options

Extracting implied information, like volatility and/or dividend, from ob...

0 Shuaiqiang Liu, et al. ∙

∙ 12/18/2019

Analytic expressions for the output evolution of a deep neural network

We present a novel methodology based on a Taylor expansion of the networ...

0 Anastasia Borovykh, et al. ∙

∙ 04/23/2019

A neural network-based framework for financial model calibration

A data-driven approach called CaNN (Calibration Neural Network) is propo...

0 Shuaiqiang Liu, et al. ∙

∙ 02/14/2019

Generalisation in fully-connected neural networks for time series forecasting

In this paper we study the generalisation capabilities of fully-connecte...

0 Anastasia Borovykh, et al. ∙

∙ 10/25/2018

A Gaussian Process perspective on Convolutional Neural Networks

In this paper we cast the well-known convolutional neural network in a G...

0 Anastasia Borovykh, et al. ∙

∙ 03/14/2017

Conditional Time Series Forecasting with Convolutional Neural Networks

We present a method for conditional time series forecasting based on the...

0 Anastasia Borovykh, et al. ∙

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