Search Results for author: Anastasia Borovykh

Found 11 papers, 3 papers with code

Quantifying and Localizing Private Information Leakage from Neural Network Gradients

no code implementations28 May 2021 Fan Mo, Anastasia Borovykh, Mohammad Malekzadeh, Hamed Haddadi, Soteris Demetriou

In this paper, we introduce theoretically-motivated measures to quantify information leakages in both attack-dependent and attack-independent manners.

Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs

1 code implementation25 May 2021 Mohammad Malekzadeh, Anastasia Borovykh, Deniz Gündüz

It is known that deep neural networks, trained for the classification of non-sensitive target attributes, can reveal sensitive attributes of their input data through internal representations extracted by the classifier.

Knowledge Distillation

Layer-wise Characterization of Latent Information Leakage in Federated Learning

no code implementations17 Oct 2020 Fan Mo, Anastasia Borovykh, Mohammad Malekzadeh, Hamed Haddadi, Soteris Demetriou

Training deep neural networks via federated learning allows clients to share, instead of the original data, only the model trained on their data.

Federated Learning

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

no code implementations15 Jul 2020 Anastasia Borovykh, Nikolas Kantas, Panos Parpas, Grigorios A. Pavliotis

A second alternative is to use a fixed step-size and run independent replicas of the algorithm and average these.

Optimally weighted loss functions for solving PDEs with Neural Networks

1 code implementation14 Feb 2020 Remco van der Meer, Cornelis Oosterlee, Anastasia Borovykh

We then derive a choice for the scaling parameter that is optimal with respect to a measure of relative error.

Numerical Analysis Numerical Analysis

On Calibration Neural Networks for extracting implied information from American options

no code implementations31 Jan 2020 Shuaiqiang Liu, Álvaro Leitao, Anastasia Borovykh, Cornelis W. Oosterlee

For the implied dividend yield, we formulate the inverse problem as a calibration problem and determine simultaneously the implied volatility and dividend yield.

Analytic expressions for the output evolution of a deep neural network

no code implementations18 Dec 2019 Anastasia Borovykh

We present a novel methodology based on a Taylor expansion of the network output for obtaining analytical expressions for the expected value of the network weights and output under stochastic training.

A neural network-based framework for financial model calibration

no code implementations23 Apr 2019 Shuaiqiang Liu, Anastasia Borovykh, Lech A. Grzelak, Cornelis W. Oosterlee

A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN).

Generalisation in fully-connected neural networks for time series forecasting

no code implementations14 Feb 2019 Anastasia Borovykh, Cornelis W. Oosterlee, Sander M. Bohte

In this paper we study the generalization capabilities of fully-connected neural networks trained in the context of time series forecasting.

Learning Theory Time Series +1

A Gaussian Process perspective on Convolutional Neural Networks

no code implementations25 Oct 2018 Anastasia Borovykh

In this paper we cast the well-known convolutional neural network in a Gaussian process perspective.

Gaussian Processes

Conditional Time Series Forecasting with Convolutional Neural Networks

3 code implementations14 Mar 2017 Anastasia Borovykh, Sander Bohte, Cornelis W. Oosterlee

The proposed network contains stacks of dilated convolutions that allow it to access a broad range of history when forecasting, a ReLU activation function and conditioning is performed by applying multiple convolutional filters in parallel to separate time series which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series.

Time Series Time Series Forecasting

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