Search Results for author:
Found 15 papers, 4 papers with code
On original and latent space connectivity in deep neural networks
We study whether inputs from the same class can be connected by a continuous path, in original or latent representation space, such that all points on the path are mapped by the neural network model to the same class.
Leave-one-out Distinguishability in Machine Learning
We introduce an analytical framework to quantify the changes in a machine learning algorithm's output distribution following the inclusion of a few data points in its training set, a notion we define as leave-one-out distinguishability (LOOD).
Privacy Risk for anisotropic Langevin dynamics using relative entropy bounds
The privacy preserving properties of Langevin dynamics with additive isotropic noise have been extensively studied.
Data-driven initialization of deep learning solvers for Hamilton-Jacobi-Bellman PDEs
A deep learning approach for the approximation of the Hamilton-Jacobi-Bellman partial differential equation (HJB PDE) associated to the Nonlinear Quadratic Regulator (NLQR) problem.
Quantifying and Localizing Usable Information Leakage from Neural Network Gradients
Our proposed framework enables clients to localize and quantify the private information leakage in a layer-wise manner, and enables a better understanding of the sources of information leakage in collaborative learning, which can be used by future studies to benchmark new attacks and defense mechanisms.
Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs
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.
Layer-wise Characterization of Latent Information Leakage in Federated Learning
Training deep neural networks via federated learning allows clients to share, instead of the original data, only the model trained on their data.
On stochastic mirror descent with interacting particles: convergence properties and variance reduction
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
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
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
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
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
In this paper we study the generalization capabilities of fully-connected neural networks trained in the context of time series forecasting.
A Gaussian Process perspective on Convolutional Neural Networks
In this paper we cast the well-known convolutional neural network in a Gaussian process perspective.
Conditional Time Series Forecasting with Convolutional Neural Networks
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.
