Verifying Low-Dimensional Input Neural Networks via Input Quantization

Input Parameters' estimation via neural networks

Input Convex Neural Networks

This paper presents the input convex neural network architecture. These are scalar-valued (potentially deep) neural networks with constraints on the network parameters such that the output of the network is a convex function of (some of) the inputs. The networks allow for efficient inference via optimization over some inputs to the network given others, and can be applied to settings including ...

Visual Explanations for Convolutional Neural Networks via Input Resampling

The predictive power of neural networks often costs model interpretability. Several techniques have been developed for explaining model outputs in terms of input features; however, it is difficult to translate such interpretations into actionable insight. Here, we propose a framework to analyze predictions in terms of the model’s internal features by inspecting information flow through the netw...

Input output stability of recurrent neural networks

i Foreword Recurrent neural networks are an attractive tool for both practical applications and for the modeling of biological nerve nets, but their successful application requires an understanding of their dynamical properties, in particular, their stability. The present work provides an in-depth study of this challenging issue and contributes a number of new results that are also important fo...

Extending the CMAC model: adaptive input quantization