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Found 8 papers, 1 papers with code
Using Higher-Order Moments to Assess the Quality of GAN-generated Image Features
The rapid advancement of Generative Adversarial Networks (GANs) necessitates the need to robustly evaluate these models.
Seven open problems in applied combinatorics
We present and discuss seven different open problems in applied combinatorics.
The SVD of Convolutional Weights: A CNN Interpretability Framework
Fully connected layers can be studied by decomposing their weight matrices using a singular value decomposition, in effect studying the correlations between the rows in each matrix to discover the dynamics of the map.
Evaluating generative networks using Gaussian mixtures of image features
We define a performance measure, which we call WaM, on two sets of images by using Inception-v3 (or another classifier) to featurize the images, estimate two GMMs, and use the restricted $2$-Wasserstein distance to compare the GMMs.
Entanglement Induced Barren Plateaus
In particular, we show that quantum neural networks that satisfy a volume-law in the entanglement entropy will give rise to models not suitable for learning with high probability.
Modeling Atmospheric Data and Identifying Dynamics: Temporal Data-Driven Modeling of Air Pollutants
Atmospheric modeling has recently experienced a surge with the advent of deep learning.
Robust Assessment of Real-World Adversarial Examples
We explore rigorous, systematic, and controlled experimental evaluation of adversarial examples in the real world and propose a testing regimen for evaluation of real world adversarial objects.
Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks
We employ physics informed DNNs to estimate the unknown space-dependent diffusion coefficient in a linear diffusion equation and an unknown constitutive relationship in a non-linear diffusion equation.
Analysis of PDEs Computational Physics
