arXiv research

Deep learning techniques are increasingly being considered for geological applications where -- much like in computer vision -- the challenges are characterized by high-dimensional spatial data dominated by multipoint statistics. In particular, a novel technique called generative adversarial networks has been recently …

The paper studies conditions for exact posterior modeling in Bayesian networks.

Method learns conditional distributions using neural entropic optimal transport.

Proposes a new GAN architecture for generating data conditioned on partial information.

CAN learns conditional and interventional distributions from unlabeled data.

Generative model extracts road networks from images.

Improves conditional distribution modeling with simpler training.

Generative model for inferring graph from time series data.

Generative neural network designs novel 3D molecules with specified properties.

We compress large neural networks for quick adaptation to specific contexts.

We present a novel tractable generative model that extends Sum-Product Networks (SPNs) and significantly boosts their power. We call it Sum-Product-Quotient Networks (SPQNs), whose core concept is to incorporate conditional distributions into the model by direct computation using quotient nodes, e.g. $P(A|B) = \frac{P(…

Study shows depth improves trainability of neural networks by improving kernel conditioning.

New insights into how linear classifiers and leaky ReLU networks can overfit without harming generalization.

The GANs are generative models whose random samples realistically reflect natural images. It also can generate samples with specific attributes by concatenating a condition vector into the input, yet research on this field is not well studied. We propose novel methods of conditioning generative adversarial networks (GA…

Generative adversarial networks sample unknown high-dimensional conditional distributions.

Despite recent advances, the remaining bottlenecks in deep generative models are necessity of extensive training and difficulties with generalization from small number of training examples. We develop a new generative model called Generative Matching Network which is inspired by the recently proposed matching networks …

The study characterizes conditions for trainability and generalization in deep neural networks.

Unified approach for nonparametric regression and conditional distribution learning.

Proposes using GANs to solve phase retrieval problems.

The network Lasso is a recently proposed convex optimization method for machine learning from massive network structured datasets, i.e., big data over networks. It is a variant of the well-known least absolute shrinkage and selection operator (Lasso), which is underlying many methods in learning and signal processing i…

This paper addresses the following question of neural network identifiability: Does the input-output map realized by a feed-forward neural network with respect to a given nonlinearity uniquely specify the network architecture, weights, and biases? Existing literature on the subject Sussman 1992, Albertini, Sontag et al…

LcGAN generates synthetic CT images for hemorrhagic lesion segmentation.

SLOGAN improves GANs' conditional generation by balancing latent attribute distributions.

Geostatistical modeling of petrophysical properties is a key step in modern integrated oil and gas reservoir studies. Recently, generative adversarial networks (GAN) have been shown to be a successful method for generating unconditional simulations of pore- and reservoir-scale models. This contribution leverages the di…

A new method uses Schrödinger bridges for deep conditional generative learning.

Conditional generators learn the data distribution for each class in a multi-class scenario and generate samples for a specific class given the right input from the latent space. In this work, a method known as "Versatile Auxiliary Classifier with Generative Adversarial Network" for multi-class scenarios is presented. …

In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network. Our model consists of a top-down stack of GANs, each learned to generate lower-level representations conditioned on …

Faster reconstruction of compressed signals using conditional GAN and NPGD.

New framework models non-exchangeable networks with latent orders and graphons.

SymPE breaks symmetries in equivariant networks, improving performance across various tasks.

Generative model creates realistic scenes from pixel-wise labels.

The recent success of Generative Adversarial Networks (GAN) is a result of their ability to generate high quality images from a latent vector space. An important application is the generation of images from a text description, where the text description is encoded and further used in the conditioning of the generated i…

A new IC-Connection improves disentanglement in conditional GANs.

A neural network approach for efficient conditional SHAP calculations.

Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input $z$ to a sample $\mathbf{x}$ that the discriminator seeks to distinguish. We propose a new GAN called Bayesian Conditional Generative Adversarial Networks (BC-GANs) that use a random generator function…

New test for conditional independence using GNNs avoids estimating conditional distributions.

We introduce a principled approach for unsupervised structure learning of deep neural networks. We propose a new interpretation for depth and inter-layer connectivity where conditional independencies in the input distribution are encoded hierarchically in the network structure. Thus, the depth of the network is determi…

This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex conditional densities as linear combinations of a family of kernel functions centered at a subset of training points. The weights are deter…

We propose Progressive Structure-conditional Generative Adversarial Networks (PSGAN), a new framework that can generate full-body and high-resolution character images based on structural information. Recent progress in generative adversarial networks with progressive training has made it possible to generate high-resol…

We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN). Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN approaches tend to learn an overly simplified distribution where an input…

New method solves PDEs for any initial condition without retraining.

A new method for learning conditional distributions using ODEs and neural networks.

New method recovers signals from compressed measurements using generative networks with contractive layers.

Recently, Neural networks have seen a huge surge in its adoption due to their ability to provide high accuracy on various tasks. On the other hand, the existence of adversarial examples have raised suspicions regarding the generalization capabilities of neural networks. In this work, we focus on the weight matrix learn…

New method tests CMI using deep neural networks for high-dimensional data.

New method efficiently simulates fluid flows across various conditions.

GWINs improve classifier accuracy by translating uncertain observations.

Conditional Generative Adversarial Networks (cGANs) are generative models that can produce data samples ($x$) conditioned on both latent variables ($z$) and known auxiliary information ($c$). We propose the Bidirectional cGAN (BiCoGAN), which effectively disentangles $z$ and $c$ in the generation process and provides a…