
An Accelerated DFO Algorithm for Finitesum Convex Functions
Derivativefree optimization (DFO) has recently gained a lot of momentum...
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Randomized BlockDiagonal Preconditioning for Parallel Learning
We study preconditioned gradientbased optimization methods where the pr...
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Convolutional Generation of Textured 3D Meshes
Recent generative models for 2D images achieve impressive visual results...
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Emulation of cosmological mass maps with conditional generative adversarial networks
Mass maps created using weak gravitational lensing techniques play a cru...
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Controlling Style and Semantics in WeaklySupervised Image Generation
We propose a weaklysupervised approach for conditional image generation...
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A Stochastic Tensor Method for Nonconvex Optimization
We present a stochastic optimization method that uses a fourthorder reg...
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Shadowing Properties of Optimization Algorithms
Ordinary differential equation (ODE) models of gradientbased optimizati...
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Cosmological Nbody simulations: a challenge for scalable generative models
Deep generative models, such as Generative Adversarial Networks (GANs) o...
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The Role of Memory in Stochastic Optimization
The choice of how to retain information about past gradients dramaticall...
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Ellipsoidal Trust Region Methods and the Marginal Value of Hessian Information for Neural Network Training
We investigate the use of ellipsoidal trust region constraints for secon...
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PolyMapper: Extracting City Maps using Polygons
We propose a method to leapfrog pixelwise, semantic segmentation of (ae...
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Evaluating GANs via Duality
Generative Adversarial Networks (GANs) have shown great results in accur...
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Continuoustime Models for Stochastic Optimization Algorithms
We propose a new continuoustime formulation for firstorder stochastic ...
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A Distributed SecondOrder Algorithm You Can Trust
Due to the rapid growth of data and computational resources, distributed...
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Towards a Theoretical Understanding of Batch Normalization
Normalization techniques such as Batch Normalization have been applied v...
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Adversarially Robust Training through Structured Gradient Regularization
We propose a novel datadependent structured gradient regularizer to inc...
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Local Saddle Point Optimization: A Curvature Exploitation Approach
Gradientbased optimization methods are the most popular choice for find...
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Escaping Saddles with Stochastic Gradients
We analyze the variance of stochastic gradients along negative curvature...
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Fast Cosmic Web Simulations with Generative Adversarial Networks
Dark matter in the universe evolves through gravity to form a complex ne...
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Fast Point Spread Function Modeling with Deep Learning
Modeling the Point Spread Function (PSF) of widefield surveys is vital ...
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Flexible Prior Distributions for Deep Generative Models
We consider the problem of training generative models with deep neural n...
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Semantic Interpolation in Implicit Models
In implicit models, one often interpolates between sampled points in lat...
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Generator Reversal
We consider the problem of training generative models with deep neural n...
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Learning Aerial Image Segmentation from Online Maps
This study deals with semantic segmentation of highresolution (aerial) ...
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Cosmological model discrimination with Deep Learning
We demonstrate the potential of Deep Learning methods for measurements o...
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An Online Learning Approach to Generative Adversarial Networks
We consider the problem of training generative models with a Generative ...
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Stabilizing Training of Generative Adversarial Networks through Regularization
Deep generative models based on Generative Adversarial Networks (GANs) h...
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Subsampled Cubic Regularization for Nonconvex Optimization
We consider the minimization of nonconvex functions that typically aris...
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Leveraging Large Amounts of Weakly Supervised Data for MultiLanguage Sentiment Classification
This paper presents a novel approach for multilingual sentiment classif...
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A Semisupervised Framework for Image Captioning
Stateoftheart approaches for image captioning require supervised trai...
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Probabilistic BagOfHyperlinks Model for Entity Linking
Many fundamental problems in natural language processing rely on determi...
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Variance Reduced Stochastic Gradient Descent with Neighbors
Stochastic Gradient Descent (SGD) is a workhorse in machine learning, ye...
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