University of Minnesota

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The University of Minnesota, Twin Cities is a public research university in Minneapolis and Saint Paul, Minnesota.

Fisher Auto-Encoders

It has been conjectured that the Fisher divergence is more robust to mod...
07/12/2020 ∙ by Khalil Elkhalil, et al. ∙ 43 ∙ share

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Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction

Following the success of deep learning in a wide range of applications, ...
04/01/2019 ∙ by Florian Knoll, et al. ∙ 42 ∙ share

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Knowledge Induced Deep Q-Network for a Slide-to-Wall Object Grasping

In robotic grasping tasks, robots usually avoid any collisions with the ...
10/09/2019 ∙ by Hengyue Liang, et al. ∙ 27 ∙ share

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Physics-Guided Deep Neural Networks for PowerFlow Analysis

Solving power flow (PF) equations is the basis of power flow analysis, w...
01/31/2020 ∙ by Xinyue Hu, et al. ∙ 27 ∙ share

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Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization

Domain generalization is the problem of machine learning when the traini...
02/13/2020 ∙ by Vikas K. Garg, et al. ∙ 26 ∙ share

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Automated Multiclass Cardiac Volume Segmentation and Model Generation

Many strides have been made in semantic segmentation of multiple classes...
09/14/2019 ∙ by Erik Gaasedelen, et al. ∙ 23 ∙ share

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A Communication Efficient Vertical Federated Learning Framework

One critical challenge for applying today's Artificial Intelligence (AI)...
12/24/2019 ∙ by Yang Liu, et al. ∙ 23 ∙ share

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Human Annotations Improve GAN Performances

Generative Adversarial Networks (GANs) have shown great success in many ...
11/15/2019 ∙ by Juanyong Duan, et al. ∙ 19 ∙ share

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A Deep Learning Approach to Grasping the Invisible

We introduce a new problem named "grasping the invisible", where a robot...
09/11/2019 ∙ by Yang Yang, et al. ∙ 17 ∙ share

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Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception

In this paper, we introduce and tackle the simultaneous enhancement and ...
02/04/2020 ∙ by Md Jahidul Islam, et al. ∙ 17 ∙ share

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Stability and Generalization of Graph Convolutional Neural Networks

Inspired by convolutional neural networks on 1D and 2D data, graph convo...
05/03/2019 ∙ by Saurabh Verma, et al. ∙ 14 ∙ share

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Algorithms for ℓ_p-based semi-supervised learning on graphs

We develop fast algorithms for solving the variational and game-theoreti...
01/15/2019 ∙ by Mauricio Flores Rios, et al. ∙ 12 ∙ share

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Causal Feature Discovery through Strategic Modification

We consider an online regression setting in which individuals adapt to t...
02/17/2020 ∙ by Yahav Bechavod, et al. ∙ 11 ∙ share

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Supervised Learning in Temporally-Coded Spiking Neural Networks with Approximate Backpropagation

In this work we propose a new supervised learning method for temporally-...
07/27/2020 ∙ by Andrew Stephan, et al. ∙ 11 ∙ share

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Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks

In this paper, we propose Generative Adversarial Network (GAN) architect...
06/11/2018 ∙ by Yash Upadhyay, et al. ∙ 10 ∙ share

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Two Birds with One Network: Unifying Failure Event Prediction and Time-to-failure Modeling

One of the key challenges in predictive maintenance is to predict the im...
12/18/2018 ∙ by Karan Aggarwal, et al. ∙ 10 ∙ share

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Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics

In this paper, we investigate deep learning (DL)-enabled signal demodula...
03/08/2019 ∙ by Hongmei Wang, et al. ∙ 8 ∙ share

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Unsupervised Ensemble Classification with Dependent Data

Ensemble learning, the machine learning paradigm where multiple algorith...
06/22/2019 ∙ by Panagiotis A. Traganitis, et al. ∙ 7 ∙ share

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Fast Estimating Pedestrian Moving State Based on Single 2D Body Pose by Shallow Neural Network

Crossing or Not-Crossing (C/NC) problem is important to autonomous vehic...
07/09/2019 ∙ by Zixing Wang, et al. ∙ 7 ∙ share

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Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and Occlusion

Images of heavily occluded objects in cluttered scenes, such as fruit cl...
01/30/2020 ∙ by Wenbo Dong, et al. ∙ 7 ∙ share

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Learning ReLU Networks on Linearly Separable Data: Algorithm, Optimality, and Generalization

Neural networks with ReLU activations have achieved great empirical succ...
08/14/2018 ∙ by Gang Wang, et al. ∙ 6 ∙ share

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Deep Compressive Autoencoder for Action Potential Compression in Large-Scale Neural Recording

Understanding the coordinated activity underlying brain computations req...
09/14/2018 ∙ by Tong Wu, et al. ∙ 6 ∙ share

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A Stochastic Composite Gradient Method with Incremental Variance Reduction

We consider the problem of minimizing the composition of a smooth (nonco...
06/24/2019 ∙ by Junyu Zhang, et al. ∙ 6 ∙ share

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Decentralized Optimization of Vehicle Route Planning – A Cross-City Comparative Study

New mobility concepts are at the forefront of research and innovation in...
01/10/2020 ∙ by Brionna Davis, et al. ∙ 6 ∙ share

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Unsupervised Continuous Object Representation Networks for Novel View Synthesis

Novel View Synthesis (NVS) is concerned with the generation of novel vie...
07/30/2020 ∙ by Nicolai Häni, et al. ∙ 6 ∙ share

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Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization

While stochastic gradient descent (SGD) and variants have been surprisin...
07/24/2019 ∙ by Xinyan Li, et al. ∙ 5 ∙ share

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Self-Supervised Adaptation of High-Fidelity Face Models for Monocular Performance Tracking

Improvements in data-capture and face modeling techniques have enabled u...
07/25/2019 ∙ by Jae Shin Yoon, et al. ∙ 5 ∙ share

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Multiview Cross-supervision for Semantic Segmentation

This paper presents a semi-supervised learning framework for a customize...
12/04/2018 ∙ by Yuan Yao, et al. ∙ 4 ∙ share

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Equal Opportunity in Online Classification with Partial Feedback

We study an online classification problem with partial feedback in which...
02/06/2019 ∙ by Yahav Bechavod, et al. ∙ 4 ∙ share

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Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective

Graph neural networks (GNNs) which apply the deep neural networks to gra...
06/10/2019 ∙ by Kaidi Xu, et al. ∙ 4 ∙ share

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Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics

Continuous control and planning remains a major challenge in robotics an...
08/13/2019 ∙ by Saurabh Daptardar, et al. ∙ 4 ∙ share

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Image Generation Via Minimizing Fréchet Distance in Discriminator Feature Space

For a given image generation problem, the intrinsic image manifold is of...
03/26/2020 ∙ by Khoa D. Doan, et al. ∙ 4 ∙ share

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Two-block vs. Multi-block ADMM: An empirical evaluation of convergence

Alternating Direction Method of Multipliers (ADMM) has become a widely u...
07/10/2019 ∙ by Andre Goncalves, et al. ∙ 3 ∙ share

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Video Storytelling

Bridging vision and natural language is a longstanding goal in computer ...
07/25/2018 ∙ by Junnan Li, et al. ∙ 2 ∙ share

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On Lipschitz Bounds of General Convolutional Neural Networks

Many convolutional neural networks (CNNs) have a feed-forward structure....
08/04/2018 ∙ by Dongmian Zou, et al. ∙ 2 ∙ share

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Solving Jigsaw Puzzles By The Graph Connection Laplacian

We propose a novel mathematical framework to address the problem of auto...
11/07/2018 ∙ by Vahan Huroyan, et al. ∙ 2 ∙ share

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On the Convergence of SARAH and Beyond

The main theme of this work is a unifying algorithm, abbreviated as L2S,...
06/05/2019 ∙ by Bingcong Li, et al. ∙ 1 ∙ share

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Fisher information regularization schemes for Wasserstein gradient flows

We propose a variational scheme for computing Wasserstein gradient flows...
07/03/2019 ∙ by Wuchen Li, et al. ∙ 1 ∙ share

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Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients

The present paper develops a novel aggregated gradient approach for dist...
09/17/2019 ∙ by Jun Sun, et al. ∙ 1 ∙ share

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Inverse Problems, Deep Learning, and Symmetry Breaking

In many physical systems, inputs related by intrinsic system symmetries ...
03/20/2020 ∙ by Kshitij Tayal, et al. ∙ 1 ∙ share

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Globally Optimal Joint Uplink Base Station Association and Beamforming

The joint base station (BS) association and beamforming problem has been...
12/15/2015 ∙ by Wei Liu, et al. ∙ 0 ∙ share

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Tensors, Learning, and 'Kolmogorov Extension' for Finite-alphabet Random Vectors

Estimating the joint probability mass function (PMF) of a set of random ...
12/01/2017 ∙ by Nikos Kargas, et al. ∙ 0 ∙ share

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3D Semantic Trajectory Reconstruction from 3D Pixel Continuum

This paper presents a method to reconstruct dense semantic trajectory st...
12/04/2017 ∙ by Jae Shin Yoon, et al. ∙ 0 ∙ share

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Kernel-based Inference of Functions over Graphs

The study of networks has witnessed an explosive growth over the past de...
11/28/2017 ∙ by Vassilis N. Ioannidis, et al. ∙ 0 ∙ share

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Adversary Detection in Neural Networks via Persistent Homology

We outline a detection method for adversarial inputs to deep neural netw...
11/28/2017 ∙ by Thomas Gebhart, et al. ∙ 0 ∙ share

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Inference of Spatio-Temporal Functions over Graphs via Multi-Kernel Kriged Kalman Filtering

Inference of space-time varying signals on graphs emerges naturally in a...
11/25/2017 ∙ by Vassilis N. Ioannidis, et al. ∙ 0 ∙ share

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Machine Learning for the Geosciences: Challenges and Opportunities

Geosciences is a field of great societal relevance that requires solutio...
11/13/2017 ∙ by Anuj Karpatne, et al. ∙ 0 ∙ share

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Kullback-Leibler Principal Component for Tensors is not NP-hard

We study the problem of nonnegative rank-one approximation of a nonnegat...
11/21/2017 ∙ by Kejun Huang, et al. ∙ 0 ∙ share

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On Convergence of Epanechnikov Mean Shift

Epanechnikov Mean Shift is a simple yet empirically very effective algor...
11/20/2017 ∙ by Kejun Huang, et al. ∙ 0 ∙ share

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Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling

This paper introduces a novel framework for learning data science models...
10/31/2017 ∙ by Anuj Karpatne, et al. ∙ 0 ∙ share

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