
Fisher AutoEncoders
It has been conjectured that the Fisher divergence is more robust to mod...
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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, ...
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Hybrid Federated Learning: Algorithms and Implementation
Federated learning (FL) is a recently proposed distributed machine learn...
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Knowledge Induced Deep QNetwork for a SlidetoWall Object Grasping
In robotic grasping tasks, robots usually avoid any collisions with the ...
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PhysicsGuided Deep Neural Networks for PowerFlow Analysis
Solving power flow (PF) equations is the basis of power flow analysis, w...
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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...
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Automated Multiclass Cardiac Volume Segmentation and Model Generation
Many strides have been made in semantic segmentation of multiple classes...
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A Communication Efficient Vertical Federated Learning Framework
One critical challenge for applying today's Artificial Intelligence (AI)...
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Human Annotations Improve GAN Performances
Generative Adversarial Networks (GANs) have shown great success in many ...
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A Deep Learning Approach to Grasping the Invisible
We introduce a new problem named "grasping the invisible", where a robot...
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Simultaneous Enhancement and SuperResolution of Underwater Imagery for Improved Visual Perception
In this paper, we introduce and tackle the simultaneous enhancement and ...
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Stability and Generalization of Graph Convolutional Neural Networks
Inspired by convolutional neural networks on 1D and 2D data, graph convo...
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Algorithms for ℓ_pbased semisupervised learning on graphs
We develop fast algorithms for solving the variational and gametheoreti...
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Causal Feature Discovery through Strategic Modification
We consider an online regression setting in which individuals adapt to t...
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Supervised Learning in TemporallyCoded Spiking Neural Networks with Approximate Backpropagation
In this work we propose a new supervised learning method for temporally...
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Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks
In this paper, we propose Generative Adversarial Network (GAN) architect...
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Two Birds with One Network: Unifying Failure Event Prediction and Timetofailure Modeling
One of the key challenges in predictive maintenance is to predict the im...
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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...
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Unsupervised Ensemble Classification with Dependent Data
Ensemble learning, the machine learning paradigm where multiple algorith...
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Fast Estimating Pedestrian Moving State Based on Single 2D Body Pose by Shallow Neural Network
Crossing or NotCrossing (C/NC) problem is important to autonomous vehic...
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Ellipse RCNN: Learning to Infer Elliptical Object from Clustering and Occlusion
Images of heavily occluded objects in cluttered scenes, such as fruit cl...
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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...
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Deep Compressive Autoencoder for Action Potential Compression in LargeScale Neural Recording
Understanding the coordinated activity underlying brain computations req...
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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...
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Decentralized Optimization of Vehicle Route Planning – A CrossCity Comparative Study
New mobility concepts are at the forefront of research and innovation in...
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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...
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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...
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SelfSupervised Adaptation of HighFidelity Face Models for Monocular Performance Tracking
Improvements in datacapture and face modeling techniques have enabled u...
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Multiview Crosssupervision for Semantic Segmentation
This paper presents a semisupervised learning framework for a customize...
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Equal Opportunity in Online Classification with Partial Feedback
We study an online classification problem with partial feedback in which...
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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...
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Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics
Continuous control and planning remains a major challenge in robotics an...
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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...
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Twoblock vs. Multiblock ADMM: An empirical evaluation of convergence
Alternating Direction Method of Multipliers (ADMM) has become a widely u...
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Video Storytelling
Bridging vision and natural language is a longstanding goal in computer ...
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On Lipschitz Bounds of General Convolutional Neural Networks
Many convolutional neural networks (CNNs) have a feedforward structure....
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Solving Jigsaw Puzzles By The Graph Connection Laplacian
We propose a novel mathematical framework to address the problem of auto...
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On the Convergence of SARAH and Beyond
The main theme of this work is a unifying algorithm, abbreviated as L2S,...
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Fisher information regularization schemes for Wasserstein gradient flows
We propose a variational scheme for computing Wasserstein gradient flows...
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CommunicationEfficient Distributed Learning via Lazily Aggregated Quantized Gradients
The present paper develops a novel aggregated gradient approach for dist...
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Inverse Problems, Deep Learning, and Symmetry Breaking
In many physical systems, inputs related by intrinsic system symmetries ...
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Globally Optimal Joint Uplink Base Station Association and Beamforming
The joint base station (BS) association and beamforming problem has been...
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Tensors, Learning, and 'Kolmogorov Extension' for Finitealphabet Random Vectors
Estimating the joint probability mass function (PMF) of a set of random ...
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3D Semantic Trajectory Reconstruction from 3D Pixel Continuum
This paper presents a method to reconstruct dense semantic trajectory st...
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Kernelbased Inference of Functions over Graphs
The study of networks has witnessed an explosive growth over the past de...
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Adversary Detection in Neural Networks via Persistent Homology
We outline a detection method for adversarial inputs to deep neural netw...
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Inference of SpatioTemporal Functions over Graphs via MultiKernel Kriged Kalman Filtering
Inference of spacetime varying signals on graphs emerges naturally in a...
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Machine Learning for the Geosciences: Challenges and Opportunities
Geosciences is a field of great societal relevance that requires solutio...
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KullbackLeibler Principal Component for Tensors is not NPhard
We study the problem of nonnegative rankone approximation of a nonnegat...
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On Convergence of Epanechnikov Mean Shift
Epanechnikov Mean Shift is a simple yet empirically very effective algor...
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University of Minnesota
The University of Minnesota, Twin Cities is a public research university in Minneapolis and Saint Paul, Minnesota.