University of Minnesota

research

∙ 07/12/2020

Fisher Auto-Encoders

It has been conjectured that the Fisher divergence is more robust to mod...

Khalil Elkhalil, et al. ∙

research

∙ 04/01/2019

Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction

Following the success of deep learning in a wide range of applications, ...

Florian Knoll, et al. ∙

research

∙ 12/22/2020

Hybrid Federated Learning: Algorithms and Implementation

Federated learning (FL) is a recently proposed distributed machine learn...

Xinwei Zhang, et al. ∙

research

∙ 10/09/2019

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

In robotic grasping tasks, robots usually avoid any collisions with the ...

Hengyue Liang, et al. ∙

research

∙ 01/31/2020

Physics-Guided Deep Neural Networks for PowerFlow Analysis

Solving power flow (PF) equations is the basis of power flow analysis, w...

Xinyue Hu, et al. ∙

research

∙ 02/13/2020

Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization

Domain generalization is the problem of machine learning when the traini...

Vikas K. Garg, et al. ∙

research

∙ 09/14/2019

Automated Multiclass Cardiac Volume Segmentation and Model Generation

Many strides have been made in semantic segmentation of multiple classes...

Erik Gaasedelen, et al. ∙

research

∙ 12/24/2019

A Communication Efficient Vertical Federated Learning Framework

One critical challenge for applying today's Artificial Intelligence (AI)...

Yang Liu, et al. ∙

research

∙ 11/15/2019

Human Annotations Improve GAN Performances

Generative Adversarial Networks (GANs) have shown great success in many ...

Juanyong Duan, et al. ∙

research

∙ 11/18/2021

Deep IDA: A Deep Learning Method for Integrative Discriminant Analysis of Multi-View Data with Feature Ranking – An Application to COVID-19 severity

COVID-19 severity is due to complications from SARS-Cov-2 but the clinic...

Jiuzhou Wang, et al. ∙

research

∙ 09/11/2019

A Deep Learning Approach to Grasping the Invisible

We introduce a new problem named "grasping the invisible", where a robot...

Yang Yang, et al. ∙

research

∙ 02/04/2020

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

In this paper, we introduce and tackle the simultaneous enhancement and ...

Md Jahidul Islam, et al. ∙

research

∙ 07/19/2022

Self-Supervised Interactive Object Segmentation Through a Singulation-and-Grasping Approach

Instance segmentation with unseen objects is a challenging problem in un...

Houjian Yu, et al. ∙

research

∙ 05/03/2019

Stability and Generalization of Graph Convolutional Neural Networks

Inspired by convolutional neural networks on 1D and 2D data, graph convo...

Saurabh Verma, et al. ∙

research

∙ 03/03/2021

Land Cover Mapping in Limited Labels Scenario: A Survey

Land cover mapping is essential for monitoring global environmental chan...

Rahul Ghosh, et al. ∙

research

∙ 01/15/2019

Algorithms for ℓ_p-based semi-supervised learning on graphs

We develop fast algorithms for solving the variational and game-theoreti...

Mauricio Flores Rios, et al. ∙

research

∙ 02/17/2020

Causal Feature Discovery through Strategic Modification

We consider an online regression setting in which individuals adapt to t...

Yahav Bechavod, et al. ∙

research

∙ 07/27/2020

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

In this work we propose a new supervised learning method for temporally-...

Andrew Stephan, et al. ∙

research

∙ 06/11/2018

Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks

In this paper, we propose Generative Adversarial Network (GAN) architect...

Yash Upadhyay, et al. ∙

research

∙ 12/18/2018

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...

Karan Aggarwal, et al. ∙

research

∙ 11/16/2021

CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing

As mobile networks proliferate, we are experiencing a strong diversifica...

Yongshuai Liu, et al. ∙

research

∙ 05/05/2022

The Batch Artifact Scanning Protocol: A new method using computed tomography (CT) to rapidly create three-dimensional models of objects from large collections en masse

Within anthropology, the use of three-dimensional (3D) imaging has becom...

Katrina Yezzi-Woodley, et al. ∙

research

∙ 03/08/2019

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...

Hongmei Wang, et al. ∙

research

∙ 06/20/2021

Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density Estimation

Learning generative probabilistic models is a core problem in machine le...

Magda Amiridi, et al. ∙

research

∙ 05/20/2022

Using machine learning on new feature sets extracted from 3D models of broken animal bones to classify fragments according to break agent

Distinguishing agents of bone modification at paleoanthropological sites...

Katrina Yezzi-Woodley, et al. ∙

research

∙ 06/22/2019

Unsupervised Ensemble Classification with Dependent Data

Ensemble learning, the machine learning paradigm where multiple algorith...

Panagiotis A. Traganitis, et al. ∙

research

∙ 07/09/2019

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...

Zixing Wang, et al. ∙

research

∙ 01/30/2020

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

Images of heavily occluded objects in cluttered scenes, such as fruit cl...

Wenbo Dong, et al. ∙

research

∙ 08/16/2021

Weakly Supervised Classification Using Group-Level Labels

In many applications, finding adequate labeled data to train predictive ...

Guruprasad Nayak, et al. ∙

research

∙ 06/09/2022

Privacy Leakage in Text Classification: A Data Extraction Approach

Recent work has demonstrated the successful extraction of training data ...

Adel Elmahdy, et al. ∙

research

∙ 08/14/2018

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

Neural networks with ReLU activations have achieved great empirical succ...

Gang Wang, et al. ∙

research

∙ 09/14/2018

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

Understanding the coordinated activity underlying brain computations req...

Tong Wu, et al. ∙

research

∙ 06/24/2019

A Stochastic Composite Gradient Method with Incremental Variance Reduction

We consider the problem of minimizing the composition of a smooth (nonco...

Junyu Zhang, et al. ∙

research

∙ 01/10/2020

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

New mobility concepts are at the forefront of research and innovation in...

Brionna Davis, et al. ∙

research

∙ 07/30/2020

Unsupervised Continuous Object Representation Networks for Novel View Synthesis

Novel View Synthesis (NVS) is concerned with the generation of novel vie...

Nicolai Häni, et al. ∙

research

∙ 05/02/2021

Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping

The availability of massive earth observing satellite data provide huge ...

Rahul Ghosh, et al. ∙

research

∙ 10/08/2021

Heavy Ball Momentum for Conditional Gradient

Conditional gradient, aka Frank Wolfe (FW) algorithms, have well-documen...

Bingcong Li, et al. ∙

research

∙ 11/25/2021

Characteristic Neural Ordinary Differential Equations

We propose Characteristic Neural Ordinary Differential Equations (C-NODE...

Xingzi Xu, et al. ∙

research

∙ 07/24/2019

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

While stochastic gradient descent (SGD) and variants have been surprisin...

Xinyan Li, et al. ∙

research

∙ 07/25/2019

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

Improvements in data-capture and face modeling techniques have enabled u...

Jae Shin Yoon, et al. ∙

research

∙ 08/16/2021

Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping

Collecting large annotated datasets in Remote Sensing is often expensive...

Rahul Ghosh, et al. ∙

research

∙ 06/10/2022

Weighted Ensembles for Active Learning with Adaptivity

Labeled data can be expensive to acquire in several application domains,...

Konstantinos D. Polyzos, et al. ∙

research

∙ 12/04/2018

Multiview Cross-supervision for Semantic Segmentation

This paper presents a semi-supervised learning framework for a customize...

Yuan Yao, et al. ∙

research

∙ 02/06/2019

Equal Opportunity in Online Classification with Partial Feedback

We study an online classification problem with partial feedback in which...

Yahav Bechavod, et al. ∙

research

∙ 06/10/2019

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

Graph neural networks (GNNs) which apply the deep neural networks to gra...

Kaidi Xu, et al. ∙

research

∙ 08/13/2019

Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics

Continuous control and planning remains a major challenge in robotics an...

Saurabh Daptardar, et al. ∙

research

∙ 03/26/2020

Image Generation Via Minimizing Fréchet Distance in Discriminator Feature Space

For a given image generation problem, the intrinsic image manifold is of...

Khoa D. Doan, et al. ∙

research

∙ 10/20/2021

Distributionally Robust Semi-Supervised Learning Over Graphs

Semi-supervised learning (SSL) over graph-structured data emerges in man...

Alireza Sadeghi, et al. ∙

research

∙ 07/10/2019

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

Alternating Direction Method of Multipliers (ADMM) has become a widely u...

Andre Goncalves, et al. ∙

research

∙ 07/25/2018

Video Storytelling

Bridging vision and natural language is a longstanding goal in computer ...

Junnan Li, et al. ∙

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