University of Florida

∙ Gainesville , United States ∙ 0 followers

The University of Florida is a public land-grant, sea-grant, and space-grant research university in Gainesville, Florida.

No-Trick (Treat) Kernel Adaptive Filtering using Deterministic Features

Kernel methods form a powerful, versatile, and theoretically-grounded un...
12/10/2019 ∙ by Kan Li, et al. ∙ 44 ∙ share

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Fast Estimation of Information Theoretic Learning Descriptors using Explicit Inner Product Spaces

Kernel methods form a theoretically-grounded, powerful and versatile fra...
01/01/2020 ∙ by Kan Li, et al. ∙ 19 ∙ share

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MTLE: A Multitask Learning Encoder of Visual Feature Representations for Video and Movie Description

Learning visual feature representations for video analysis is a daunting...
09/19/2018 ∙ by Oliver Nina, et al. ∙ 18 ∙ share

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Machine Learning Explanations to Prevent Overtrust in Fake News Detection

Combating fake news and misinformation propagation is a challenging task...
07/24/2020 ∙ by Sina Mohseni, et al. ∙ 16 ∙ share

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An Exact Reformulation of Feature-Vector-based Radial-Basis-Function Networks for Graph-based Observations

Radial-basis-function networks are traditionally defined for sets of vec...
01/22/2019 ∙ by Isaac J. Sledge, et al. ∙ 14 ∙ share

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Comparison of Hand-held WEMI Target Detection Algorithms

Wide-band Electromagnetic Induction Sensors (WEMI) have been used for a ...
03/22/2019 ∙ by Connor H. McCurley, et al. ∙ 10 ∙ share

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Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition

Explainable machine learning and artificial intelligence models have bee...
05/05/2020 ∙ by Mahsan Nourani, et al. ∙ 10 ∙ share

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Overcoming Small Minirhizotron Datasets Using Transfer Learning

Minirhizotron technology is widely used for studying the development of ...
03/22/2019 ∙ by Weihuang Xu, et al. ∙ 8 ∙ share

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Investigation of Initialization Strategies for the Multiple Instance Adaptive Cosine Estimator

Sensors which use electromagnetic induction (EMI) to excite a response i...
04/30/2019 ∙ by James Bocinsky, et al. ∙ 8 ∙ share

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Histogram Layers for Texture Analysis

We present a histogram layer for artificial neural networks (ANNs). An e...
01/01/2020 ∙ by Joshua Peeples, et al. ∙ 8 ∙ share

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Intelligent Intersection: Two-Stream Convolutional Networks for Real-time Near Accident Detection in Traffic Video

In Intelligent Transportation System, real-time systems that monitor and...
01/04/2019 ∙ by Xiaohui Huang, et al. ∙ 6 ∙ share

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A Convolutional Forward and Back-Projection Model for Fan-Beam Geometry

Iterative methods for tomographic image reconstruction have great potent...
07/24/2019 ∙ by Kai Zhang, et al. ∙ 6 ∙ share

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Tuning-Free Disentanglement via Projection

In representation learning and non-linear dimension reduction, there is ...
06/27/2019 ∙ by Yue Bai, et al. ∙ 5 ∙ share

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Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM

We present a multiple instance learning class activation map (MIL-CAM) a...
07/30/2020 ∙ by Guohao Yu, et al. ∙ 5 ∙ share

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A Unified Framework for Multiclass and Multilabel Support Vector Machines

We propose a novel integrated formulation for multiclass and multilabel ...
03/25/2020 ∙ by Hoda Shajari, et al. ∙ 1 ∙ share

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Detectability of Symbol Manipulation by an Amplify-and-Forward Relay

This paper studies the problem of detecting a potential malicious relay ...
05/11/2012 ∙ by Eric Graves, et al. ∙ 0 ∙ share

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A coding approach to guarantee information integrity against a Byzantine relay

This paper presents a random coding scheme with which two nodes can exch...
02/01/2013 ∙ by Eric Graves, et al. ∙ 0 ∙ share

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Equating the achievable exponent region to the achievable entropy region by partitioning the source

In this paper we investigate the image size characterization problem. We...
04/25/2014 ∙ by Eric Graves, et al. ∙ 0 ∙ share

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Equal-image-size source partitioning: Creating strong Fano's inequalities for multi-terminal discrete memoryless channels

This paper introduces equal-image-size source partitioning, a new tool f...
12/02/2015 ∙ by Eric Graves, et al. ∙ 0 ∙ share

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Keyless authentication in the presence of a simultaneously transmitting adversary

If Alice must communicate with Bob over a channel shared with the advers...
09/29/2016 ∙ by Eric Graves, et al. ∙ 0 ∙ share

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Wiretap channel capacity: Secrecy criteria, strong converse, and phase change

This paper employs equal-image-size source partitioning techniques to de...
01/25/2017 ∙ by Eric Graves, et al. ∙ 0 ∙ share

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Learning Pain from Action Unit Combinations: A Weakly Supervised Approach via Multiple Instance Learning

Facial pain expression is an important modality for assessing pain, espe...
12/05/2017 ∙ by Zhanli Chen, et al. ∙ 0 ∙ share

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Multiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection

The Multiple Instance Hybrid Estimator for discriminative target charact...
10/31/2017 ∙ by Changzhe Jiao, et al. ∙ 0 ∙ share

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Quantized Minimum Error Entropy Criterion

Comparing with traditional learning criteria, such as mean square error ...
10/11/2017 ∙ by Badong Chen, et al. ∙ 0 ∙ share

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A Gaussian mixture model representation of endmember variability in hyperspectral unmixing

Hyperspectral unmixing while considering endmember variability is usuall...
09/29/2017 ∙ by Yuan Zhou, et al. ∙ 0 ∙ share

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Possibilistic Fuzzy Local Information C-Means for Sonar Image Segmentation

Side-look synthetic aperture sonar (SAS) can produce very high quality i...
09/28/2017 ∙ by Alina Zare, et al. ∙ 0 ∙ share

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Tensor Product Generation Networks

We present a new tensor product generation network (TPGN) that generates...
09/26/2017 ∙ by Qiuyuan Huang, et al. ∙ 0 ∙ share

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Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing

In a streaming environment, there is often a need for statistical predic...
07/25/2017 ∙ by Shujian Yu, et al. ∙ 0 ∙ share

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Identifying the Absorption Bump with Deep Learning

The pervasive interstellar dust grains provide significant insights to u...
11/17/2015 ∙ by Min Li, et al. ∙ 0 ∙ share

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Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour Detection

The fast growing deep learning technologies have become the main solutio...
08/24/2017 ∙ by Zizhao Zhang, et al. ∙ 0 ∙ share

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Large-scale Artificial Neural Network: MapReduce-based Deep Learning

Faced with continuously increasing scale of data, original back-propagat...
10/09/2015 ∙ by Kairan Sun, et al. ∙ 0 ∙ share

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Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms

A multiple instance dictionary learning approach, Dictionary Learning us...
06/11/2017 ∙ by Changzhe Jiao, et al. ∙ 0 ∙ share

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Sensitivity Analysis for additive STDP rule

Spike Timing Dependent Plasticity (STDP) is a Hebbian like synaptic lear...
02/28/2015 ∙ by Subhajit Sengupta, et al. ∙ 0 ∙ share

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Learning Pixel-Distribution Prior with Wider Convolution for Image Denoising

In this work, we explore an innovative strategy for image denoising by u...
07/28/2017 ∙ by Peng Liu, et al. ∙ 0 ∙ share

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Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior

We explore an innovative strategy for image denoising by using convoluti...
07/17/2017 ∙ by Peng Liu, et al. ∙ 0 ∙ share

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DeepCancer: Detecting Cancer through Gene Expressions via Deep Generative Learning

Transcriptional profiling on microarrays to obtain gene expressions has ...
12/09/2016 ∙ by Rajendra Rana Bhat, et al. ∙ 0 ∙ share

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MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network

The inability to interpret the model prediction in semantically and visu...
07/08/2017 ∙ by Zizhao Zhang, et al. ∙ 0 ∙ share

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Robustness of Maximum Correntropy Estimation Against Large Outliers

The maximum correntropy criterion (MCC) has recently been successfully a...
03/23/2017 ∙ by Badong Chen, et al. ∙ 0 ∙ share

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Quaternion Based Camera Pose Estimation From Matched Feature Points

We present a novel solution to the camera pose estimation problem, where...
04/09/2017 ∙ by Kaveh Fathian, et al. ∙ 0 ∙ share

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Adversarial Examples: Attacks and Defenses for Deep Learning

With rapid progress and great successes in a wide spectrum of applicatio...
12/19/2017 ∙ by Xiaoyong Yuan, et al. ∙ 0 ∙ share

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Kernel Risk-Sensitive Loss: Definition, Properties and Application to Robust Adaptive Filtering

Nonlinear similarity measures defined in kernel space, such as correntro...
08/01/2016 ∙ by Badong Chen, et al. ∙ 0 ∙ share

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Microscopic Muscle Image Enhancement

We propose a robust image enhancement algorithm dedicated for muscle fib...
12/17/2016 ∙ by Xiangfei Kong, et al. ∙ 0 ∙ share

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Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks

We propose a convolutional recurrent neural network, with Winner-Take-Al...
10/31/2016 ∙ by Eder Santana, et al. ∙ 0 ∙ share

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Generalized Uniformly Optimal Methods for Nonlinear Programming

In this paper, we present a generic framework to extend existing uniform...
08/29/2015 ∙ by Saeed Ghadimi, et al. ∙ 0 ∙ share

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An optimal randomized incremental gradient method

In this paper, we consider a class of finite-sum convex optimization pro...
07/08/2015 ∙ by Guanghui Lan, et al. ∙ 0 ∙ share

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Multiple Instance Hyperspectral Target Characterization

In this paper, two methods for multiple instance target characterization...
06/20/2016 ∙ by Alina Zare, et al. ∙ 0 ∙ share

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An efficient Exact-PGA algorithm for constant curvature manifolds

Manifold-valued datasets are widely encountered in many computer vision ...
03/13/2016 ∙ by Rudrasis Chakraborty, et al. ∙ 0 ∙ share

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A Grassmannian Graph Approach to Affine Invariant Feature Matching

In this work, we present a novel and practical approach to address one o...
01/28/2016 ∙ by Mark Moyou, et al. ∙ 0 ∙ share

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Spatial Scaling of Satellite Soil Moisture using Temporal Correlations and Ensemble Learning

A novel algorithm is developed to downscale soil moisture (SM), obtained...
01/21/2016 ∙ by Subit Chakrabarti, et al. ∙ 0 ∙ share

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Disaggregation of SMAP L3 Brightness Temperatures to 9km using Kernel Machines

In this study, a machine learning algorithm is used for disaggregation o...
01/20/2016 ∙ by Subit Chakrabarti, et al. ∙ 0 ∙ share

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