
Robust subsamplingbased sparse Bayesian inference to tackle four challenges (large noise, outliers, data integration, and extrapolation) in the discovery of physical laws from
The derivation of physical laws is a dominant topic in scientific resear...
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Unrestricted Adversarial Attacks for Semantic Segmentation
Semantic segmentation is one of the most impactful applications of machi...
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Horseshoe Regularization for Machine Learning in Complex and Deep Models
Since the advent of the horseshoe priors for regularization, globalloca...
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Multilevel hypothesis testing for populations of heterogeneous networks
In this work, we consider hypothesis testing and anomaly detection on da...
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Fast Deep Learning for Automatic Modulation Classification
In this work, we investigate the feasibility and effectiveness of employ...
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City2City: Translating Place Representations across Cities
Large mobility datasets collected from various sources have allowed us t...
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A TwoStage Approach to FewShot Learning for Image Recognition
This paper proposes a multilayer neural network structure for fewshot ...
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A Comparative Evaluation of SGM Variants (including a New Variant, tMGM) for Dense Stereo Matching
Our goal here is threefold: [1] To present a new densestereo matching a...
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LowPower Computer Vision: Status, Challenges, Opportunities
Computer vision has achieved impressive progress in recent years. Meanwh...
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AdaWISH: Faster Discrete Integration via Adaptive Quantiles
Discrete integration in a high dimensional space of n variables poses fu...
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A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks
In this era of machine learning models, their functionality is being thr...
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TiMDNN: Ternary in Memory accelerator for Deep Neural Networks
The use of lower precision to perform computations has emerged as a popu...
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Classspecific Anchoring Proposal for 3D Object Recognition in LIDAR and RGB Images
Detecting objects in a twodimensional setting is often insufficient in ...
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Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge
The recent advent of `Internet of Things' (IOT) has increased the demand...
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Megapixel PhotonCounting Color Imaging using Quanta Image Sensor
Quanta Image Sensor (QIS) is a singlephoton detector designed for extre...
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Robust datadriven discovery of governing physical laws using a new subsamplingbased sparse Bayesian method to tackle four challenges (large noise, outliers, data integration,
The derivation of physical laws is a dominant topic in scientific resear...
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Interactive Learning for Identifying Relevant Tweets to Support Realtime Situational Awareness
Various domain users are increasingly leveraging realtime social media ...
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Automatic Foreground Extraction using MultiAgent Consensus Equilibrium
While foreground extraction is fundamental to virtual reality systems an...
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MEBF: a fast and efficient Boolean matrix factorization method
Boolean matrix has been used to represent digital information in many fi...
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A New Stereo Benchmarking Dataset for Satellite Images
In order to facilitate further research in stereo reconstruction with mu...
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Flexible Mixture Modeling on Constrained Spaces
This paper addresses challenges in flexibly modeling multimodal data tha...
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Flappy Hummingbird: An Open Source Dynamic Simulation of Flapping Wing Robots and Animals
Insects and hummingbirds exhibit extraordinary flight capabilities and c...
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3D Object Classification via Spherical Projections
In this paper, we introduce a new method for classifying 3D objects. Our...
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Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing
Deep convolutional neural network (DCNN) based supervised learning is a ...
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On Deterministic Sampling Patterns for Robust LowRank Matrix Completion
In this letter, we study the deterministic sampling patterns for the com...
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Deep Neural Network Architectures for Modulation Classification
In this work, we investigate the value of employing deep learning for th...
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Accelerating Convolutional Neural Networks for Continuous Mobile Vision via Cache Reuse
Convolutional Neural Network (CNN) is the stateoftheart algorithm of ...
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From Monte Carlo to Las Vegas: Improving Restricted Boltzmann Machine Training Through Stopping Sets
We propose a Las Vegas transformation of Markov Chain Monte Carlo (MCMC)...
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TraNNsformer: Neural Network Transformation for Memristive Crossbar based Neuromorphic System Design
Implementation of Neuromorphic Systems using post Complementary MetalOx...
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Context Augmentation for Convolutional Neural Networks
Recent enhancements of deep convolutional neural networks (ConvNets) emp...
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VoltageDriven DomainWall Motion based NeuroSynaptic Devices for Dynamic Online Learning
Conventional vonNeumann computing models have achieved remarkable feats...
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DyVEDeep: Dynamic Variable Effort Deep Neural Networks
Deep Neural Networks (DNNs) have advanced the stateoftheart in a vari...
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ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks
A fundamental feature of learning in animals is the "ability to forget" ...
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SparCE: Sparsity aware General Purpose Core Extensions to Accelerate Deep Neural Networks
Deep Neural Networks (DNNs) have emerged as the method of choice for sol...
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The Error Probability of Random Fourier Features is Dimensionality Independent
We show that the error probability of reconstructing kernel matrices fro...
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Convolutional Spike Timing Dependent Plasticity based Feature Learning in Spiking Neural Networks
Braininspired learning models attempt to mimic the cortical architectur...
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RESPARC: A Reconfigurable and EnergyEfficient Architecture with Memristive Crossbars for Deep Spiking Neural Networks
Neuromorphic computing using postCMOS technologies is gaining immense p...
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Multitask Learning using Task Clustering with Applications to Predictive Modeling and GWAS of Plant Varieties
Inferring predictive maps between multiple input and multiple output var...
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Proposal for a LeakyIntegrateFire Spiking Neuron based on MagnetoElectric Switching of Ferromagnets
The efficiency of the human brain in performing classification tasks has...
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Rates of Convergence of Spectral Methods for Graphon Estimation
This paper studies the problem of estimating the grahpon model  the und...
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Attention Tree: Learning Hierarchies of Visual Features for LargeScale Image Recognition
One of the key challenges in machine learning is to design a computation...
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Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications
Statistical analysis (SA) is a complex process to deduce population prop...
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Significance Driven Hybrid 8T6T SRAM for EnergyEfficient Synaptic Storage in Artificial Neural Networks
Multilayered artificial neural networks (ANN) have found widespread util...
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Domain Specific Author Attribution Based on Feedforward Neural Network Language Models
Authorship attribution refers to the task of automatically determining t...
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Stochastic Gradient Descent for Relational Logistic Regression via Partial Network Crawls
Research in statistical relational learning has produced a number of met...
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Learning to see people like people
Humans make complex inferences on faces, ranging from objective properti...
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Recurrent Neural Networks Hardware Implementation on FPGA
Recurrent Neural Networks (RNNs) have the ability to retain memory and l...
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Rank Determination for LowRank Data Completion
Recently, fundamental conditions on the sampling patterns have been obta...
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Coreset Construction via Randomized Matrix Multiplication
Coresets are small sets of points that approximate the properties of a l...
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Flattened Convolutional Neural Networks for Feedforward Acceleration
We present flattened convolutional neural networks that are designed for...
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Purdue University is a public research university in West Lafayette, Indiana, and the flagship campus of the Purdue University system.