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07/07/2023
Robot Motion Prediction by Channel State Information
Autonomous robotic systems have gained a lot of attention, in recent yea...
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10/11/2022
Joint Human Orientation-Activity Recognition Using WiFi Signals for Human-Machine Interaction
WiFi sensing is an important part of the new WiFi 802.11bf standard, whi...
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03/07/2022
S-Rocket: Selective Random Convolution Kernels for Time Series Classification
Random convolution kernel transform (Rocket) is a fast, efficient, and n...
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01/23/2022
LiteHAR: Lightweight Human Activity Recognition from WiFi Signals with Random Convolution Kernels
Anatomical movements of the human body can change the channel state info...
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02/25/2021
A Framework For Pruning Deep Neural Networks Using Energy-Based Models
A typical deep neural network (DNN) has a large number of trainable para...
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02/10/2021
Pruning of Convolutional Neural Networks Using Ising Energy Model
Pruning is one of the major methods to compress deep neural networks. In...
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02/09/2021
A Real-World Demonstration of Machine Learning Generalizability: Intracranial Hemorrhage Detection on Head CT
Machine learning (ML) holds great promise in transforming healthcare. Wh...
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10/26/2020
Deep Sequential Learning for Cervical Spine Fracture Detection on Computed Tomography Imaging
Fractures of the cervical spine are a medical emergency and may lead to ...
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06/07/2020
EDropout: Energy-Based Dropout and Pruning of Deep Neural Networks
Dropout is a well-known regularization method by sampling a sub-network ...
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04/25/2019
Survey of Dropout Methods for Deep Neural Networks
Dropout methods are a family of stochastic techniques used in neural net...
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02/07/2019
Ising-Dropout: A Regularization Method for Training and Compression of Deep Neural Networks
Overfitting is a major problem in training machine learning models, spec...
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09/24/2018
Cylindrical Transform: 3D Semantic Segmentation of Kidneys With Limited Annotated Images
In this paper, we propose a novel technique for sampling sequential imag...
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12/29/2017
Recent Advances in Recurrent Neural Networks
Recurrent neural networks (RNNs) are capable of learning features and lo...
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11/08/2017
Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks
Medical datasets are often highly imbalanced, over representing common m...
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09/08/2017
Opposition based Ensemble Micro Differential Evolution
Differential evolution (DE) algorithm with a small population size is ca...
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08/24/2017
Interpretation of Mammogram and Chest X-Ray Reports Using Deep Neural Networks - Preliminary Results
Radiology reports are an important means of communication between radiol...
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08/14/2017
Image Augmentation using Radial Transform for Training Deep Neural Networks
Deep learning models have a large number of free parameters that must be...
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08/07/2017
A Convolutional Neural Network for Search Term Detection
Pathfinding in hospitals is challenging for patients, visitors, and even...
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02/13/2016
Learning Over Long Time Lags
The advantage of recurrent neural networks (RNNs) in learning dependenci...
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12/25/2015
Diversity Enhancement for Micro-Differential Evolution
The differential evolution (DE) algorithm suffers from high computationa...
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11/09/2015
Deep Recurrent Neural Networks for Sequential Phenotype Prediction in Genomics
In analyzing of modern biological data, we are often dealing with ill-po...
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04/28/2015
Combined A*-Ants Algorithm: A New Multi-Parameter Vehicle Navigation Scheme
In this paper a multi-parameter A*(A- star)-ants based algorithm is prop...
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04/28/2015
