AIMC Topic: Neural Networks, Computer

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Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations.

Cognitive science
Decades of psychological research have been aimed at modeling how people learn features and categories. The empirical validation of these theories is often based on artificial stimuli with simple representations. Recently, deep neural networks have r...

The Vapnik-Chervonenkis dimension of graph and recursive neural networks.

Neural networks : the official journal of the International Neural Network Society
The Vapnik-Chervonenkis dimension (VC-dim) characterizes the sample learning complexity of a classification model and it is often used as an indicator for the generalization capability of a learning method. The VC-dim has been studied on common feed-...

Sign backpropagation: An on-chip learning algorithm for analog RRAM neuromorphic computing systems.

Neural networks : the official journal of the International Neural Network Society
Currently, powerful deep learning models usually require significant resources in the form of processors and memory, which leads to very high energy consumption. The emerging resistive random access memory (RRAM) has shown great potential for constru...

Kyasanur Forest Disease Classification Framework Using Novel Extremal Optimization Tuned Neural Network in Fog Computing Environment.

Journal of medical systems
Kyasanur Forest Disease (KFD) is a life-threatening tick-borne viral infectious disease endemic to South Asia and has been taking so many lives every year in the past decade. But recently, this disease has been witnessed in other regions to a large e...

Extracting Biomedical Events with Parallel Multi-Pooling Convolutional Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Biomedical event extraction is important for medical research and disease prevention, which has attracted much attention in recent years. Traditionally, most of the state-of-the-art systems have been based on shallow machine learning methods, which r...

Single-view 2D CNNs with fully automatic non-nodule categorization for false positive reduction in pulmonary nodule detection.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In pulmonary nodule detection, the first stage, candidate detection, aims to detect suspicious pulmonary nodules. However, detected candidates include many false positives and thus in the following stage, false positive redu...

Predicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology.

Computers in biology and medicine
Hospital readmission is one of the critical metrics used for measuring the performance of hospitals. The HITECH Act imposes penalties when patients are readmitted to hospitals if they are diagnosed with one of the six conditions mentioned in the Act....

Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network.

Sensors (Basel, Switzerland)
Wearable inertial measurement unit (IMU) sensors are powerful enablers for acquisition of motion data. Specifically, in human activity recognition (HAR), IMU sensor data collected from human motion are categorically combined to formulate datasets tha...

Synthesis of Patient-Specific Transmission Data for PET Attenuation Correction for PET/MRI Neuroimaging Using a Convolutional Neural Network.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Attenuation correction is a notable challenge associated with simultaneous PET/MRI, particularly in neuroimaging, where sharp boundaries between air and bone volumes exist. This challenge leads to concerns about the visual and, more specifically, qua...