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Found 10 papers, 0 papers with code
Open Stamped Parts Dataset
The real part images, captured from 7 cameras, consist of 7, 980 unlabeled images and 1, 680 labeled images.
Benefits of temporal information for appearance-based gaze estimation
The magnitude of contribution from temporal gaze trace is yet unclear for higher resolution/frame rate imaging systems, in which more detailed information about an eye is captured.
OpenEDS2020: Open Eyes Dataset
We present the second edition of OpenEDS dataset, OpenEDS2020, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted display mounted with two synchronized eye-facing cameras.
OpenEDS: Open Eye Dataset
We present a large scale data set, OpenEDS: Open Eye Dataset, of eye-images captured using a virtual-reality (VR) head mounted display mounted with two synchronized eyefacing cameras at a frame rate of 200 Hz under controlled illumination.
Deep Recurrent Neural Networks for seizure detection and early seizure detection systems
In addition, systems for early seizure detection can lead to the development of new types of intervention systems that are designed to control or shorten the duration of seizure events.
Overcoming Challenges in Fixed Point Training of Deep Convolutional Networks
It is known that training deep neural networks, in particular, deep convolutional networks, with aggressively reduced numerical precision is challenging.
Fixed Point Quantization of Deep Convolutional Networks
In recent years increasingly complex architectures for deep convolution networks (DCNs) have been proposed to boost the performance on image recognition tasks.
Improving performance of recurrent neural network with relu nonlinearity
In recent years significant progress has been made in successfully training recurrent neural networks (RNNs) on sequence learning problems involving long range temporal dependencies.
Hyper-parameter optimization of Deep Convolutional Networks for object recognition
Recently sequential model based optimization (SMBO) has emerged as a promising hyper-parameter optimization strategy in machine learning.
Fast SVM training using approximate extreme points
A linear time algorithm based on convex hulls and extreme points is used to compute the representative set in kernel space.
