• Publications
  • Influence
Fixed Point Quantization of Deep Convolutional Networks
TLDR
This paper proposes a quantizer design for fixed point implementation of DCNs, formulate and solve an optimization problem to identify optimal fixed point bit-width allocation across DCN layers, and demonstrates that fine-tuning can further enhance the accuracy of fixed point DCNs beyond that of the original floating point model. Expand
Support vector machines for seizure detection in an animal model of chronic epilepsy.
TLDR
It is found that SVDD not only performed better than the other SVM types in terms of highest value of the mean optimality index metric (O⁻) but also gave a more reliable performance across the two EEG datasets. Expand
Improving performance of recurrent neural network with relu nonlinearity
TLDR
This paper offers a simple dynamical systems perspective on weight initialization process, which allows for a modified weight initialization strategy, and shows that this initialization technique leads to successfully training RNNs composed of ReLUs. Expand
OpenEDS: Open Eye Dataset
TLDR
It is anticipated that OpenEDS will create opportunities to researchers in the eye tracking community and the broader machine learning and computer vision community to advance the state of eye-tracking for VR applications. Expand
Fast SVM training using approximate extreme points
TLDR
The AESVM implementation was found to train much faster than the other methods, while its classification accuracy was similar to that of LIBSVM in all cases, and it gave competitively fast classification times. Expand
Deep Recurrent Neural Networks for seizure detection and early seizure detection systems
  • S. Talathi
  • Mathematics, Computer Science
  • ArXiv
  • 10 June 2017
TLDR
A deep learning framework via the use of Gated Recurrent Unit (GRU) RNNs for seizure detection and early seizure detection systems is proposed, which can detect about 98% of seizure events within the first 5 seconds of the overall epileptic seizure duration. Expand
Computational models of epilepsy
TLDR
This review article presents a survey of computational models of epilepsy from the point of view that epilepsy is a dynamical brain disease that is primarily characterized by unprovoked spontaneous epileptic seizures and explores the potential of recently developed optogenetics tools to provide novel avenue for seizure control. Expand
Spike timing dependent plasticity promotes synchrony of inhibitory networks in the presence of heterogeneity
TLDR
Using the method of spike time response curve (STRC), it is shown how iSTDP influences the dynamics of the coupled neurons, such that the pair synchronizes under moderately large heterogeneity in the firing rates. Expand
Non-parametric early seizure detection in an animal model of temporal lobe epilepsy.
The performance of five non-parametric, univariate seizure detection schemes (embedding delay, Hurst scale, wavelet scale, nonlinear autocorrelation and variance energy) were evaluated as a functionExpand
Epilepsy Detection and Monitoring
TLDR
This chapter investigates how wavelets, synchronization, Lyapunov exponents, principal component analysis, and other techniques can help investigators extract information about impending seizures and illustrates how these techniques can be brought together in a closed-loop seizure prevention system. Expand
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