This effect is reflected in the results where the impact of stochastic correlation on calculated CVA is substantial when compared to the case when a high constant correlation is assumed between exposure and credit.
Successful real-world deployment of legged robots would require them to adapt in real-time to unseen scenarios like changing terrains, changing payloads, wear and tear.
A CNN-based classifier was developed to identify body regions in CT and MRI.
We propose an end-to-end real time framework to generate high resolution graphics grade textured 3D map of urban environment.
The framework can quickly and incrementally learn novel items in an online manner by real-time data acquisition and generating corresponding ground truths on its own.
In the overall process, first, shape histogram of a sample surface (e. g. planar) is computed, which captures the profile of surface normals around a point in form of a probability distribution.
In this work, we propose an information theory based framework DeepMI to train deep neural networks (DNN) using Mutual Information (MI).
In this work, we present a pragmatic approach to enable unmanned aerial vehicle (UAVs) to autonomously perform highly complicated tasks of object pick and place.
The current methods to explain the predictions of a pre-trained model rely on gradient information, often resulting in saliency maps that focus on the foreground object as a whole.
In this paper, we present a novel unsupervised learning based algorithm for the selection of feasible grasp regions.
The challenge that the community sets as a benchmark is usually the challenge that the community eventually solves.
We demonstrate our proposed approach in context of navigation, and show that we can successfully learn consistent and diverse visuomotor subroutines from passive egocentric videos.
FastRNN addresses these limitations by adding a residual connection that does not constrain the range of the singular values explicitly and has only two extra scalar parameters.
Extraction of complementary information from the object environment via multiple features and adaption to the target's appearance variations are the key problems of this work.
Equipped with this abstraction, a second network observes the world and decides how to act to retrace the path under noisy actuation and a changing environment.
Particle Filter(PF) is used extensively for estimation of target Non-linear and Non-gaussian state.
This paper develops a novel tree-based algorithm, called Bonsai, for efficient prediction on IoT devices – such as those based on the Arduino Uno board having an 8 bit ATmega328P microcontroller operating at 16 MHz with no native floating point support, 2 KB RAM and 32 KB read-only flash.
Such applications demand prediction models with small storage and computational complexity that do not compromise significantly on accuracy.
For conversion of speech into English text HTK and Julius tools have been used and for conversion of English text query into SQL query we have implemented a System which uses rule based translation to translate English Language Query into SQL Query.