To validate the proposed approach, we train a BiGAN with the proposed training approach to detect taxpayers, who are manipulating their tax returns.
For demonstration, the experiments are conducted with Graph convolutional neural network(GCNN) architecture, however, the proposed framework is easily amenable to any existing GNN architecture.
Deep learning researchers have a keen interest in proposing new novel activation functions that can boost neural network performance.
A good choice of activation function can have significant consequences in improving network performance.
The instrument addresses three technical challenges: it delivers targeted illumination to specified regions of the animal's body such as its head or tail; it automatically delivers stimuli triggered upon the animal's behavior; and it achieves high throughput by targeting many animals simultaneously.
An activation function is a crucial component of a neural network that introduces non-linearity in the network.
In this work, we describe our system submission to the SemEval 2021 Task 11: NLP Contribution Graph Challenge.
We use multi-class classification models for the categorical outcome variable, cyclone grade, and regression models for MSWS as it is a continuous variable.
The model takes as input the best track data of cyclone consisting of its location, pressure, sea surface temperature, and intensity for certain hours (from 12 to 36 hours) anytime during the course of the cyclone as a time series and then provide predictions with high accuracy.
Landfall of a tropical cyclone is the event when it moves over the land after crossing the coast of the ocean.
We study the process of dispersion of low-regularity solutions to the Schr\"odinger equation using fractional weights (observables).
Analysis of PDEs 35J10, 35B99
In recent years, several novel activation functions arising from these basic functions have been proposed, which have improved accuracy in some challenging datasets.
Deep learning at its core, contains functions that are composition of a linear transformation with a non-linear function known as activation function.
In the last 150 years, CO2 concentration in the atmosphere has increased from 280 parts per million to 400 parts per million.
This is a Bayesian framework and the results show a remarkable similarity to natural questions as validated by a human study.
Then we introduce a unified graph learning framework, lying at the integration of the spectral properties of the Laplacian matrix with Gaussian graphical modeling that is capable of learning structures of a large class of graph families.
Different from a number of existing approaches, however, the proposed framework is flexible enough to incorporate a class of non-convex objective functions, allow distributed operation with and without a fusion center, and include variance reduced methods as special cases.
Then we develop an optimization framework that leverages graph learning with specific structures via spectral constraints on graph matrices.
Generating natural questions from an image is a semantic task that requires using visual and language modality to learn multimodal representations.
This research proposed a new algorithm for automatic live FED using radial basis function; Haar discrete wavelet transform and Gray-level difference method is used for feature extraction and classification.
One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far.
Multidimensional scaling (MDS) is a popular dimensionality reduction techniques that has been widely used for network visualization and cooperative localization.
Our method, when tested on a 25 node Hadoop cluster shows 66\% decrease in execution time of Hadoop jobs on an average, when compared to the default configuration.
ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for both benchmark functions and real world problems.