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STRUCTURE AWARE HUMAN POSE ESTIMATION USING ADVERSARIAL LEARNING
(2021-05-10)
Pose estimation using Deep Neural Networks (DNNs) has shown outstanding performance in recent years, due to the availability of powerful GPUs and larger training datasets. However, there are still many challenges due to ...
LINK PREDICTION BASED FACE CLUSTERING USING VARIATIONAL ATTENTIONAL GRAPH AUTOENCODER
(2020-12-01)
In this work, we address the problem of clustering faces according to their individual
identities present inherently in the dataset.The current clustering frameworks are either
based on some heuristic method or require ...
DECOUPLING-BASED APPROACH TO CENTRALITY DETECTION IN HETEROGENEOUS MULTILAYER NETWORKS
(2021-08-13)
Graph analysis is one of the techniques widely used for data analysis. It is used extensively on single graphs. Its ability to capture entities and relationships makes it an attractive data model. Search on graphs, such ...
EARLY DETECTION OF GLAUCOMA USING MODIFIED RESIDUAL U-NET CONVOLUTIONAL NEURAL NETWORK
(2020-12-07)
Glaucoma is the second leading cause of blindness all over the world, with
apparently 75 million cases reported worldwide in 2018. If it’s not diagnosed at
an early stage, glaucoma may cause irreversible damage to the optic ...
Using ChebConv and B-Spline GNN models for Solving Unit Commitment and Economic Dispatch in a day ahead Energy Trading Market based on ERCOT Nodal Model
(2020-05-22)
Spectral Convolutions and B-Spline Graph Neural Network techniques have been used in past to learn embeddings in various complex, multidimensional structured knowledge graphs like genetics, social networks, geometric shapes ...
3D SKELETON CONSTRUCTION FROM MULTIPLE CAMERA VIEWS FOR QUANTIFYING GAIT PARAMETERS
(2020-06-05)
Research has shown that human gait characteristics permit inference with respect to different personal and health characteristics and can thus be used as a diagnostic tool. To do this automatically it is important to be ...
Randomized and Evolutionary Approaches to Dataset Characterization, Feature Weighting, and Sampling in K-Nearest Neighbors
(2020-06-05)
K-Nearest Neighbors (KNN) has remained one of the most popular methods for supervised machine learning tasks. However, its performance often depends on the characteristics of the dataset and on appropriate feature scaling. ...
MR_QP: A Scalable Approach To Query Processing on Arbitrary-Size Graphs Using The Map/Reduce Framework
(2020-06-03)
The utility and widespread use of Relational Database Management Systems(RDBMSs) comes not only from its simple, easy-to-understand data model (a relation or a set) but mainly from the ability to write non-procedural queries ...
INTRINSIC CURIOSITY IN REINFORCEMENT LEARNING BY IMPROVING NEXT STATE PREDICTION
(2020-06-03)
In Reinforcement Learning, an agent receives feedback from the environment in the form of an extrinsic reward. It learns to take actions that maximize this extrinsic reward. However, to start learning, the agent needs to ...
MLN-Subdue: Decoupling Approach-Based Substructure Discovery In Multilayer Networks (MLNs)
(2020-06-03)
Substructure discovery is well-researched for single graphs (both simple and attribute) as it is an important component of knowledge discovery for many applications
such as finding the core substructure in a protein, ...