Search Results for author: Soumi Chaki

Found 9 papers, 0 papers with code

Communication Trade-offs in Federated Learning of Spiking Neural Networks

no code implementations27 Feb 2023 Soumi Chaki, David Weinberg, Ayca Özcelikkale

We investigate federated learning for training multiple SNNs at clients when two mechanisms reduce the uplink communication cost: i) random masking of the model updates sent from the clients to the server; and ii) client dropouts where some clients do not send their updates to the server.

Federated Learning

Development of a hybrid learning system based on SVM, ANFIS and domain knowledge: DKFIS

no code implementations2 Dec 2016 Soumi Chaki, Aurobinda Routray, William K. Mohanty, Mamata Jenamani

The classification results have been further fine-tuned applying expert knowledge based on the relationship among predictor variables i. e. well logs and target variable - oil saturation.

A novel multiclassSVM based framework to classify lithology from well logs: a real-world application

no code implementations2 Dec 2016 Soumi Chaki, Aurobinda Routray, William K. Mohanty, Mamata Jenamani

Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifiers, and finally the classification results are combined following either the one-against-one or one-against-all strategies.

Classification General Classification

Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach

no code implementations23 Sep 2015 Akhilesh K Verma, Soumi Chaki, Aurobinda Routray, William K. Mohanty, Mamata Jenamani

Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability.

Well Tops Guided Prediction of Reservoir Properties using Modular Neural Network Concept A Case Study from Western Onshore, India

no code implementations23 Sep 2015 Soumi Chaki, Akhilesh K Verma, Aurobinda Routray, William K. Mohanty, Mamata Jenamani

The data set used in this study comprising three seismic attributes and well log data from eight wells, is acquired from a western onshore hydrocarbon field of India.

A Novel Pre-processing Scheme to Improve the Prediction of Sand Fraction from Seismic Attributes using Neural Networks

no code implementations23 Sep 2015 Soumi Chaki, Aurobinda Routray, William K. Mohanty

The network yielding satisfactory performance in the validation stage is used to predict lithological properties from seismic attributes throughout a given volume.

BIG-bench Machine Learning

Reservoir Characterization: A Machine Learning Approach

no code implementations15 Jun 2015 Soumi Chaki

Reservoir Characterization (RC) can be defined as the act of building a reservoir model that incorporates all the characteristics of the reservoir that are pertinent to its ability to store hydrocarbons and also to produce them. It is a difficult problem due to non-linear and heterogeneous subsurface properties and associated with a number of complex tasks such as data fusion, data mining, formulation of the knowledge base, and handling of the uncertainty. This present work describes the development of algorithms to obtain the functional relationships between predictor seismic attributes and target lithological properties.

BIG-bench Machine Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.