Search Results for author: Vinod Kumar Chauhan

Found 13 papers, 8 papers with code

Sample Selection Bias in Machine Learning for Healthcare

1 code implementation13 May 2024 Vinod Kumar Chauhan, Lei Clifton, Achille Salaün, Huiqi Yvonne Lu, Kim Branson, Patrick Schwab, Gaurav Nigam, David A. Clifton

Specifically, we propose two independent networks(T-Net) and a multitasking network (MT-Net) for addressing SSB, where one network/task identifies the target subpopulation which is representative of the study population and the second makes predictions for the identified subpopulation.

Selection bias

GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks

no code implementations22 Mar 2024 Sukhdeep Singh, Anuj Sharma, Vinod Kumar Chauhan

Graph Neural Networks (GNN) have emerged as a popular and standard approach for learning from graph-structured data.

Graph Classification

A Brief Review of Hypernetworks in Deep Learning

1 code implementation12 Jun 2023 Vinod Kumar Chauhan, Jiandong Zhou, Ping Lu, Soheila Molaei, David A. Clifton

They offer a new way to design and train neural networks, and they have the potential to improve the performance of deep learning models on a variety of tasks.

Causal Inference Continual Learning +5

Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation

1 code implementation25 May 2023 Vinod Kumar Chauhan, Jiandong Zhou, Ghadeer Ghosheh, Soheila Molaei, David A. Clifton

To tackle this problem, we propose a deep learning framework based on `\textit{soft weight sharing}' to train ITE learners, enabling \textit{dynamic end-to-end} information sharing among treatment groups.

counterfactual Counterfactual Inference

Adversarial De-confounding in Individualised Treatment Effects Estimation

no code implementations19 Oct 2022 Vinod Kumar Chauhan, Soheila Molaei, Marzia Hoque Tania, Anshul Thakur, Tingting Zhu, David A. Clifton

Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc.

counterfactual Counterfactual Inference

COPER: Continuous Patient State Perceiver

1 code implementation5 Aug 2022 Vinod Kumar Chauhan, Anshul Thakur, Odhran O'Donoghue, David A. Clifton

COPER uses Perceiver model and the concept of neural ordinary differential equations (ODEs) to learn the continuous time dynamics of patient state, i. e., continuity of input space and continuity of output space.

Irregular Time Series Mortality Prediction +2

HCR-Net: A deep learning based script independent handwritten character recognition network

1 code implementation15 Aug 2021 Vinod Kumar Chauhan, Sukhdeep Singh, Anuj Sharma

To address these limitations, we have proposed a script independent deep learning network for HCR research, called HCR-Net, that sets a new research direction for the field.

Image Augmentation Transfer Learning

LIBS2ML: A Library for Scalable Second Order Machine Learning Algorithms

1 code implementation20 Apr 2019 Vinod Kumar Chauhan, Anuj Sharma, Kalpana Dahiya

LIBS2ML is a library based on scalable second order learning algorithms for solving large-scale problems, i. e., big data problems in machine learning.

BIG-bench Machine Learning Second-order methods

Stochastic Trust Region Inexact Newton Method for Large-scale Machine Learning

1 code implementation26 Dec 2018 Vinod Kumar Chauhan, Anuj Sharma, Kalpana Dahiya

Nowadays stochastic approximation methods are one of the major research direction to deal with the large-scale machine learning problems.

BIG-bench Machine Learning Second-order methods

SAAGs: Biased Stochastic Variance Reduction Methods for Large-scale Learning

no code implementations24 Jul 2018 Vinod Kumar Chauhan, Anuj Sharma, Kalpana Dahiya

Stochastic approximation is one of the effective approach to deal with the large-scale machine learning problems and the recent research has focused on reduction of variance, caused by the noisy approximations of the gradients.

Faster Learning by Reduction of Data Access Time

no code implementations18 Jan 2018 Vinod Kumar Chauhan, Anuj Sharma, Kalpana Dahiya

In this paper, we have proposed one possible solution to handle the big data problems in machine learning.

BIG-bench Machine Learning

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