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Found 12 papers, 7 papers with code
GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks
Graph Neural Networks (GNN) have emerged as a popular and standard approach for learning from graph-structured data.
A Brief Review of Hypernetworks in Deep Learning
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.
Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation
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.
Synthesizing Mixed-type Electronic Health Records using Diffusion Models
Electronic Health Records (EHRs) contain sensitive patient information, which presents privacy concerns when sharing such data.
Adversarial De-confounding in Individualised Treatment Effects Estimation
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.
Using Bottleneck Adapters to Identify Cancer in Clinical Notes under Low-Resource Constraints
Processing information locked within clinical health records is a challenging task that remains an active area of research in biomedical NLP.
COPER: Continuous Patient State Perceiver
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.
HCR-Net: A deep learning based script independent handwritten character recognition network
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.
LIBS2ML: A Library for Scalable Second Order Machine Learning Algorithms
LIBS2ML is a library based on scalable second order learning algorithms for solving large-scale problems, i. e., big data problems in machine learning.
Stochastic Trust Region Inexact Newton Method for Large-scale Machine Learning
Nowadays stochastic approximation methods are one of the major research direction to deal with the large-scale machine learning problems.
SAAGs: Biased Stochastic Variance Reduction Methods for Large-scale Learning
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
In this paper, we have proposed one possible solution to handle the big data problems in machine learning.
