1 citations · 2 across the 8 of their papers we have counts for
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Hyperbolic Time Series Forecasting with Learnable Decomposition and Manifold Dynamics
Sarang Rajendra Patil, Vishal Fenn, Mengjia Xu
Multiscale structure is central to time series forecasting: temporal signals contain slowly varying trends, intermediate seasonal patterns, and high-frequency residuals. Traditiona…
Multilevel Graph Wavelet Compressed Sensing with Scale-Aware Neural Recovery
Amirhossein Nouranizadeh, Sarang Rajendra Patil, Alan John Varghese +3
Scientific machine learning methods such as neural operators and physics-informed neural networks have advanced engineering applications and inverse problems, but their training ty…
RGE-GCN: Recursive Gene Elimination with Graph Convolutional Networks for RNA-seq based Early Cancer Detection
Shreyas Shende, Varsha Narayanan, Vishal Fenn +5
Early detection of cancer plays a key role in improving survival rates, but identifying reliable biomarkers from RNA-seq data is still a major challenge. The data are high-dimensio…
BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation
Yiran Huang, Amirhossein Nouranizadeh, Christine Ahrends +1
Functional Magnetic Resonance Imaging (fMRI) is an imaging technique widely used to study human brain activity. fMRI signals in areas across the brain transiently synchronise and d…
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers
Ashish Parmanand Pandey, Alan John Varghese, Sarang Patil +1
Dynamic graph embedding has emerged as an important technique for modeling complex time-evolving networks across diverse domains. While transformer-based models have shown promise…