1 citations · 2 across the 8 of their papers we have counts for
10 papers
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…
Prediction of Magnetic Flux Evolution During Solar Active Region Emergence using Long Short-Term Memory Networks
Eren Dogan, Spiridon Kasapis, Sarang Patil +5
Solar active regions (ARs) are the primary drivers of space weather events, making their early prediction crucial for operational forecasting systems. We develop machine learning m…
SolARED: Solar Active Region Emergence Dataset for Machine Learning Aided Predictions
Spiridon Kasapis, Eren Dogan, Irina N. Kitiashvili +6
The development of accurate forecasts of solar eruptive activity has become increasingly important for preventing potential impacts on space technologies and exploration. Therefore…
Forecasting Continuum Intensity for Solar Active Region Emergence Prediction using Transformers
Jonas Tirona, Sarang Patil, Spiridon Kasapis +5
Early and accurate prediction of solar active region (AR) emergence is crucial for space weather forecasting. Building on established Long Short-Term Memory (LSTM) based approaches…
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…
Hyperbolic Large Language Models
Sarang Patil, Zeyong Zhang, Yiran Huang +2
Large language models (LLMs) have achieved remarkable success and demonstrated superior performance across various tasks, including natural language processing (NLP), weather forec…
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…
Hierarchical Mamba Meets Hyperbolic Geometry: A New Paradigm for Structured Language Embeddings
Sarang Patil, Ashish Parmanand Pandey, Ioannis Koutis +1
Selective state-space models excel at long-sequence modeling, but their capacity for language representation -- in complex hierarchical reasoning -- remains underexplored. Most lar…
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…