74 citations · 171 across the 29 of their papers we have counts for
7 papers · 1 filter
Bidirectional Multimodal Fusion of Sky Images and Time-Series for Solar Forecasting with Large Language Models
Ken Chen, Maneesha Perera, Wei Wang +3
Short-term photovoltaic (PV) power and global horizontal irradiance (GHI) forecasts are essential for effective dispatch, reserve scheduling, and grid operations. At these forecast…
Knowledge-Inclusive Adaptive Physics-Informed Neural Network for Microbial Interaction Modelling
Ravisha Rupasinghe, Rajith Vidanaarachchi, Asela Hevapathige +3
Physics-Informed Neural Network (PINN) is a way of including knowledge in the form of equations in Machine Learning methods. Beyond equations, knowledge exists in other forms, such…
From Specification to Architecture: A Theory Compiler for Knowledge-Guided Machine Learning
Asela Hevapathige, Yu Xia, Sachith Seneviratne +1
Theory-guided machine learning has demonstrated that including authentic domain knowledge directly into model design improves performance, sample efficiency and out-of-distribution…
Arch-VQ: Discrete Architecture Representation Learning with Autoregressive Priors
Deshani Geethika Poddenige, Sachith Seneviratne, Asela Hevapathige +4
Existing neural architecture representation learning methods focus on continuous representation learning, typically using Variational Autoencoders (VAEs) to map discrete architectu…
NeST: Neighborhood-aware semantic alignment and temporal modulation for LLM based time series forecasting
Jayanie Bogahawatte, Sachith Seneviratne, Maneesha Perera +1
Adapting Large Language Models (LLMs) trained on discrete text data, to forecast continuous time series signals is challenging. While finetuning the LLMs enables such adaptation, e…
GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks
Nisal Ranasinghe, Yu Xia, Sachith Seneviratne +1
Neural networks are powerful function approximators, yet their ``black-box" nature often renders them opaque and difficult to interpret. While many post-hoc explanation methods exi…
GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations
Nisal Ranasinghe, Damith Senanayake, Sachith Seneviratne +2
Traditional machine learning is generally treated as a black-box optimization problem and does not typically produce interpretable functions that connect inputs and outputs. Howeve…