74 citations · 171 across the 29 of their papers we have counts for
33 papers
HarnessNet: Feedback-driven Hierarchical Joint Harness Evolution for Multi-agent Systems
Zhaoyuan Qiu, Sachith Seneviratne, Yu Xia +1
AI agents can evolve by using environment feedback to revise their harnesses, the software managing model inputs, tool use, memory, and execution, while keeping foundation models f…
SmoothOperator: Enhancing Representations for Fine-grained Open-set Recognition via Modulated Label Smoothing
Thiru Thillai Nadarasar Bahavan, Yu Xia, Sachith Seneviratne +1
Open Set Recognition (OSR) aims to enable models to accurately classify known classes while rejecting samples from unseen classes. A key challenge in OSR lies in the inability to m…
When Agents Disagree: Bayesian Backward Reasoning as a Label-Free Anchor for Multi-Agent Collective Decision-Making
Ken Chen, Wei Wang, Sachith Seneviratne +2
When multiple LLM agents yield conflicting answers, the decision-making process dictates whether agent diversity improves performance or merely compounds shared errors. Existing co…
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…
What Personal Information Improves LLM-Based Next-Location Prediction?
Xin Wang, Paraic Carroll, Kerry Nice +2
Large language models (LLMs) are increasingly used for individual next-location prediction, with personal information easily added to prompts alongside mobility history. Yet the in…
FedLAS: Feature-Modulated Bidirectional Label Smoothing for Neural Network Calibration
Thiru Thillai Nadarasar Bahavan, Sachith Seneviratne, Saman Halgamuge
Deep Neural Network (DNN) classifiers suffer from poor calibration when their softmax outputs (predictive confidence) deviate from the empirical likelihoods. This manifests itself…
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…
Parameter-efficient Prompt Tuning and Hierarchical Textual Guidance for Few-shot Whole Slide Image Classification
Jayanie Bogahawatte, Sachith Seneviratne, Saman Halgamuge
Whole Slide Images (WSIs) are giga-pixel in scale and are typically partitioned into small instances in WSI classification pipelines for computational feasibility. However, obtaini…
MSRAMIE: Multimodal Structured Reasoning Agent for Multi-instruction Image Editing
Zhaoyuan Qiu, Ken Chen, Xiangwei Wang +3
Existing instruction-based image editing models perform well with simple, single-step instructions but degrade in realistic scenarios that involve multiple, lengthy, and interdepen…
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…
Flood-LDM: Generalizable Latent Diffusion Models for rapid and accurate zero-shot High-Resolution Flood Mapping
Sun Han Neo, Sachith Seneviratne, Herath Mudiyanselage Viraj Vidura Herath +3
Flood prediction is critical for emergency planning and response to mitigate human and economic losses. Traditional physics-based hydrodynamic models generate high-resolution flood…
FRAME-C: A knowledge-augmented deep learning pipeline for classifying multi-electrode array electrophysiological signals
Nisal Ranasinghe, Dzung Do-Ha, Simon Maksour +4
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by motor neuron degeneration, with alterations in neural excitability serving as key indicat…
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…
Beyond Deepfake vs Real: Facial Deepfake Detection in the Open-Set Paradigm
Nadarasar Bahavan, Sachith Seneviratne, Sanjay Saha +3
Facial forgery methods such as deepfakes can be misused for identity manipulation and spreading misinformation. They have evolved alongside advancements in generative AI, leading t…
SphOR: A Representation Learning Perspective on Open-set Recognition for Identifying Unknown Classes in Deep Learning Models
Nadarasar Bahavan, Sachith Seneviratne, Saman Halgamuge
The reliance on Deep Neural Network (DNN)-based classifiers in safety-critical and real-world applications necessitates Open-Set Recognition (OSR). OSR enables the identification o…
TT-MPD: Test Time Model Pruning and Distillation
Haihang Wu, Wei Wang, Tamasha Malepathirana +3
Pruning can be an effective method of compressing large pre-trained models for inference speed acceleration. Previous pruning approaches rely on access to the original training dat…
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…
AniFaceDiff: Animating Stylized Avatars via Parametric Conditioned Diffusion Models
Ken Chen, Sachith Seneviratne, Wei Wang +7
Animating stylized avatars with dynamic poses and expressions has attracted increasing attention for its broad range of applications. Previous research has made significant progres…
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…
Scalable Label-efficient Footpath Network Generation Using Remote Sensing Data and Self-supervised Learning
Xinye Wanyan, Sachith Seneviratne, Kerry Nice +4
Footpath mapping, modeling, and analysis can provide important geospatial insights to many fields of study, including transport, health, environment and urban planning. The availab…
Semantic Segmentation using Vision Transformers: A survey
Hans Thisanke, Chamli Deshan, Kavindu Chamith +3
Semantic segmentation has a broad range of applications in a variety of domains including land coverage analysis, autonomous driving, and medical image analysis. Convolutional neur…
Undercover Deepfakes: Detecting Fake Segments in Videos
Sanjay Saha, Rashindrie Perera, Sachith Seneviratne +5
The recent renaissance in generative models, driven primarily by the advent of diffusion models and iterative improvement in GAN methods, has enabled many creative applications. Ho…
FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning
Shuchang Shen, Sachith Seneviratne, Xinye Wanyan +1
In recent decades, wildfires, as widespread and extremely destructive natural disasters, have caused tremendous property losses and fatalities, as well as extensive damage to fores…