14 papers
HetGPS: Scalable Graph Multi-Agent Reinforcement Learning with Physics-Anchored Adaptive Safety for EV Charging
Xiangwei Wang, Nanduni Nimalsiri, Yu Xia +2
Safety interventions for large populations of network-coupled agents must protect shared constraints without unnecessarily overriding task-oriented policy decisions. We present Het…
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…
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…
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…
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…