10 citations · 23 across the 5 of their papers we have counts for
5 papers
Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering
Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen +3
Retrieval Augment Generation (RAG) is a recent advancement in Open-Domain Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia-based external knowledg…
Fine-tune the Entire RAG Architecture (including DPR retriever) for Question-Answering
Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen +1
In this paper, we illustrate how to fine-tune the entire Retrieval Augment Generation (RAG) architecture in an end-to-end manner. We highlighted the main engineering challenges tha…
Jointly Fine-Tuning "BERT-like" Self Supervised Models to Improve Multimodal Speech Emotion Recognition
Shamane Siriwardhana, Andrew Reis, Rivindu Weerasekera +1
Multimodal emotion recognition from speech is an important area in affective computing. Fusing multiple data modalities and learning representations with limited amounts of labeled…
VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation
Shamane Siriwardhana, Rivindu Weerasakera, Denys J. C. Matthies +1
In this paper, we show how novel transfer reinforcement learning techniques can be applied to the complex task of target driven navigation using the photorealistic AI2THOR simulato…
Target Driven Visual Navigation with Hybrid Asynchronous Universal Successor Representations
Shamane Siriwardhana, Rivindu Weerasekera, Suranga Nanayakkara
Being able to navigate to a target with minimal supervision and prior knowledge is critical to creating human-like assistive agents. Prior work on map-based and map-less approaches…