Search Results for author:
Found 7 papers, 2 papers with code
ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler
State-of-the-art intent classification (IC) and slot filling (SF) methods often rely on data-intensive deep learning models, limiting their practicality for industry applications.
InCA: Rethinking In-Car Conversational System Assessment Leveraging Large Language Models
To address these challenges, this paper introduces a set of KPIs tailored for evaluating the performance of in-car ConvQA systems, along with datasets specifically designed for these KPIs.
Cooperative Localization for Autonomous Underwater Vehicles -- a comprehensive review
Cooperative localization is an important technique in environments devoid of GPS-based localization, more so in underwater scenarios, where none of the terrestrial localization techniques based on radio frequency or optics are suitable due to severe attenuation.
Robust Short-term Operation of AC Power Network with Injection Uncertainties
A computationally time-efficient, economical, and robust solution is essential for ACOPF with short-term injection uncertainties.
Data Augmentation using Feature Generation for Volumetric Medical Images
Deep Learning models, in particular, show promising results on image segmentation and classification problems, but they require very large datasets for training.
Subsystem decomposition and state estimation of nonlinear processes with implicit time-scale multiplicity
In this work, we propose a subsystem decomposition approach and a distributed estimation scheme for a class of implicit two-time-scale nonlinear systems.
DHSGAN: An End to End Dehazing Network for Fog and Smoke
In this paper we propose a novel end-to-end convolution dehazing architecture, called De-Haze and Smoke GAN (DHSGAN).
