Zobrazeno 1 - 10
of 1 996
pro vyhledávání: '"P Prajwal"'
Spatial metapopulation models are fundamental to theoretical ecology, enabling to study how landscape structure influences global species dynamics. Traditional models, including recent generalizations, often rely on the deterministic limit of stochas
Externí odkaz:
http://arxiv.org/abs/2412.18448
Autor:
Nayak, Ashutosh, NJ, Prajwal, Keshav, Sameeksha, N., Kavitha S., Reddy, Roja, Muni, Rajasekhara Reddy Duvvuru
Recommender systems create enormous value for businesses and their consumers. They increase revenue for businesses while improving the consumer experience by recommending relevant products amidst huge product base. Product bundling is an exciting dev
Externí odkaz:
http://arxiv.org/abs/2412.17310
The success of deep learning in supervised fine-grained recognition for domain-specific tasks relies heavily on expert annotations. The Open-Set for fine-grained Self-Supervised Learning (SSL) problem aims to enhance performance on downstream tasks b
Externí odkaz:
http://arxiv.org/abs/2412.16942
Transforming CO$_2$ into methanol represents a crucial step towards closing the carbon cycle, with thermoreduction technology nearing industrial application. However, obtaining high methanol yields and ensuring the stability of heterocatalysts remain
Externí odkaz:
http://arxiv.org/abs/2412.13838
Publikováno v:
Proceedings of the 3rd Machine Learning for Health Symposium, PMLR 225:201-216, 2023
Accurate diagnostic coding of medical notes is crucial for enhancing patient care, medical research, and error-free billing in healthcare organizations. Manual coding is a time-consuming task for providers, and diagnostic codes often exhibit low sens
Externí odkaz:
http://arxiv.org/abs/2412.11477
In this paper, we present a novel keypoint-based classification model designed to recognise British Sign Language (BSL) words within continuous signing sequences. Our model's performance is assessed using the BOBSL dataset, revealing that the keypoin
Externí odkaz:
http://arxiv.org/abs/2412.09475
Safe offline reinforcement learning aims to learn policies that maximize cumulative rewards while adhering to safety constraints, using only offline data for training. A key challenge is balancing safety and performance, particularly when the policy
Externí odkaz:
http://arxiv.org/abs/2412.08880
In safe offline reinforcement learning (RL), the objective is to develop a policy that maximizes cumulative rewards while strictly adhering to safety constraints, utilizing only offline data. Traditional methods often face difficulties in balancing t
Externí odkaz:
http://arxiv.org/abs/2412.08794
Autor:
Corcoran, Kyle A., Ransom, Scott M., Rosenthal, Alexandra C., DeCesar, Megan E., Freire, Paulo C. C., Hessels, Jason W. T., Lynch, Ryan S., Padmanabh, Prajwal V., Stairs, Ingrid H.
We present timing solutions spanning nearly two decades for five redback (RB) systems found in globular clusters (GC), created using a novel technique that effectively "isolates" the pulsar. By accurately measuring the time of passage through periast
Externí odkaz:
http://arxiv.org/abs/2412.08688
Due to the extensive application of machine learning (ML) in a wide range of fields and the necessity of data privacy, privacy-preserving machine learning (PPML) solutions have recently gained significant traction. One group of approaches relies on H
Externí odkaz:
http://arxiv.org/abs/2412.07954