Zobrazeno 1 - 10
of 3 026
pro vyhledávání: '"P., Ganapathi"'
Autor:
Liu, Guiliang, Xu, Sheng, Liu, Shicheng, Gaurav, Ashish, Subramanian, Sriram Ganapathi, Poupart, Pascal
Inverse Constrained Reinforcement Learning (ICRL) is the task of inferring the implicit constraints followed by expert agents from their demonstration data. As an emerging research topic, ICRL has received considerable attention in recent years. This
Externí odkaz:
http://arxiv.org/abs/2409.07569
Autor:
Rao, Jing, Sun, Binhan, Ganapathi, Arulkumar, Dong, Xizhen, Hohenwarter, Anton, Wu, Chun-Hung, Rohwerder, Michael, Dehm, Gerhard, Duarte, Maria Jazmin
Hydrogen embrittlement can result in a sudden failure in metallic materials, which is particularly harmful in industrially relevant alloys, such as steels. A more comprehensive understanding of hydrogen interactions with microstructural features is c
Externí odkaz:
http://arxiv.org/abs/2409.02787
Autor:
Subramanian, Sriram Ganapathi, Liu, Guiliang, Elmahgiubi, Mohammed, Rezaee, Kasra, Poupart, Pascal
In coming up with solutions to real-world problems, humans implicitly adhere to constraints that are too numerous and complex to be specified completely. However, reinforcement learning (RL) agents need these constraints to learn the correct optimal
Externí odkaz:
http://arxiv.org/abs/2406.16782
Autor:
Kristiadi, Agustinus, Strieth-Kalthoff, Felix, Subramanian, Sriram Ganapathi, Fortuin, Vincent, Poupart, Pascal, Pleiss, Geoff
Bayesian optimization (BO) is an integral part of automated scientific discovery -- the so-called self-driving lab -- where human inputs are ideally minimal or at least non-blocking. However, scientists often have strong intuition, and thus human fee
Externí odkaz:
http://arxiv.org/abs/2406.06459
Autor:
Javed, Sajid, Mahmood, Arif, Ganapathi, Iyyakutti Iyappan, Dharejo, Fayaz Ali, Werghi, Naoufel, Bennamoun, Mohammed
This paper proposes Comprehensive Pathology Language Image Pre-training (CPLIP), a new unsupervised technique designed to enhance the alignment of images and text in histopathology for tasks such as classification and segmentation. This methodology e
Externí odkaz:
http://arxiv.org/abs/2406.05205
Autor:
Sahoo, Anubhab, Dixit, Tejendra, Kumar, K. V. Anil, Ganapathi, K. Lakshmi, Nayak, Pramoda K., Rao, M. S. Ramachandra, Krishnan, Sivarama
Herein, $\mathrm{MoS_{2}}$ quantum dot (QDs) with controlled optical, structural, and electronic properties are synthesized using the femtosecond pulsed laser ablation in liquid (fs-PLAL) technique by varying pulse-width, ablation power, and ablation
Externí odkaz:
http://arxiv.org/abs/2405.11934
We investigate the problem of pixelwise correspondence for deformable objects, namely cloth and rope, by comparing both classical and learning-based methods. We choose cloth and rope because they are traditionally some of the most difficult deformabl
Externí odkaz:
http://arxiv.org/abs/2405.08996
Analysis of the 3D Texture is indispensable for various tasks, such as retrieval, segmentation, classification, and inspection of sculptures, knitted fabrics, and biological tissues. A 3D texture is a locally repeated surface variation independent of
Externí odkaz:
http://arxiv.org/abs/2311.10651
Autor:
Lakshmi Ganapathi, Aylur K. Srikrishnan, Allison M. McFall, Mihili P. Gunaratne, Muniratnam Suresh Kumar, Gregory M. Lucas, Shruti H. Mehta, Sunil S. Solomon
Publikováno v:
Harm Reduction Journal, Vol 21, Iss 1, Pp 1-11 (2024)
Abstract Background Over the last decade, India has had an alarming rise in injection of opioids across several cities. Although scale-up of public sector services for people who inject drugs (PWID) in India has occurred over decades, accessibility h
Externí odkaz:
https://doaj.org/article/527da3711ea84eb09632cbafac23f0d9
Autor:
Beeler, Chris, Subramanian, Sriram Ganapathi, Sprague, Kyle, Chatti, Nouha, Bellinger, Colin, Shahen, Mitchell, Paquin, Nicholas, Baula, Mark, Dawit, Amanuel, Yang, Zihan, Li, Xinkai, Crowley, Mark, Tamblyn, Isaac
This paper provides a simulated laboratory for making use of Reinforcement Learning (RL) for chemical discovery. Since RL is fairly data intensive, training agents `on-the-fly' by taking actions in the real world is infeasible and possibly dangerous.
Externí odkaz:
http://arxiv.org/abs/2305.14177