Zobrazeno 1 - 10
of 1 105
pro vyhledávání: '"Honavar, P. P."'
Pretraining molecular representations is crucial for drug and material discovery. Recent methods focus on learning representations from geometric structures, effectively capturing 3D position information. Yet, they overlook the rich information in bi
Externí odkaz:
http://arxiv.org/abs/2411.10821
This paper introduces a novel generalized self-imitation learning ($\textbf{GSIL}$) framework, which effectively and efficiently aligns large language models with offline demonstration data. We develop $\textbf{GSIL}$ by deriving a surrogate objectiv
Externí odkaz:
http://arxiv.org/abs/2410.10093
Autor:
Jacobs, Ryan, Polak, Maciej P., Schultz, Lane E., Mahdavi, Hamed, Honavar, Vasant, Morgan, Dane
We demonstrate the ability of large language models (LLMs) to perform material and molecular property regression tasks, a significant deviation from the conventional LLM use case. We benchmark the Large Language Model Meta AI (LLaMA) 3 on several mol
Externí odkaz:
http://arxiv.org/abs/2409.06080
Matching is one of the simplest approaches for estimating causal effects from observational data. Matching techniques compare the observed outcomes across pairs of individuals with similar covariate values but different treatment statuses in order to
Externí odkaz:
http://arxiv.org/abs/2404.10907
Recent advancements in biology and chemistry have leveraged multi-modal learning, integrating molecules and their natural language descriptions to enhance drug discovery. However, current pre-training frameworks are limited to two modalities, and des
Externí odkaz:
http://arxiv.org/abs/2403.08167
Generating molecular structures with desired properties is a critical task with broad applications in drug discovery and materials design. We propose 3M-Diffusion, a novel multi-modal molecular graph generation method, to generate diverse, ideally no
Externí odkaz:
http://arxiv.org/abs/2403.07179
Autor:
Dalvi, Abhishek, Honavar, Vasant
We introduce a novel method for transductive learning on graphs using hyperdimensional representations. The proposed approach encodes data samples using random projections into a very high-dimensional space (hyperdimensional or HD space for short). I
Externí odkaz:
http://arxiv.org/abs/2402.17073
Autor:
Ren, Weijieying, Honavar, Vasant G
A key challenge in the continual learning setting is to efficiently learn a sequence of tasks without forgetting how to perform previously learned tasks. Many existing approaches to this problem work by either retraining the model on previous tasks o
Externí odkaz:
http://arxiv.org/abs/2401.05667
Autor:
Salil K Mandal, Santosh G Honavar, Asrik Mukhopadhyay, Anwesha Maitra, Oishik Sarkar, Mausree Gayen, Nazibul H Mallick
Publikováno v:
Indian Journal of Ophthalmology, Vol 72, Iss 11, Pp 1645-1652 (2024)
Purpose: To describe the surgical technique using a 10/14 French silicone urinary catheter as a novel tissue expander for repair of defects after removal of eyelid tumors. This device recruits additional tissue by tissue expansion for repair of large
Externí odkaz:
https://doaj.org/article/b8ddf909016e421a991e02dd7d864772
Publikováno v:
Indian Journal of Ophthalmology, Vol 72, Iss 10, Pp 1433-1441 (2024)
Purpose: With the increased survival of retinoblastoma (RB) patients, it is important to evaluate the quality of life (QoL) of RB survivors as well as caregivers to provide comprehensive care to the children and caregivers. This study aims to assess
Externí odkaz:
https://doaj.org/article/e6718877bf3446a9b288b1512729a095