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pro vyhledávání: '"Faldu A"'
An essential requirement for a real-world Knowledge Base Question Answering (KBQA) system is the ability to detect the answerability of questions when generating logical forms. However, state-of-the-art KBQA models assume all questions to be answerab
Externí odkaz:
http://arxiv.org/abs/2403.10849
We model short-duration (e.g. day) trading in financial markets as a sequential decision-making problem under uncertainty, with the added complication of continual concept-drift. We, therefore, employ meta reinforcement learning via the RL2 algorithm
Externí odkaz:
http://arxiv.org/abs/2302.08996
Autor:
Patidar, Mayur, Faldu, Prayushi, Singh, Avinash, Vig, Lovekesh, Bhattacharya, Indrajit, Mausam
When answering natural language questions over knowledge bases, missing facts, incomplete schema and limited scope naturally lead to many questions being unanswerable. While answerability has been explored in other QA settings, it has not been studie
Externí odkaz:
http://arxiv.org/abs/2212.10189
Autor:
Lavanya Kandasamy, Anand Mahendran, Sai Harsha Varma Sangaraju, Preksha Mathur, Soham Vijaykumar Faldu, Manuel Mazzara
Publikováno v:
Results in Engineering, Vol 25, Iss , Pp 103604- (2025)
Water pollution is a pressing global concern, impacting numerous communities across the world. Existing water quality monitoring systems rely on static or periodically collected data, presenting limitations in their ability to provide real-time dynam
Externí odkaz:
https://doaj.org/article/0f14727d78244bc79b57a853e3e1c3b5
Publikováno v:
Journal of Ideas in Health, Vol 7, Iss 4 (2024)
Background: Inflammatory demyelinating diseases of the central nervous system (CNS) are autoimmune conditions leading to significant neurological disability in adults. Recent classifications include myelin oligodendrocyte glycoprotein antibody associ
Externí odkaz:
https://doaj.org/article/fe416aa6e157437dbab5ee900595a249
Recently, quite a few novel neural architectures were derived to solve math word problems by predicting expression trees. These architectures varied from seq2seq models, including encoders leveraging graph relationships combined with tree decoders. T
Externí odkaz:
http://arxiv.org/abs/2206.01268
Autor:
Kandasamy, Lavanya, Mahendran, Anand, Sangaraju, Sai Harsha Varma, Mathur, Preksha, Faldu, Soham Vijaykumar, Mazzara, Manuel
Publikováno v:
In Results in Engineering March 2025 25
Mathematical reasoning would be one of the next frontiers for artificial intelligence to make significant progress. The ongoing surge to solve math word problems (MWPs) and hence achieve better mathematical reasoning ability would continue to be a ke
Externí odkaz:
http://arxiv.org/abs/2111.05364
AI systems have seen significant adoption in various domains. At the same time, further adoption in some domains is hindered by inability to fully trust an AI system that it will not harm a human. Besides the concerns for fairness, privacy, transpare
Externí odkaz:
http://arxiv.org/abs/2108.01174
Contextualized entity representations learned by state-of-the-art transformer-based language models (TLMs) like BERT, GPT, T5, etc., leverage the attention mechanism to learn the data context from training data corpus. However, these models do not us
Externí odkaz:
http://arxiv.org/abs/2104.08145