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pro vyhledávání: '"Vasudeva, P"'
A framework of finite-velocity model based Boltzmann equation has been developed for convection-diffusion equations. These velocities are kept flexible and adjusted to control numerical diffusion. A flux difference splitting based kinetic scheme is t
Externí odkaz:
http://arxiv.org/abs/2409.20101
This paper describes our approach for SemEval-2024 Task 9: BRAINTEASER: A Novel Task Defying Common Sense. The BRAINTEASER task comprises multiple-choice Question Answering designed to evaluate the models' lateral thinking capabilities. It consists o
Externí odkaz:
http://arxiv.org/abs/2405.16129
The proliferation of LLMs in various NLP tasks has sparked debates regarding their reliability, particularly in annotation tasks where biases and hallucinations may arise. In this shared task, we address the challenge of distinguishing annotations ma
Externí odkaz:
http://arxiv.org/abs/2405.11192
Hallucinations in large language models (LLMs) have recently become a significant problem. A recent effort in this direction is a shared task at Semeval 2024 Task 6, SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistak
Externí odkaz:
http://arxiv.org/abs/2404.06948
Transformers achieve state-of-the-art accuracy and robustness across many tasks, but an understanding of the inductive biases that they have and how those biases are different from other neural network architectures remains elusive. Various neural ne
Externí odkaz:
http://arxiv.org/abs/2403.06925
Architectural Knowledge Management (AKM) involves the organized handling of information related to architectural decisions and design within a project or organization. An essential artifact of AKM is the Architecture Decision Records (ADR), which doc
Externí odkaz:
http://arxiv.org/abs/2403.01709
Self-attention, the core mechanism of transformers, distinguishes them from traditional neural networks and drives their outstanding performance. Towards developing the fundamental optimization principles of self-attention, we investigate the implici
Externí odkaz:
http://arxiv.org/abs/2402.05738
Autor:
Murugesan, Balamurali, Vasudeva, Sukesh Adiga, Liu, Bingyuan, Lombaert, Hervé, Ayed, Ismail Ben, Dolz, Jose
Ensuring reliable confidence scores from deep neural networks is of paramount significance in critical decision-making systems, particularly in real-world domains such as healthcare. Recent literature on calibrating deep segmentation networks has res
Externí odkaz:
http://arxiv.org/abs/2401.14487
Autor:
Maity, Ankita, Sharma, Anubhav, Dhar, Rudra, Abhishek, Tushar, Gupta, Manish, Varma, Vasudeva
Lack of diverse perspectives causes neutrality bias in Wikipedia content leading to millions of worldwide readers getting exposed by potentially inaccurate information. Hence, neutrality bias detection and mitigation is a critical problem. Although p
Externí odkaz:
http://arxiv.org/abs/2312.15181
Neural networks (NNs) are known to exhibit simplicity bias where they tend to prefer learning 'simple' features over more 'complex' ones, even when the latter may be more informative. Simplicity bias can lead to the model making biased predictions wh
Externí odkaz:
http://arxiv.org/abs/2310.06161