Zobrazeno 1 - 10
of 14 537
pro vyhledávání: '"PREMKUMAR, A."'
With the boom of machine learning (ML) techniques, software practitioners build ML systems to process the massive volume of streaming data for diverse software engineering tasks such as failure prediction in AIOps. Trained using historical data, such
Externí odkaz:
http://arxiv.org/abs/2410.09190
Autor:
Premkumar, Akhil
We investigate the use of diffusion models as neural density estimators. The current approach to this problem involves converting the generative process to a smooth flow, known as the Probability Flow ODE. The log density at a given sample can be obt
Externí odkaz:
http://arxiv.org/abs/2410.06986
Autor:
Premkumar, Akhil
We examine the connection between deep learning and information theory through the paradigm of diffusion models. Using well-established principles from non-equilibrium thermodynamics we can characterize the amount of information required to reverse a
Externí odkaz:
http://arxiv.org/abs/2409.03817
Experimental evaluations of software engineering innovations, e.g., tools and processes, often include human-subject studies as a component of a multi-pronged strategy to obtain greater generalizability of the findings. However, human-subject studies
Externí odkaz:
http://arxiv.org/abs/2408.05534
The availability of vast amounts of publicly accessible data of source code and the advances in modern language models, coupled with increasing computational resources, have led to a remarkable surge in the development of large language models for co
Externí odkaz:
http://arxiv.org/abs/2405.16746
Autor:
Hussain, Aftab, Rabin, Md Rafiqul Islam, Ahmed, Toufique, Xu, Bowen, Devanbu, Premkumar, Alipour, Mohammad Amin
Large language models (LLMs) have provided a lot of exciting new capabilities in software development. However, the opaque nature of these models makes them difficult to reason about and inspect. Their opacity gives rise to potential security risks,
Externí odkaz:
http://arxiv.org/abs/2405.02828
A good summary can often be very useful during program comprehension. While a brief, fluent, and relevant summary can be helpful, it does require significant human effort to produce. Often, good summaries are unavailable in software projects, thus ma
Externí odkaz:
http://arxiv.org/abs/2404.19318
Automated program repair has emerged as a powerful technique to mitigate the impact of software bugs on system reliability and user experience. This paper introduces RepairAgent, the first work to address the program repair challenge through an auton
Externí odkaz:
http://arxiv.org/abs/2403.17134
Autor:
Hsu, I-Hung, Xue, Zihan, Pochh, Nilay, Bansal, Sahil, Natarajan, Premkumar, Srinivasa, Jayanth, Peng, Nanyun
Event linking connects event mentions in text with relevant nodes in a knowledge base (KB). Prior research in event linking has mainly borrowed methods from entity linking, overlooking the distinct features of events. Compared to the extensively expl
Externí odkaz:
http://arxiv.org/abs/2403.15097
Autor:
Premkumar, Amritha, Rajendran, Prajit T, Menon, Vignesh V, Wieckowski, Adam, Bross, Benjamin, Marpe, Detlev
Traditional per-title encoding schemes aim to optimize encoding resolutions to deliver the highest perceptual quality for each representation. XPSNR is observed to correlate better with the subjective quality of VVC-coded bitstreams. Towards this rea
Externí odkaz:
http://arxiv.org/abs/2403.10976