Zobrazeno 1 - 10
of 10 288
pro vyhledávání: '"Anoop, P P"'
Autor:
Doddapaneni, Sumanth, Khan, Mohammed Safi Ur Rahman, Venkatesh, Dilip, Dabre, Raj, Kunchukuttan, Anoop, Khapra, Mitesh M.
Evaluating machine-generated text remains a significant challenge in NLP, especially for non-English languages. Current methodologies, including automated metrics, human assessments, and LLM-based evaluations, predominantly focus on English, revealin
Externí odkaz:
http://arxiv.org/abs/2410.13394
Along with the partition of a planar bounded domain $\Omega$ by the nodal set of a fixed eigenfunction of the Laplace operator in $\Omega$, one can consider another natural partition of $\Omega$ by, roughly speaking, gradient flow lines of a special
Externí odkaz:
http://arxiv.org/abs/2410.07811
Autor:
Lawton, Neal, Padmakumar, Aishwarya, Gaspers, Judith, FitzGerald, Jack, Kumar, Anoop, Steeg, Greg Ver, Galstyan, Aram
QLoRA reduces the memory-cost of fine-tuning a large language model (LLM) with LoRA by quantizing the base LLM. However, quantization introduces quantization errors that negatively impact model performance after fine-tuning. In this paper we introduc
Externí odkaz:
http://arxiv.org/abs/2410.14713
The aim of this manuscript is to address non-linear differential equations of the Lane Emden equation of second order using the shifted Legendre neural network (SLNN) method. Here all the equations are classified as singular initial value problems. T
Externí odkaz:
http://arxiv.org/abs/2410.05409
Autor:
Melcer, Daniel, Gonugondla, Sujan, Perera, Pramuditha, Qian, Haifeng, Chiang, Wen-Hao, Wang, Yanjun, Jain, Nihal, Garg, Pranav, Ma, Xiaofei, Deoras, Anoop
It is common to reject undesired outputs of Large Language Models (LLMs); however, current methods to do so require an excessive amount of computation, or severely distort the distribution of outputs. We present a method to balance the distortion of
Externí odkaz:
http://arxiv.org/abs/2410.01103
Autor:
Myers, Skatje, Miller, Timothy A., Gao, Yanjun, Churpek, Matthew M., Mayampurath, Anoop, Dligach, Dmitriy, Afshar, Majid
Objective: Applying large language models (LLMs) to the clinical domain is challenging due to the context-heavy nature of processing medical records. Retrieval-augmented generation (RAG) offers a solution by facilitating reasoning over large text sou
Externí odkaz:
http://arxiv.org/abs/2409.15163
Autor:
Gangan, Abhijeet S., Schoenholz, Samuel S., Cubuk, Ekin Dogus, Bauchy, Mathieu, Krishnan, N. M. Anoop
The accuracy of atomistic simulations depends on the precision of force fields. Traditional numerical methods often struggle to optimize the empirical force field parameters for reproducing target properties. Recent approaches rely on training these
Externí odkaz:
http://arxiv.org/abs/2409.13844
Data poisoning and leakage risks impede the massive deployment of federated learning in the real world. This chapter reveals the truths and pitfalls of understanding two dominating threats: {\em training data privacy intrusion} and {\em training data
Externí odkaz:
http://arxiv.org/abs/2409.13004
We present an efficient momentum based perturbation scheme to evaluate polarizability tensors of small molecules and at the fraction of the computational cost compared to conventional energy based perturbation schemes. Furthermore, the simplicity of
Externí odkaz:
http://arxiv.org/abs/2409.10184
Autor:
Bhat, Anoop, Gutow, Geordan, Vundurthy, Bhaskar, Ren, Zhongqiang, Rathinam, Sivakumar, Choset, Howie
The moving target traveling salesman problem with obstacles (MT-TSP-O) is a generalization of the traveling salesman problem (TSP) where, as its name suggests, the targets are moving. A solution to the MT-TSP-O is a trajectory that visits each moving
Externí odkaz:
http://arxiv.org/abs/2409.09852