Zobrazeno 1 - 10
of 13 174
pro vyhledávání: '"Ghosal A"'
Autor:
de Haan, Tijmen, Ting, Yuan-Sen, Ghosal, Tirthankar, Nguyen, Tuan Dung, Accomazzi, Alberto, Wells, Azton, Ramachandra, Nesar, Pan, Rui, Sun, Zechang
AstroSage-Llama-3.1-8B is a domain-specialized natural-language AI assistant tailored for research in astronomy, astrophysics, and cosmology. Trained on the complete collection of astronomy-related arXiv papers from 2007-2024 along with millions of s
Externí odkaz:
http://arxiv.org/abs/2411.09012
Autor:
Godambe, S., Mankuzhiyil, N., Borwankar, C., Ghosal, B., Tolamatti, A., Pal, M., Chandra, P., Khurana, M., Pandey, P., Dar, Z. A., Godiyal, S., Hariharan, J., Anand, Keshav, Norlha, S., Sarkar, D., Thubstan, R., Venugopal, K., Pathania, A., Kotwal, S., Kumar, Raj, Bhatt, N., Chanchalani, K., Das, M., Singh, K. K., Gour, K. K., Kothari, M., Kumar, Nandan, Kumar, Naveen, Marandi, P., Kushwaha, C. P., Koul, M. K., Dorjey, P., Dorji, N., Chitnis, V. R., Rannot, R. C., Bhattacharyya, S., Chouhan, N., Dhar, V. K., Sharma, M., Yadav, K. K.
Publikováno v:
The Astrophysical Journal Letters, Volume 974, Number 2 (2024)
The radio galaxy NGC 1275, located at the central region of Perseus cluster, is a well-known very high-energy (VHE) gamma-ray emitter. The Major Atmospheric Cherenkov Experiment Telescope has detected two distinct episodes of VHE (E > 80 GeV) gamma-r
Externí odkaz:
http://arxiv.org/abs/2411.01823
In this work, we investigate the causal reasoning abilities of large language models (LLMs) through the representative problem of inferring causal relationships from narratives. We find that even state-of-the-art language models rely on unreliable sh
Externí odkaz:
http://arxiv.org/abs/2410.23884
We propose a method for simultaneously estimating a contemporaneous graph structure and autocorrelation structure for a causal high-dimensional vector autoregressive process (VAR). The graph is estimated by estimating the stationary precision matrix
Externí odkaz:
http://arxiv.org/abs/2410.22617
Autor:
Ghosal, Promit, Mukherjee, Sumit
We investigate the probability that a random polynomial with independent, mean-zero and finite variance coefficients has no real zeros. Specifically, we consider a random polynomial of degree $2n$ with coefficients given by an i.i.d. sequence of mean
Externí odkaz:
http://arxiv.org/abs/2410.20714
Deep learning (DL) methods have emerged as a powerful tool for the inversion of geophysical data. When applied to field data, these models often struggle without additional fine-tuning of the network. This is because they are built on the assumption
Externí odkaz:
http://arxiv.org/abs/2410.19858
This project explores the application of machine learning techniques for music genre classification using the GTZAN dataset, which contains 100 audio files per genre. Motivated by the growing demand for personalized music recommendations, we focused
Externí odkaz:
http://arxiv.org/abs/2410.14990
Autor:
Ni, Jinjie, Song, Yifan, Ghosal, Deepanway, Li, Bo, Zhang, David Junhao, Yue, Xiang, Xue, Fuzhao, Zheng, Zian, Zhang, Kaichen, Shah, Mahir, Jain, Kabir, You, Yang, Shieh, Michael
Perceiving and generating diverse modalities are crucial for AI models to effectively learn from and engage with real-world signals, necessitating reliable evaluations for their development. We identify two major issues in current evaluations: (1) in
Externí odkaz:
http://arxiv.org/abs/2410.13754
In this paper, we study the problem of finding an envy-free allocation of indivisible goods among multiple agents. EFX, which stands for envy-freeness up to any good, is a well-studied relaxation of the envy-free allocation problem and has been shown
Externí odkaz:
http://arxiv.org/abs/2410.13580
Large language models (LLMs) have shown increasing proficiency in solving mathematical reasoning problems. However, many current open-source LLMs often still make calculation and semantic understanding errors in their intermediate reasoning steps. In
Externí odkaz:
http://arxiv.org/abs/2410.12608