Zobrazeno 1 - 10
of 12 001
pro vyhledávání: '"Thiagarajan A"'
Autor:
Pathrudkar, Shashank, Taylor, Stephanie, Keripale, Abhishek, Gangan, Abhijeet Sadashiv, Thiagarajan, Ponkrshnan, Agarwal, Shivang, Marian, Jaime, Ghosh, Susanta, Banerjee, Amartya S.
We propose machine learning (ML) models to predict the electron density -- the fundamental unknown of a material's ground state -- across the composition space of concentrated alloys. From this, other physical properties can be inferred, enabling acc
Externí odkaz:
http://arxiv.org/abs/2410.08294
Autor:
Pudelko, Wojciech R., Liu, Huanlong, Petocchi, Francesco, Li, Hang, Guedes, Eduardo Bonini, Küspert, Julia, von Arx, Karin, Wang, Qisi, Wagner, Ron Cohn, Polley, Craig M., Leandersson, Mats, Osiecki, Jacek, Thiagarajan, Balasubramanian, Radović, Milan, Werner, Philipp, Schilling, Andreas, Chang, Johan, Plumb, Nicholas C.
Layered transition metal dichalcogenides (TMDs) stabilize in multiple structural forms with profoundly distinct and exotic electronic phases. Interfacing different layer types is a promising route to manipulate TMDs' properties, not only as a means t
Externí odkaz:
http://arxiv.org/abs/2409.13384
Large language models (LLMs) are increasingly employed in information-seeking and decision-making tasks. Despite their broad utility, LLMs tend to generate information that conflicts with real-world facts, and their persuasive style can make these in
Externí odkaz:
http://arxiv.org/abs/2409.12180
LLM-based data generation for real-world tabular data can be challenged by the lack of sufficient semantic context in feature names used to describe columns. We hypothesize that enriching prompts with domain-specific insights can improve both the qua
Externí odkaz:
http://arxiv.org/abs/2409.03946
Autor:
Guo, Qinda, Xu, Ke-Jun, Berntsen, Magnus H., Grubišić-Čabo, Antonija, Dendzik, Maciej, Balasubramanian, Thiagarajan, Polley, Craig, Chen, Su-Di, He, Junfeng, He, Yu, Rotundu, Costel R., Lee, Young S., Hashimoto, Makoto, Lu, Dong-Hui, Devereaux, Thomas P., Lee, Dung-Hai, Shen, Zhi-Xun, Tjernberg, Oscar
Spin- and charge-lattice interactions are potential key factors in the microscopic mechanism of high-temperature superconductivity in cuprates. Although both interactions can dramatically shape the low-energy electronic structure, their phenomenologi
Externí odkaz:
http://arxiv.org/abs/2408.01685
Reliably detecting when a deployed machine learning model is likely to fail on a given input is crucial for ensuring safe operation. In this work, we propose DECIDER (Debiasing Classifiers to Identify Errors Reliably), a novel approach that leverages
Externí odkaz:
http://arxiv.org/abs/2408.00331
Autor:
Wang, Lingzhi, Wang, Jiahui, Jung, Kyle, Thiagarajan, Kedar, Wei, Emily, Shen, Xiangmin, Chen, Yan, Li, Zhenyuan
The escalating battles between attackers and defenders in cybersecurity make it imperative to test and evaluate defense capabilities from the attackers' perspective. However, constructing full-life-cycle cyberattacks and performing red team emulation
Externí odkaz:
http://arxiv.org/abs/2407.16928
In this work, we investigate the fundamental trade-off regarding accuracy and parameter efficiency in the parameterization of neural network weights using predictor networks. We present a surprising finding that, when recovering the original model ac
Externí odkaz:
http://arxiv.org/abs/2407.00356
Scaling up neural networks has been a key recipe to the success of large language and vision models. However, in practice, up-scaled models can be disproportionately costly in terms of computations, providing only marginal improvements in performance
Externí odkaz:
http://arxiv.org/abs/2406.17117
Offshore wind farms have emerged as a popular renewable energy source that can generate substantial electric power with a low environmental impact. However, integrating these farms into the grid poses significant complexities. To address these issues
Externí odkaz:
http://arxiv.org/abs/2406.10365