Zobrazeno 1 - 10
of 24 812
pro vyhledávání: '"A, Madani"'
Autor:
Madani, Pooria
Publikováno v:
2023 5th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)
Code metamorphism refers to a computer programming exercise wherein the program modifies its own code (partial or entire) consistently and automatically while retaining its core functionality. This technique is often used for online performance optim
Externí odkaz:
http://arxiv.org/abs/2410.23894
This study investigates a counterintuitive phenomenon in adversarial machine learning: the potential for noise-based defenses to inadvertently aid evasion attacks in certain scenarios. While randomness is often employed as a defensive strategy agains
Externí odkaz:
http://arxiv.org/abs/2410.23870
Autor:
Setak, Mohammad, Madani, Pooria
Recent advancements in Large Language Models (LLMs) have significantly improved their capabilities in natural language processing and code synthesis, enabling more complex applications across different fields. This paper explores the application of L
Externí odkaz:
http://arxiv.org/abs/2410.22293
Autor:
Madani, Navid, Bagalkotkar, Anusha, Anand, Supriya, Arnson, Gabriel, Srihari, Rohini, Joseph, Kenneth
In recent years, there has been significant effort to align large language models with human preferences. This work focuses on developing a chatbot specialized in the real estate domain, with an emphasis on incorporating compliant behavior to ensure
Externí odkaz:
http://arxiv.org/abs/2410.10860
The conditional generation of proteins with desired functions and/or properties is a key goal for generative models. Existing methods based on prompting of language models can generate proteins conditioned on a target functionality, such as a desired
Externí odkaz:
http://arxiv.org/abs/2410.03634
Autor:
Jin, Helen, Havaldar, Shreya, Kim, Chaehyeon, Xue, Anton, You, Weiqiu, Qu, Helen, Gatti, Marco, Hashimoto, Daniel A, Jain, Bhuvnesh, Madani, Amin, Sako, Masao, Ungar, Lyle, Wong, Eric
Feature-based methods are commonly used to explain model predictions, but these methods often implicitly assume that interpretable features are readily available. However, this is often not the case for high-dimensional data, and it can be hard even
Externí odkaz:
http://arxiv.org/abs/2409.13684
In 1988, Erd\H{o}s suggested the question of minimizing the number of edges in a connected $n$-vertex graph where every edge is contained in a triangle. Shortly after, Catlin, Grossman, Hobbs, and Lai resolved this in a stronger form. In this paper,
Externí odkaz:
http://arxiv.org/abs/2409.11216
Autor:
Hibi, Takayuki, Madani, Sara Saeedi
Let $G$ be a finite simple non-complete connected graph on $[n] = \{1, \ldots, n\}$ and $\kappa(G) \geq 1$ its vertex connectivity. Let $f(G)$ denote the number of free vertices of $G$ and $\mathrm{diam}(G)$ the diameter of $G$. The final goal of thi
Externí odkaz:
http://arxiv.org/abs/2408.15141
Autor:
Maji, Moupiya, More, Surhud, Sule, Aniket, Balasubramanya, Vishaak, Bhandari, Ankit, Chand, Hum, Chavan, Kshitij, Dasgupta, Avik, De, Anindya, Gangopadhyay, Jayant, Gulati, Mamta, Hasan, Priya, Ishtiyaq, Syed, Madani, Meraj, Misra, Kuntal, N, Amoghavarsha, Oberoi, Divya, Pattnaik, Subhendu, Patwardhan, Mayuri, Ramanujam, Niruj Mohan, Ranadive, Pritesh, Sawant, Disha, Sharma, Paryag, Sharma, Twinkle, Shetye, Sai, Singhal, Akshat, Srivastava, Ajit M., Sudan, Madhu, Syed, Mumtaz, Vikranth, Pulamathi, Yadav, Virendra
We present the results of a nation-wide baseline survey, conducted by us, for the status of Astronomy education among secondary school students in India. The survey was administered in 10 different languages to over 2000 students from diverse backgro
Externí odkaz:
http://arxiv.org/abs/2406.12308
Autor:
Gema, Aryo Pradipta, Leang, Joshua Ong Jun, Hong, Giwon, Devoto, Alessio, Mancino, Alberto Carlo Maria, Saxena, Rohit, He, Xuanli, Zhao, Yu, Du, Xiaotang, Madani, Mohammad Reza Ghasemi, Barale, Claire, McHardy, Robert, Harris, Joshua, Kaddour, Jean, van Krieken, Emile, Minervini, Pasquale
Maybe not. We identify and analyse errors in the popular Massive Multitask Language Understanding (MMLU) benchmark. Even though MMLU is widely adopted, our analysis demonstrates numerous ground truth errors that obscure the true capabilities of LLMs.
Externí odkaz:
http://arxiv.org/abs/2406.04127