Zobrazeno 1 - 10
of 50 423
pro vyhledávání: '"Alizadeh, A. A."'
Autor:
Alizadeh, Meysam, Asgari, Yasaman, Samei, Zeynab, Yari, Sara, Dehghani, Shirin, Kubli, Mael, Zare, Darya, Bermeo, Juan Diego, Batzdorfer, Veronika, Gilardi, Fabrizio
Academics increasingly acknowledge the predictive power of social media for a wide variety of events and, more specifically, for financial markets. Anecdotal and empirical findings show that cryptocurrencies are among the financial assets that have b
Externí odkaz:
http://arxiv.org/abs/2411.05577
Autor:
Chegini, Atoosa, Kazemi, Hamid, Mirzadeh, Iman, Yin, Dong, Horton, Maxwell, Nabi, Moin, Farajtabar, Mehrdad, Alizadeh, Keivan
In Large Language Model (LLM) development, Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning models with human values and preferences. RLHF traditionally relies on the Kullback-Leibler (KL) divergence between the current polic
Externí odkaz:
http://arxiv.org/abs/2411.01798
Deep reinforcement learning (DRL) is emerging as a promising method for adaptive robotic motion and complex task automation, effectively addressing the limitations of traditional control methods. However, ensuring safety throughout both the learning
Externí odkaz:
http://arxiv.org/abs/2410.20907
Autor:
Ashkboos, Saleh, Mirzadeh, Iman, Alizadeh, Keivan, Sekhavat, Mohammad Hossein, Nabi, Moin, Farajtabar, Mehrdad, Faghri, Fartash
While large language models (LLMs) dominate the AI landscape, Small-scale large Language Models (SLMs) are gaining attention due to cost and efficiency demands from consumers. However, there is limited research on the training behavior and computatio
Externí odkaz:
http://arxiv.org/abs/2410.19456
This paper proposes an AI-based scheme for islanding detection in active distribution networks. By reviewing existing studies, it is clear that there are several gaps in the field to ensure reliable islanding detection, including (i) model complexity
Externí odkaz:
http://arxiv.org/abs/2410.13926
Autor:
Ghiasvand, Sajjad, Yang, Yifan, Xue, Zhiyu, Alizadeh, Mahnoosh, Zhang, Zheng, Pedarsani, Ramtin
Parameter-efficient fine-tuning (PEFT) methods typically assume that Large Language Models (LLMs) are trained on data from a single device or client. However, real-world scenarios often require fine-tuning these models on private data distributed acr
Externí odkaz:
http://arxiv.org/abs/2410.13097
Autor:
Mirzadeh, Iman, Alizadeh, Keivan, Shahrokhi, Hooman, Tuzel, Oncel, Bengio, Samy, Farajtabar, Mehrdad
Recent advancements in Large Language Models (LLMs) have sparked interest in their formal reasoning capabilities, particularly in mathematics. The GSM8K benchmark is widely used to assess the mathematical reasoning of models on grade-school-level que
Externí odkaz:
http://arxiv.org/abs/2410.05229
Autor:
Dehghani, Mahshid, Shafiee, Amirahmad, Shafiei, Ali, Fallah, Neda, Alizadeh, Farahmand, Gholinejad, Mohammad Mehdi, Behroozi, Hamid, Habibi, Jafar, Asgari, Ehsaneddin
Existing 3D facial emotion modeling have been constrained by limited emotion classes and insufficient datasets. This paper introduces "Emo3D", an extensive "Text-Image-Expression dataset" spanning a wide spectrum of human emotions, each paired with i
Externí odkaz:
http://arxiv.org/abs/2410.02049
Autor:
Alizadeh, Keivan, Mirzadeh, Iman, Shahrokhi, Hooman, Belenko, Dmitry, Sun, Frank, Cho, Minsik, Sekhavat, Mohammad Hossein, Nabi, Moin, Farajtabar, Mehrdad
Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget, leading to inefficient resource utilization. To address this shortcoming, recent advancements in mixture of expert (MoE) models, speculative decoding
Externí odkaz:
http://arxiv.org/abs/2410.10846
We study the band structure and scattering of in-plane coupled longitudinal and shear stress waves in layered media and observe that exceptional points (EP) appear for elastic (lossless) media, when parameterized with real-valued frequency and tangen
Externí odkaz:
http://arxiv.org/abs/2409.16162