Zobrazeno 1 - 10
of 58 970
pro vyhledávání: '"Mousavi AS"'
Autor:
Mousavi, Ali, Elvira, Víctor
Importance sampling (IS) is a powerful Monte Carlo (MC) technique for approximating intractable integrals, for instance in Bayesian inference. The performance of IS relies heavily on the appropriate choice of the so-called proposal distribution. Adap
Externí odkaz:
http://arxiv.org/abs/2412.19576
ImageNet, an influential dataset in computer vision, is traditionally evaluated using single-label classification, which assumes that an image can be adequately described by a single concept or label. However, this approach may not fully capture the
Externí odkaz:
http://arxiv.org/abs/2412.18409
In this work, we develop a novel neural operator, the Solute Transport Operator Network (STONet), to efficiently model contaminant transport in micro-cracked reservoirs. The model combines different networks to encode heterogeneous properties effecti
Externí odkaz:
http://arxiv.org/abs/2412.05576
Binary classification tasks with imbalanced classes pose significant challenges in machine learning. Traditional classifiers often struggle to accurately capture the characteristics of the minority class, resulting in biased models with subpar predic
Externí odkaz:
http://arxiv.org/abs/2412.01936
Reinforcement Learning (RL) has emerged as a powerful paradigm in Artificial Intelligence (AI), enabling agents to learn optimal behaviors through interactions with their environments. Drawing from the foundations of trial and error, RL equips agents
Externí odkaz:
http://arxiv.org/abs/2411.18892
Autor:
Salmanpour, Mohammad R., Gorji, Arman, Mousavi, Amin, Jouzdani, Ali Fathi, Sanati, Nima, Maghsudi, Mehdi, Leung, Bonnie, Ho, Cheryl, Yuan, Ren, Rahmim, Arman
Objective: This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets to enhance lung cancer (LCa) survival predictions, analyzing Handcrafted and Deep Radiomic Features (HRF/DRF) from PET/CT scans with Hybri
Externí odkaz:
http://arxiv.org/abs/2412.00068
We explore the collaborative dynamics of an innovative language model interaction system involving advanced models such as GPT-4-0125-preview, Meta-LLaMA-3-70B-Instruct, Claude-3-Opus, and Gemini-1.5-Flash. These models generate and answer complex, P
Externí odkaz:
http://arxiv.org/abs/2411.16797
Autor:
Salmanpour, Mohammad R., Alizadeh, Morteza, Mousavi, Ghazal, Sadeghi, Saba, Amiri, Sajad, Oveisi, Mehrdad, Rahmim, Arman, Hacihaliloglu, Ilker
This study evaluates metrics for tasks such as classification, regression, clustering, correlation analysis, statistical tests, segmentation, and image-to-image (I2I) translation. Metrics were compared across Python libraries, R packages, and Matlab
Externí odkaz:
http://arxiv.org/abs/2411.12032
Large Language Models (LLMs) have demonstrated impressive capabilities across diverse Natural Language Processing (NLP) tasks, including language understanding, reasoning, and generation. However, general-domain LLMs often struggle with financial tas
Externí odkaz:
http://arxiv.org/abs/2411.02476
Safety Verification for Evasive Collision Avoidance in Autonomous Vehicles with Enhanced Resolutions
Autor:
Arab, Aliasghar, Khaleghi, Milad, Partovi, Alireza, Abbaspour, Alireza, Shinde, Chaitanya, Mousavi, Yashar, Azimi, Vahid, Karimmoddini, Ali
This paper presents a comprehensive hazard analysis, risk assessment, and loss evaluation for an Evasive Minimum Risk Maneuvering (EMRM) system designed for autonomous vehicles. The EMRM system is engineered to enhance collision avoidance and mitigat
Externí odkaz:
http://arxiv.org/abs/2411.02706