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pro vyhledávání: '"Askari P"'
Geospatial Data Fusion: Combining Lidar, SAR, and Optical Imagery with AI for Enhanced Urban Mapping
This study explores the integration of Lidar, Synthetic Aperture Radar (SAR), and optical imagery through advanced artificial intelligence techniques for enhanced urban mapping. By fusing these diverse geospatial datasets, we aim to overcome the limi
Externí odkaz:
http://arxiv.org/abs/2412.18994
Autor:
Askari, MohammadReza, Parsa, Navid
Abstract: The rising global temperatures caused by climate change significantly impact energy consumption and electricity generation. Fluctuating temperatures and frequent extreme weather events disrupt energy production and consumption patterns. Add
Externí odkaz:
http://arxiv.org/abs/2412.12235
Autor:
Hall, Melissa, Mañas, Oscar, Askari-Hemmat, Reyhane, Ibrahim, Mark, Ross, Candace, Astolfi, Pietro, Ifriqi, Tariq Berrada, Havasi, Marton, Benchetrit, Yohann, Ullrich, Karen, Braga, Carolina, Charnalia, Abhishek, Ryan, Maeve, Rabbat, Mike, Drozdzal, Michal, Verbeek, Jakob, Romero-Soriano, Adriana
As the use of text-to-image generative models increases, so does the adoption of automatic benchmarking methods used in their evaluation. However, while metrics and datasets abound, there are few unified benchmarking libraries that provide a framewor
Externí odkaz:
http://arxiv.org/abs/2412.10604
Autor:
Askari, Mohammad Taha, Lampe, Lutz
Optimizing the input probability distribution of a discrete-time channel is a standard step in the information-theoretic analysis of digital communication systems. Nevertheless, many practical communication systems transmit uniformly and independentl
Externí odkaz:
http://arxiv.org/abs/2412.09581
We address the task of hierarchical multi-label classification (HMC) of scientific documents at an industrial scale, where hundreds of thousands of documents must be classified across thousands of dynamic labels. The rapid growth of scientific public
Externí odkaz:
http://arxiv.org/abs/2412.05137
The Sherrington-Kirkpatrick spin-glass model used the replica symmetry method to find the phase transition of the system. In 1979-1980, Parisi proposed a solution based on replica symmetry breaking (RSB), which allowed him to identify the underlying
Externí odkaz:
http://arxiv.org/abs/2411.04567
Autor:
Ifriqi, Tariq Berrada, Astolfi, Pietro, Hall, Melissa, Askari-Hemmat, Reyhane, Benchetrit, Yohann, Havasi, Marton, Muckley, Matthew, Alahari, Karteek, Romero-Soriano, Adriana, Verbeek, Jakob, Drozdzal, Michal
Large-scale training of latent diffusion models (LDMs) has enabled unprecedented quality in image generation. However, the key components of the best performing LDM training recipes are oftentimes not available to the research community, preventing a
Externí odkaz:
http://arxiv.org/abs/2411.03177
Modern-world robotics involves complex environments where multiple autonomous agents must interact with each other and other humans. This necessitates advanced interactive multi-agent motion planning techniques. Generalized Nash equilibrium(GNE), a s
Externí odkaz:
http://arxiv.org/abs/2410.05554
In the context of few-shot classification, the goal is to train a classifier using a limited number of samples while maintaining satisfactory performance. However, traditional metric-based methods exhibit certain limitations in achieving this objecti
Externí odkaz:
http://arxiv.org/abs/2409.07989
Autor:
Askari, Arian, Meng, Chuan, Aliannejadi, Mohammad, Ren, Zhaochun, Kanoulas, Evangelos, Verberne, Suzan
Existing generative retrieval (GR) approaches rely on training-based indexing, i.e., fine-tuning a model to memorise the associations between a query and the document identifier (docid) of a relevant document. Training-based indexing has three limita
Externí odkaz:
http://arxiv.org/abs/2408.02152