Zobrazeno 1 - 10
of 36 658
pro vyhledávání: '"Devendra, A."'
Graph Neural Networks (GNNs) are non-Euclidean deep learning models for graph-structured data. Despite their successful and diverse applications, oversmoothing prohibits deep architectures due to node features converging to a single fixed point. This
Externí odkaz:
http://arxiv.org/abs/2412.04064
Autor:
Dopiriak, Matúš, Šlapak, Eugen, Gazda, Juraj, Gurjar, Devendra S., Faruque, Mohammad Abdullah Al, Levorato, Marco
Connected and autonomous vehicles (CAVs) offload computationally intensive tasks to multi-access edge computing (MEC) servers via vehicle-to-infrastructure (V2I) communication, enabling applications within the vehicular metaverse, which transforms ph
Externí odkaz:
http://arxiv.org/abs/2411.11857
Autor:
Wüst, Antonia, Tobiasch, Tim, Helff, Lukas, Dhami, Devendra S., Rothkopf, Constantin A., Kersting, Kristian
Recently, newly developed Vision-Language Models (VLMs), such as OpenAI's GPT-4o, have emerged, seemingly demonstrating advanced reasoning capabilities across text and image modalities. Yet, the depth of these advances in language-guided perception a
Externí odkaz:
http://arxiv.org/abs/2410.19546
Time series data is prevalent across numerous fields, necessitating the development of robust and accurate forecasting models. Capturing patterns both within and between temporal and multivariate components is crucial for reliable predictions. We int
Externí odkaz:
http://arxiv.org/abs/2410.16928
Company fundamentals are key to assessing companies' financial and overall success and stability. Forecasting them is important in multiple fields, including investing and econometrics. While statistical and contemporary machine learning methods have
Externí odkaz:
http://arxiv.org/abs/2411.05791
Autor:
Dahiphale, Devendra, Madiraju, Naveen, Lin, Justin, Karve, Rutvik, Agrawal, Monu, Modwal, Anant, Balakrishnan, Ramanan, Shah, Shanay, Kaushal, Govind, Mandawat, Priya, Hariramani, Prakash, Merchant, Arif
Digital payment systems have revolutionized financial transactions, offering unparalleled convenience and accessibility to users worldwide. However, the increasing popularity of these platforms has also attracted malicious actors seeking to exploit t
Externí odkaz:
http://arxiv.org/abs/2410.19845
Autor:
Willig, Moritz, Tobiasch, Tim Nelson, Busch, Florian Peter, Seng, Jonas, Dhami, Devendra Singh, Kersting, Kristian
Most work on causality in machine learning assumes that causal relationships are driven by a constant underlying process. However, the flexibility of agents' actions or tipping points in the environmental process can change the qualitative dynamics o
Externí odkaz:
http://arxiv.org/abs/2410.13054
Humans can leverage both symbolic reasoning and intuitive reactions. In contrast, reinforcement learning policies are typically encoded in either opaque systems like neural networks or symbolic systems that rely on predefined symbols and rules. This
Externí odkaz:
http://arxiv.org/abs/2410.11689
Autor:
Agrawal, Pravesh, Antoniak, Szymon, Hanna, Emma Bou, Bout, Baptiste, Chaplot, Devendra, Chudnovsky, Jessica, Costa, Diogo, De Monicault, Baudouin, Garg, Saurabh, Gervet, Theophile, Ghosh, Soham, Héliou, Amélie, Jacob, Paul, Jiang, Albert Q., Khandelwal, Kartik, Lacroix, Timothée, Lample, Guillaume, Casas, Diego Las, Lavril, Thibaut, Scao, Teven Le, Lo, Andy, Marshall, William, Martin, Louis, Mensch, Arthur, Muddireddy, Pavankumar, Nemychnikova, Valera, Pellat, Marie, Von Platen, Patrick, Raghuraman, Nikhil, Rozière, Baptiste, Sablayrolles, Alexandre, Saulnier, Lucile, Sauvestre, Romain, Shang, Wendy, Soletskyi, Roman, Stewart, Lawrence, Stock, Pierre, Studnia, Joachim, Subramanian, Sandeep, Vaze, Sagar, Wang, Thomas, Yang, Sophia
We introduce Pixtral-12B, a 12--billion-parameter multimodal language model. Pixtral-12B is trained to understand both natural images and documents, achieving leading performance on various multimodal benchmarks, surpassing a number of larger models.
Externí odkaz:
http://arxiv.org/abs/2410.07073
Accurately predicting water table dynamics is vital for sustaining groundwater resources that support ecological functions and anthropogenic activities. This study evaluates a statistical model (BigVAR) that handles three major flexibilities: (a) pre
Externí odkaz:
http://arxiv.org/abs/2410.01001