Zobrazeno 1 - 10
of 24 051
pro vyhledávání: '"A Khandelwal"'
Deceptive patterns (DPs) in digital interfaces manipulate users into making unintended decisions, exploiting cognitive biases and psychological vulnerabilities. These patterns have become ubiquitous across various digital platforms. While efforts to
Externí odkaz:
http://arxiv.org/abs/2411.07441
Monitoring public sentiment via social media is potentially helpful during health crises such as the COVID-19 pandemic. However, traditional frequency-based, data-driven neural network-based approaches can miss newly relevant content due to the evolv
Externí odkaz:
http://arxiv.org/abs/2411.07163
Autor:
Grosnit, Antoine, Maraval, Alexandre, Doran, James, Paolo, Giuseppe, Thomas, Albert, Beevi, Refinath Shahul Hameed Nabeezath, Gonzalez, Jonas, Khandelwal, Khyati, Iacobacci, Ignacio, Benechehab, Abdelhakim, Cherkaoui, Hamza, El-Hili, Youssef Attia, Shao, Kun, Hao, Jianye, Yao, Jun, Kegl, Balazs, Bou-Ammar, Haitham, Wang, Jun
We introduce Agent K v1.0, an end-to-end autonomous data science agent designed to automate, optimise, and generalise across diverse data science tasks. Fully automated, Agent K v1.0 manages the entire data science life cycle by learning from experie
Externí odkaz:
http://arxiv.org/abs/2411.03562
Autor:
Khandelwal, Apoorv, Yun, Tian, Nayak, Nihal V., Merullo, Jack, Bach, Stephen H., Sun, Chen, Pavlick, Ellie
Pre-training is notoriously compute-intensive and academic researchers are notoriously under-resourced. It is, therefore, commonly assumed that academics can't pre-train models. In this paper, we seek to clarify this assumption. We first survey acade
Externí odkaz:
http://arxiv.org/abs/2410.23261
Autor:
Kumar, Shanu, Venkata, Akhila Yesantarao, Khandelwal, Shubhanshu, Santra, Bishal, Agrawal, Parag, Gupta, Manish
As large language models become increasingly central to solving complex tasks, the challenge of optimizing long, unstructured prompts has become critical. Existing optimization techniques often struggle to effectively handle such prompts, leading to
Externí odkaz:
http://arxiv.org/abs/2410.20788
In this paper, on the basis of a (Fenchel) duality theory on the continuous level, we derive an $\textit{a posteriori}$ error identity for arbitrary conforming approximations of a primal formulation and a dual formulation of variational problems invo
Externí odkaz:
http://arxiv.org/abs/2410.18780
We examine the speed of different boarding methods in a proposed Flying Wing aircraft design with four aisles using an agent-based model. We study the effect of various passenger movement variables on the boarding process. We evaluate the impact of t
Externí odkaz:
http://arxiv.org/abs/2410.17870
Autor:
Xu, Shaoming, Renganathan, Arvind, Khandelwal, Ankush, Ghosh, Rahul, Li, Xiang, Liu, Licheng, Tayal, Kshitij, Harrington, Peter, Jia, Xiaowei, Jin, Zhenong, Nieber, Jonh, Kumar, Vipin
Streamflow, vital for water resource management, is governed by complex hydrological systems involving intermediate processes driven by meteorological forces. While deep learning models have achieved state-of-the-art results of streamflow prediction,
Externí odkaz:
http://arxiv.org/abs/2410.14137
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
Autor:
Khandelwal, Trishia
Topic models are used to identify and group similar themes in a set of documents. Recent advancements in deep learning based neural topic models has received significant research interest. In this paper, an approach is proposed that further enhances
Externí odkaz:
http://arxiv.org/abs/2410.09063