Zobrazeno 1 - 10
of 1 387
pro vyhledávání: '"Toshev, A."'
Large pretrained models are showing increasingly better performance in reasoning and planning tasks across different modalities, opening the possibility to leverage them for complex sequential decision making problems. In this paper, we investigate t
Externí odkaz:
http://arxiv.org/abs/2410.05656
Autor:
Li, Jeffrey, Fang, Alex, Smyrnis, Georgios, Ivgi, Maor, Jordan, Matt, Gadre, Samir, Bansal, Hritik, Guha, Etash, Keh, Sedrick, Arora, Kushal, Garg, Saurabh, Xin, Rui, Muennighoff, Niklas, Heckel, Reinhard, Mercat, Jean, Chen, Mayee, Gururangan, Suchin, Wortsman, Mitchell, Albalak, Alon, Bitton, Yonatan, Nezhurina, Marianna, Abbas, Amro, Hsieh, Cheng-Yu, Ghosh, Dhruba, Gardner, Josh, Kilian, Maciej, Zhang, Hanlin, Shao, Rulin, Pratt, Sarah, Sanyal, Sunny, Ilharco, Gabriel, Daras, Giannis, Marathe, Kalyani, Gokaslan, Aaron, Zhang, Jieyu, Chandu, Khyathi, Nguyen, Thao, Vasiljevic, Igor, Kakade, Sham, Song, Shuran, Sanghavi, Sujay, Faghri, Fartash, Oh, Sewoong, Zettlemoyer, Luke, Lo, Kyle, El-Nouby, Alaaeldin, Pouransari, Hadi, Toshev, Alexander, Wang, Stephanie, Groeneveld, Dirk, Soldaini, Luca, Koh, Pang Wei, Jitsev, Jenia, Kollar, Thomas, Dimakis, Alexandros G., Carmon, Yair, Dave, Achal, Schmidt, Ludwig, Shankar, Vaishaal
We introduce DataComp for Language Models (DCLM), a testbed for controlled dataset experiments with the goal of improving language models. As part of DCLM, we provide a standardized corpus of 240T tokens extracted from Common Crawl, effective pretrai
Externí odkaz:
http://arxiv.org/abs/2406.11794
Multimodal Large Language Models (MLLMs) have demonstrated a wide range of capabilities across many domains, including Embodied AI. In this work, we study how to best ground a MLLM into different embodiments and their associated action spaces, with t
Externí odkaz:
http://arxiv.org/abs/2406.07904
Autor:
McKinzie, Brandon, Gan, Zhe, Fauconnier, Jean-Philippe, Dodge, Sam, Zhang, Bowen, Dufter, Philipp, Shah, Dhruti, Du, Xianzhi, Peng, Futang, Weers, Floris, Belyi, Anton, Zhang, Haotian, Singh, Karanjeet, Kang, Doug, Jain, Ankur, Hè, Hongyu, Schwarzer, Max, Gunter, Tom, Kong, Xiang, Zhang, Aonan, Wang, Jianyu, Wang, Chong, Du, Nan, Lei, Tao, Wiseman, Sam, Yin, Guoli, Lee, Mark, Wang, Zirui, Pang, Ruoming, Grasch, Peter, Toshev, Alexander, Yang, Yinfei
In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. Through careful and comprehensive ablations of the image encoder, the v
Externí odkaz:
http://arxiv.org/abs/2403.09611
Autor:
Toshev, Artur P., Ramachandran, Harish, Erbesdobler, Jonas A., Galletti, Gianluca, Brandstetter, Johannes, Adams, Nikolaus A.
Particle-based fluid simulations have emerged as a powerful tool for solving the Navier-Stokes equations, especially in cases that include intricate physics and free surfaces. The recent addition of machine learning methods to the toolbox for solving
Externí odkaz:
http://arxiv.org/abs/2403.04750
Smoothed particle hydrodynamics (SPH) is omnipresent in modern engineering and scientific disciplines. SPH is a class of Lagrangian schemes that discretize fluid dynamics via finite material points that are tracked through the evolving velocity field
Externí odkaz:
http://arxiv.org/abs/2402.06275
Recent advances in image tokenizers, such as VQ-VAE, have enabled text-to-image generation using auto-regressive methods, similar to language modeling. However, these methods have yet to leverage pre-trained language models, despite their adaptabilit
Externí odkaz:
http://arxiv.org/abs/2311.16201
Autor:
Szot, Andrew, Schwarzer, Max, Agrawal, Harsh, Mazoure, Bogdan, Talbott, Walter, Metcalf, Katherine, Mackraz, Natalie, Hjelm, Devon, Toshev, Alexander
We show that large language models (LLMs) can be adapted to be generalizable policies for embodied visual tasks. Our approach, called Large LAnguage model Reinforcement Learning Policy (LLaRP), adapts a pre-trained frozen LLM to take as input text in
Externí odkaz:
http://arxiv.org/abs/2310.17722
Autor:
Fang, Alex, Jose, Albin Madappally, Jain, Amit, Schmidt, Ludwig, Toshev, Alexander, Shankar, Vaishaal
Large training sets have become a cornerstone of machine learning and are the foundation for recent advances in language modeling and multimodal learning. While data curation for pre-training is often still ad-hoc, one common paradigm is to first col
Externí odkaz:
http://arxiv.org/abs/2309.17425
Machine learning has been successfully applied to grid-based PDE modeling in various scientific applications. However, learned PDE solvers based on Lagrangian particle discretizations, which are the preferred approach to problems with free surfaces o
Externí odkaz:
http://arxiv.org/abs/2309.16342