Zobrazeno 1 - 10
of 14 185
pro vyhledávání: '"Kleiman A"'
Autor:
Rong, Frieda, Kleiman-Weiner, Max
Social rewards shape human behavior. During development, a caregiver guides a learner's behavior towards culturally aligned goals and values. How do these behaviors persist and generalize when the caregiver is no longer present, and the learner must
Externí odkaz:
http://arxiv.org/abs/2407.14681
Autor:
Jin, Zhijing, Levine, Sydney, Kleiman-Weiner, Max, Piatti, Giorgio, Liu, Jiarui, Adauto, Fernando Gonzalez, Ortu, Francesco, Strausz, András, Sachan, Mrinmaya, Mihalcea, Rada, Choi, Yejin, Schölkopf, Bernhard
We evaluate the moral alignment of large language models (LLMs) with human preferences in multilingual trolley problems. Building on the Moral Machine experiment, which captures over 40 million human judgments across 200+ countries, we develop a cros
Externí odkaz:
http://arxiv.org/abs/2407.02273
Autor:
Bensadoun, Raphael, Monnier, Tom, Kleiman, Yanir, Kokkinos, Filippos, Siddiqui, Yawar, Kariya, Mahendra, Harosh, Omri, Shapovalov, Roman, Graham, Benjamin, Garreau, Emilien, Karnewar, Animesh, Cao, Ang, Azuri, Idan, Makarov, Iurii, Le, Eric-Tuan, Toisoul, Antoine, Novotny, David, Gafni, Oran, Neverova, Natalia, Vedaldi, Andrea
We introduce Meta 3D Gen (3DGen), a new state-of-the-art, fast pipeline for text-to-3D asset generation. 3DGen offers 3D asset creation with high prompt fidelity and high-quality 3D shapes and textures in under a minute. It supports physically-based
Externí odkaz:
http://arxiv.org/abs/2407.02599
Autor:
Siddiqui, Yawar, Monnier, Tom, Kokkinos, Filippos, Kariya, Mahendra, Kleiman, Yanir, Garreau, Emilien, Gafni, Oran, Neverova, Natalia, Vedaldi, Andrea, Shapovalov, Roman, Novotny, David
We present Meta 3D AssetGen (AssetGen), a significant advancement in text-to-3D generation which produces faithful, high-quality meshes with texture and material control. Compared to works that bake shading in the 3D object's appearance, AssetGen out
Externí odkaz:
http://arxiv.org/abs/2407.02445
Autor:
Bensadoun, Raphael, Kleiman, Yanir, Azuri, Idan, Harosh, Omri, Vedaldi, Andrea, Neverova, Natalia, Gafni, Oran
The recent availability and adaptability of text-to-image models has sparked a new era in many related domains that benefit from the learned text priors as well as high-quality and fast generation capabilities, one of which is texture generation for
Externí odkaz:
http://arxiv.org/abs/2407.02430
Autor:
Zhang, Edwin, Zhu, Vincent, Saphra, Naomi, Kleiman, Anat, Edelman, Benjamin L., Tambe, Milind, Kakade, Sham M., Malach, Eran
Generative models are trained with the simple objective of imitating the conditional probability distribution induced by the data they are trained on. Therefore, when trained on data generated by humans, we may not expect the artificial model to outp
Externí odkaz:
http://arxiv.org/abs/2406.11741
Autor:
Piatti, Giorgio, Jin, Zhijing, Kleiman-Weiner, Max, Schölkopf, Bernhard, Sachan, Mrinmaya, Mihalcea, Rada
As AI systems pervade human life, ensuring that large language models (LLMs) make safe decisions remains a significant challenge. We introduce the Governance of the Commons Simulation (GovSim), a generative simulation platform designed to study strat
Externí odkaz:
http://arxiv.org/abs/2404.16698
Autor:
Jin, Zhijing, Chen, Yuen, Leeb, Felix, Gresele, Luigi, Kamal, Ojasv, Lyu, Zhiheng, Blin, Kevin, Adauto, Fernando Gonzalez, Kleiman-Weiner, Max, Sachan, Mrinmaya, Schölkopf, Bernhard
The ability to perform causal reasoning is widely considered a core feature of intelligence. In this work, we investigate whether large language models (LLMs) can coherently reason about causality. Much of the existing work in natural language proces
Externí odkaz:
http://arxiv.org/abs/2312.04350
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract The Bio-Hermes Study was a cross-sectional observational study designed to develop a database of blood-based and digital biomarkers to improve detection of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). We examined the abili
Externí odkaz:
https://doaj.org/article/2d3a67b09fad47a68046d554ee780ad6
Autor:
Karthik Kanagaraj, Michelle A. Phillippi, Elizabeth H. Ober, Igor Shuryak, Norman J. Kleiman, John Olson, George Schaaf, J. Mark Cline, Helen C. Turner
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract There are currently no available FDA-cleared biodosimetry tools for rapid and accurate assessment of absorbed radiation dose following a radiation/nuclear incident. Previously we developed a protein biomarker-based FAST-DOSE bioassay system
Externí odkaz:
https://doaj.org/article/54ce1567c9eb483a953a551bb96d2cf9