16 code implementations • 18 Jul 2023 • Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Ranked #2 on Question Answering on PubChemQA
no code implementations • CVPR 2023 • Samarth Sinha, Roman Shapovalov, Jeremy Reizenstein, Ignacio Rocco, Natalia Neverova, Andrea Vedaldi, David Novotny
Obtaining photorealistic reconstructions of objects from sparse views is inherently ambiguous and can only be achieved by learning suitable reconstruction priors.
1 code implementation • ICCV 2021 • Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, David Novotny
Traditional approaches for learning 3D object categories have been predominantly trained and evaluated on synthetic datasets due to the unavailability of real 3D-annotated category-centric data.
no code implementations • CVPR 2021 • Philipp Henzler, Jeremy Reizenstein, Patrick Labatut, Roman Shapovalov, Tobias Ritschel, Andrea Vedaldi, David Novotny
Our goal is to learn a deep network that, given a small number of images of an object of a given category, reconstructs it in 3D.
3 code implementations • 16 Jul 2020 • Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan-Yen Lo, Justin Johnson, Georgia Gkioxari
We address these challenges by introducing PyTorch3D, a library of modular, efficient, and differentiable operators for 3D deep learning.
no code implementations • NeurIPS 2019 • Ben Graham, David Novotny, Jeremy Reizenstein
Given a set of a reference RGBD views of an indoor environment, and a new viewpoint, our goal is to predict the view from that location.
2 code implementations • 22 Feb 2018 • Jeremy Reizenstein, Benjamin Graham
Iterated-integral signatures and log signatures are vectors calculated from a path that characterise its shape.
Data Structures and Algorithms Mathematical Software Rings and Algebras
no code implementations • 18 Jan 2018 • Joscha Diehl, Jeremy Reizenstein
We introduce a novel class of features for multidimensional time series, that are invariant with respect to transformations of the ambient space.
2 code implementations • 7 Dec 2017 • Jeremy Reizenstein
We explain the algebra needed to make sense of the log signature of a path, with plenty of examples.
Rings and Algebras
no code implementations • 9 Feb 2015 • Ben Graham, Jeremy Reizenstein, Leigh Robinson
Dropout networks are generally trained by minibatch gradient descent with a dropout mask turning off some of the units---a different pattern of dropout is applied to every sample in the minibatch.