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Found 10 papers, 5 papers with code
Llama 2: Open Foundation and Fine-Tuned Chat Models
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.
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Common Pets in 3D: Dynamic New-View Synthesis of Real-Life Deformable Categories
Obtaining photorealistic reconstructions of objects from sparse views is inherently ambiguous and can only be achieved by learning suitable reconstruction priors.
Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction
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.
Unsupervised Learning of 3D Object Categories from Videos in the Wild
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.
Accelerating 3D Deep Learning with PyTorch3D
We address these challenges by introducing PyTorch3D, a library of modular, efficient, and differentiable operators for 3D deep learning.
PerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor Environments
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.
The iisignature library: efficient calculation of iterated-integral signatures and log signatures
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
Invariants of multidimensional time series based on their iterated-integral signature
We introduce a novel class of features for multidimensional time series, that are invariant with respect to transformations of the ambient space.
Calculation of Iterated-Integral Signatures and Log Signatures
We explain the algebra needed to make sense of the log signature of a path, with plenty of examples.
Rings and Algebras
Efficient batchwise dropout training using submatrices
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.
