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Found 9 papers, 6 papers with code
Automap: Towards Ergonomic Automated Parallelism for ML Models
The rapid rise in demand for training large neural network architectures has brought into focus the need for partitioning strategies, for example by using data, model, or pipeline parallelism.
Memory-efficient array redistribution through portable collective communication
In this paper we address the problem of redistributing multi-dimensional array data in SPMD computations, the most prevalent form of parallelism in deep learning.
Decomposing reverse-mode automatic differentiation
We decompose reverse-mode automatic differentiation into (forward-mode) linearization followed by transposition.
Tensors Fitting Perfectly
Multidimensional arrays (NDArrays) are a central abstraction in modern scientific computing environments.
PyTorch Distributed: Experiences on Accelerating Data Parallel Training
This paper presents the design, implementation, and evaluation of the PyTorch distributed data parallel module.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Deep learning frameworks have often focused on either usability or speed, but not both.
Automatic Differentiation in PyTorch
In this article, we describe an automatic differentiation module of PyTorch — a library designed to enable rapid research on machine learning models.
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
The ability to perform pixel-wise semantic segmentation in real-time is of paramount importance in mobile applications.
Ranked #9 on
Semantic Segmentation
on ScanNetV2
An Analysis of Deep Neural Network Models for Practical Applications
Since the emergence of Deep Neural Networks (DNNs) as a prominent technique in the field of computer vision, the ImageNet classification challenge has played a major role in advancing the state-of-the-art.
