Chainer: A Deep Learning Framework for Accelerating the Research Cycle
arXiv:1908.00213
Abstract
Software frameworks for neural networks play a key role in the development and application of deep learning methods. In this paper, we introduce the Chainer framework, which intends to provide a flexible, intuitive, and high performance means of implementing the full range of deep learning models needed by researchers and practitioners. Chainer provides acceleration using Graphics Processing Units with a familiar NumPy-like API through CuPy, supports general and dynamic models in Python through Define-by-Run, and also provides add-on packages for state-of-the-art computer vision models as well as distributed training.
Accepted for Applied Data Science Track in KDD'19
References in corpus (18)
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Distributed Representations of Words and Phrases and their Compositionality
- Auto-Encoding Variational Bayes
- Sequence to Sequence Learning with Neural Networks
- YOLOv3: An Incremental Improvement
- Playing Atari with Deep Reinforcement Learning
- DeepPose: Human Pose Estimation via Deep Neural Networks
- Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
- MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
- A Neural Conversational Model
- The Cityscapes Dataset for Semantic Urban Scene Understanding
- Revisiting Distributed Synchronous SGD
- PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation
- Horovod: fast and easy distributed deep learning in TensorFlow
- YOLO9000: Better, Faster, Stronger
- Pylearn2: a machine learning research library
- Pomegranate: fast and flexible probabilistic modeling in python
- ImageNet Training in Minutes
Cited by in corpus (7)
- ChainerRL: A Deep Reinforcement Learning Library
- Classifying near-threshold enhancement using deep neural network
- ESPnet-ST: All-in-One Speech Translation Toolkit
- A Study of Checkpointing in Large Scale Training of Deep Neural Networks
- Analysis of memory consumption by neural networks based on hyperparameters
- Team PFDet's Methods for Open Images Challenge 2019
- PrototypeML: A Neural Network Integrated Design and Development Environment