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Going deeper with convolutions
We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition…
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Rethinking the Inception Architecture for Computer Vision
TLDR
This work is exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TLDR
The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields.
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Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
TLDR
Clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly is given and several new streamlined architectures for both residual and non-residual Inception Networks are presented.
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Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
TLDR
This article provides an overview of progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.
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Deep Neural Networks for Acoustic Modeling in Speech Recognition
TLDR
This paper provides an overview of this progress and repres nts the shared views of four research groups who have had recent successes in using deep neural networks for a coustic modeling in speech recognition.
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
TLDR
QT-Opt is introduced, a scalable self-supervised vision-based reinforcement learning framework that can leverage over 580k real-world grasp attempts to train a deep neural network Q-function with over 1.2M parameters to perform closed-loop, real- world grasping that generalizes to 96% grasp success on unseen objects.
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YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video
TLDR
A new large-scale data set of video URLs with densely-sampled object bounding box annotations called YouTube-BoundingBoxes (YT-BB), which consists of approximately 380,000 video segments automatically selected to feature objects in natural settings without editing or post-processing.
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Improving the speed of neural networks on CPUs
TLDR
This paper uses speech recognition as an example task, and shows that a real-time hybrid hidden Markov model / neural network (HMM/NN) large vocabulary system can be built with a 10× speedup over an unoptimized baseline and a 4× speed up over an aggressively optimized floating-point baseline at no cost in accuracy.
Sim-to-Real: Learning Agile Locomotion For Quadruped Robots
TLDR
This system can learn quadruped locomotion from scratch using simple reward signals and users can provide an open loop reference to guide the learning process when more control over the learned gait is needed.
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