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Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction | Proceedings of the AAAI Conference on Artificial Intelligence
| | futurism.com
Biggest Neural Network Ever Pushes AI Deep Learning
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| | Digital Reasoning, a cognitive computing company, just announced that it has trained a neural network consisting of 160 billion parameters-more than 10 times larger than previous neural networks.
| | kpzhang93.github.io
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
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| | Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations and occlusions. Recent studies show that deep learning approaches can achieve impressive performance on these two tasks. In this paper, we propose a deep cascaded multi-task framework which exploits the inherent correlation between detection and alignment to boost up their performance. In particular, our framework leverages a cascaded architecture with three stages of carefully designed deep convolutional networks to predict face and landmark location in a coarse-to-fine manner. In addition, we propose a new online hard sample mining strategy that further improves the performance in practice. Our method achieves superior accuracy over the state-of-th...
| | polukhin.tech
Lightweight Neural Network Architectures | Andrii Polukhin
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| | As the field of Deep Learning continues to grow, the demand for efficient and lightweight neural networks becomes increasingly important. In this blog post, we will explore six lightweight neural network architectures.
| | www.khanna.law
Structuring Deep Learning Projects
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| You want to train a deep neural network. You have the data. It's labeled and wrangled into a useful format. What do you do now?