Technical Papers

XNor-Net

ImageNet classification using binary convolutional neural networks.

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YOLO

We present YOLO, a new approach to object detection.

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YOLO 9000

We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories.

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Label Refinery

Among the three main components (data, labels, and models) of any supervised learning system, data and models have been the main subjects of active research.

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LCNN

Porting state of the art deep learning algorithms to resource constrained compute platforms (e.g. VR, AR, wearables) is extremely challenging.

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Skim-RNN

Inspired by the principles of speed reading, we introduce Skim-RNN, a recurrent neural network (RNN) that dynamically decides to update only a small fraction of the hidden state for relatively unimportant input tokens.

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BiDAF

Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query.

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Re3

Robust object tracking requires knowledge and understanding of the object being tracked: its appearance, its motion, and how it changes over time.

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