InfoQ Homepage Neural Networks Content on InfoQ
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Facebook Builds an Efficient Neural Network Model over a Billion Words
Using Neural Networks for sequence prediction is a well-known Computer Science problem with a vast array of applications in speech recognition, machine translation, language modeling and other fields. FB AI Research scientists designed adaptive softmax, an approximation algorithm tailored for GPUs which can be used to efficiently train neural networks over vocabularies of a billion words & beyond.
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DeepMind AI Program Increases Google Data Center Cooling Power Usage Efficiency by 40%
DeepMind Sensor data captured from Google data centers yield a 40% increase in data center power usage efficiency and an overall site-wide 15% power usage efficiency gain using an AI program similar to an earlier game-like program of theirs that had learned how to play Atari games.
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Deep Convolutional Networks for Super-Resolution Image Reconstruction at Flipboard
Flipboard recently reported on an in-house application of deep learning to scale up low-resolution images that illustrates the power and flexibility of this class of learning algorithms.
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Nvidia Introduces cuDNN, a CUDA-based library for Deep Neural Networks
Nvidia earlier this month released cuDNN, a set of optimized low-level primitives to boost the processing speed of deep neural networks (DNN) on CUDA compatible GPUs. The company intends to help developers harness the power of graphics processing units for deep learning applications.