Furthermore, researchers involved in exploring learning algorithms for neural networks are gradually uncovering general principles that allow a learning machine to be successful. Journal of Molecular Biology. ArXiv : 1708.08551. Neural network research slowed until computers achieved far greater processing power.
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Further, the use of irrational values for weights results in a machine with super-Turing power. It is a full generative model, generalized from abstract concepts flowing through the layers of the model, which is able to synthesize new examples in novel classes that look "reasonably" natural. Lecture Notes in Computer Science. 143 Once the encoding function fdisplaystyle f_theta of the first denoising auto encoder is learned and used to uncorrupt the input (corrupted input the second level can be trained. Markoff, John (November 23, 2012). Hochreiter., " Untersuchungen zu dynamischen neuronalen Netzen Diploma thesis. "The Machine Learning Dictionary". For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the results to identify cats in other images. "Unidirectional Long Short-Term Memory Recurrent Neural Network with Recurrent Output Layer for Low-Latency Speech Synthesis" (PDF). Thus, the model is fully differentiable and trains end-to-end.