Measuring abstract reasoning in neural networks

Neural network-based models continue to achieve impressive results on longstanding machine learning problems, but establishing their capacity to reason about abstract concepts has proven difficult. Building on previous efforts to solve this important feature of general-purpose learning systems, our latest paper sets out an approach for measuring abstract reasoning in learning machines, and reveals some important insights about the nature of generalisation itself.Read More

DeepMind papers at ICML 2018

The 2018 International Conference on Machine Learning will take place in Stockholm, Sweden from 10-15 July.For those attending and planning the week ahead, we are sharing a schedule of DeepMind presentations at ICML (you can download a pdf version here). We look forward to the many engaging discussions, ideas, and collaborations that are sure to arise from the conference!Efficient Neural Audio SynthesisAuthors: Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Nouri, Norman Casagrande, Edward Lockhart, Sander Dieleman, Aaron van den Oord, Koray KavukcuogluSequential models achieve state-of-the-art results in audio, visual and textual domains with respect to both estimating the data distribution and generating desired samples. Efficient sampling for this class of models at the cost of little to no loss in quality has however remained an elusive task. With a focus on text-to-speech synthesis, we show that compact recurrent architectures, a remarkably high degree of weight sparsification and a novel reordering of the variables greatly reduce sampling latency while maintaining high audio fidelity. We first describe a compact single-layer recurrent neural network, the WaveRNN, with a novel dual softmax layer that matches the quality of the state-of-the-art WaveNet model.Read More