75,018 research outputs found
Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras
We introduce Kapre, Keras layers for audio and music signal preprocessing.
Music research using deep neural networks requires a heavy and tedious
preprocessing stage, for which audio processing parameters are often ignored in
parameter optimisation. To solve this problem, Kapre implements time-frequency
conversions, normalisation, and data augmentation as Keras layers. We report
simple benchmark results, showing real-time on-GPU preprocessing adds a
reasonable amount of computation.Comment: ICML 2017 machine learning for music discover
Variational System Identification for Nonlinear State-Space Models
This paper considers parameter estimation for nonlinear state-space models,
which is an important but challenging problem. We address this challenge by
employing a variational inference (VI) approach, which is a principled method
that has deep connections to maximum likelihood estimation. This VI approach
ultimately provides estimates of the model as solutions to an optimisation
problem, which is deterministic, tractable and can be solved using standard
optimisation tools. A specialisation of this approach for systems with additive
Gaussian noise is also detailed. The proposed method is examined numerically on
a range of simulated and real examples focusing on the robustness to parameter
initialisation; additionally, favourable comparisons are performed against
state-of-the-art alternatives
Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data
We analyse multimodal time-series data corresponding to weight, sleep and
steps measurements. We focus on predicting whether a user will successfully
achieve his/her weight objective. For this, we design several deep long
short-term memory (LSTM) architectures, including a novel cross-modal LSTM
(X-LSTM), and demonstrate their superiority over baseline approaches. The
X-LSTM improves parameter efficiency by processing each modality separately and
allowing for information flow between them by way of recurrent
cross-connections. We present a general hyperparameter optimisation technique
for X-LSTMs, which allows us to significantly improve on the LSTM and a prior
state-of-the-art cross-modal approach, using a comparable number of parameters.
Finally, we visualise the model's predictions, revealing implications about
latent variables in this task.Comment: To appear in NIPS ML4H 2017 and NIPS TSW 201
Practical Gauss-Newton Optimisation for Deep Learning
We present an efficient block-diagonal ap- proximation to the Gauss-Newton
matrix for feedforward neural networks. Our result- ing algorithm is
competitive against state- of-the-art first order optimisation methods, with
sometimes significant improvement in optimisation performance. Unlike
first-order methods, for which hyperparameter tuning of the optimisation
parameters is often a labo- rious process, our approach can provide good
performance even when used with default set- tings. A side result of our work
is that for piecewise linear transfer functions, the net- work objective
function can have no differ- entiable local maxima, which may partially explain
why such transfer functions facilitate effective optimisation.Comment: ICML 201
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