4,812 research outputs found
DDLSTM: Dual-Domain LSTM for Cross-Dataset Action Recognition
Domain alignment in convolutional networks aims to learn the degree of
layer-specific feature alignment beneficial to the joint learning of source and
target datasets. While increasingly popular in convolutional networks, there
have been no previous attempts to achieve domain alignment in recurrent
networks. Similar to spatial features, both source and target domains are
likely to exhibit temporal dependencies that can be jointly learnt and aligned.
In this paper we introduce Dual-Domain LSTM (DDLSTM), an architecture that is
able to learn temporal dependencies from two domains concurrently. It performs
cross-contaminated batch normalisation on both input-to-hidden and
hidden-to-hidden weights, and learns the parameters for cross-contamination,
for both single-layer and multi-layer LSTM architectures. We evaluate DDLSTM on
frame-level action recognition using three datasets, taking a pair at a time,
and report an average increase in accuracy of 3.5%. The proposed DDLSTM
architecture outperforms standard, fine-tuned, and batch-normalised LSTMs.Comment: To appear in CVPR 201
Interpretable deep learning for guided structure-property explorations in photovoltaics
The performance of an organic photovoltaic device is intricately connected to
its active layer morphology. This connection between the active layer and
device performance is very expensive to evaluate, either experimentally or
computationally. Hence, designing morphologies to achieve higher performances
is non-trivial and often intractable. To solve this, we first introduce a deep
convolutional neural network (CNN) architecture that can serve as a fast and
robust surrogate for the complex structure-property map. Several tests were
performed to gain trust in this trained model. Then, we utilize this fast
framework to perform robust microstructural design to enhance device
performance.Comment: Workshop on Machine Learning for Molecules and Materials (MLMM),
Neural Information Processing Systems (NeurIPS) 2018, Montreal, Canad
Deep Learning Techniques for Music Generation -- A Survey
This paper is a survey and an analysis of different ways of using deep
learning (deep artificial neural networks) to generate musical content. We
propose a methodology based on five dimensions for our analysis:
Objective - What musical content is to be generated? Examples are: melody,
polyphony, accompaniment or counterpoint. - For what destination and for what
use? To be performed by a human(s) (in the case of a musical score), or by a
machine (in the case of an audio file).
Representation - What are the concepts to be manipulated? Examples are:
waveform, spectrogram, note, chord, meter and beat. - What format is to be
used? Examples are: MIDI, piano roll or text. - How will the representation be
encoded? Examples are: scalar, one-hot or many-hot.
Architecture - What type(s) of deep neural network is (are) to be used?
Examples are: feedforward network, recurrent network, autoencoder or generative
adversarial networks.
Challenge - What are the limitations and open challenges? Examples are:
variability, interactivity and creativity.
Strategy - How do we model and control the process of generation? Examples
are: single-step feedforward, iterative feedforward, sampling or input
manipulation.
For each dimension, we conduct a comparative analysis of various models and
techniques and we propose some tentative multidimensional typology. This
typology is bottom-up, based on the analysis of many existing deep-learning
based systems for music generation selected from the relevant literature. These
systems are described and are used to exemplify the various choices of
objective, representation, architecture, challenge and strategy. The last
section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P.
Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music
Generation, Computational Synthesis and Creative Systems, Springer, 201
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