285 research outputs found
A Deep Architecture for Semantic Parsing
Many successful approaches to semantic parsing build on top of the syntactic
analysis of text, and make use of distributional representations or statistical
models to match parses to ontology-specific queries. This paper presents a
novel deep learning architecture which provides a semantic parsing system
through the union of two neural models of language semantics. It allows for the
generation of ontology-specific queries from natural language statements and
questions without the need for parsing, which makes it especially suitable to
grammatically malformed or syntactically atypical text, such as tweets, as well
as permitting the development of semantic parsers for resource-poor languages.Comment: In Proceedings of the Semantic Parsing Workshop at ACL 2014
(forthcoming
Bivariate Beta-LSTM
Long Short-Term Memory (LSTM) infers the long term dependency through a cell
state maintained by the input and the forget gate structures, which models a
gate output as a value in [0,1] through a sigmoid function. However, due to the
graduality of the sigmoid function, the sigmoid gate is not flexible in
representing multi-modality or skewness. Besides, the previous models lack
modeling on the correlation between the gates, which would be a new method to
adopt inductive bias for a relationship between previous and current input.
This paper proposes a new gate structure with the bivariate Beta distribution.
The proposed gate structure enables probabilistic modeling on the gates within
the LSTM cell so that the modelers can customize the cell state flow with
priors and distributions. Moreover, we theoretically show the higher upper
bound of the gradient compared to the sigmoid function, and we empirically
observed that the bivariate Beta distribution gate structure provides higher
gradient values in training. We demonstrate the effectiveness of bivariate Beta
gate structure on the sentence classification, image classification, polyphonic
music modeling, and image caption generation.Comment: AAAI 202
Interpretable Transformations with Encoder-Decoder Networks
Deep feature spaces have the capacity to encode complex transformations of
their input data. However, understanding the relative feature-space
relationship between two transformed encoded images is difficult. For instance,
what is the relative feature space relationship between two rotated images?
What is decoded when we interpolate in feature space? Ideally, we want to
disentangle confounding factors, such as pose, appearance, and illumination,
from object identity. Disentangling these is difficult because they interact in
very nonlinear ways. We propose a simple method to construct a deep feature
space, with explicitly disentangled representations of several known
transformations. A person or algorithm can then manipulate the disentangled
representation, for example, to re-render an image with explicit control over
parameterized degrees of freedom. The feature space is constructed using a
transforming encoder-decoder network with a custom feature transform layer,
acting on the hidden representations. We demonstrate the advantages of explicit
disentangling on a variety of datasets and transformations, and as an aid for
traditional tasks, such as classification.Comment: Accepted at ICCV 201
MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer
We introduce MIDI-VAE, a neural network model based on Variational
Autoencoders that is capable of handling polyphonic music with multiple
instrument tracks, as well as modeling the dynamics of music by incorporating
note durations and velocities. We show that MIDI-VAE can perform style transfer
on symbolic music by automatically changing pitches, dynamics and instruments
of a music piece from, e.g., a Classical to a Jazz style. We evaluate the
efficacy of the style transfer by training separate style validation
classifiers. Our model can also interpolate between short pieces of music,
produce medleys and create mixtures of entire songs. The interpolations
smoothly change pitches, dynamics and instrumentation to create a harmonic
bridge between two music pieces. To the best of our knowledge, this work
represents the first successful attempt at applying neural style transfer to
complete musical compositions.Comment: Paper accepted at the 19th International Society for Music
Information Retrieval Conference, ISMIR 2018, Paris, Franc
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