367 research outputs found
Calibrated Prediction Intervals for Neural Network Regressors
Ongoing developments in neural network models are continually advancing the
state of the art in terms of system accuracy. However, the predicted labels
should not be regarded as the only core output; also important is a
well-calibrated estimate of the prediction uncertainty. Such estimates and
their calibration are critical in many practical applications. Despite their
obvious aforementioned advantage in relation to accuracy, contemporary neural
networks can, generally, be regarded as poorly calibrated and as such do not
produce reliable output probability estimates. Further, while post-processing
calibration solutions can be found in the relevant literature, these tend to be
for systems performing classification. In this regard, we herein present two
novel methods for acquiring calibrated predictions intervals for neural network
regressors: empirical calibration and temperature scaling. In experiments using
different regression tasks from the audio and computer vision domains, we find
that both our proposed methods are indeed capable of producing calibrated
prediction intervals for neural network regressors with any desired confidence
level, a finding that is consistent across all datasets and neural network
architectures we experimented with. In addition, we derive an additional
practical recommendation for producing more accurate calibrated prediction
intervals. We release the source code implementing our proposed methods for
computing calibrated predicted intervals. The code for computing calibrated
predicted intervals is publicly available
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks
auDeep is a Python toolkit for deep unsupervised representation learning from
acoustic data. It is based on a recurrent sequence to sequence autoencoder
approach which can learn representations of time series data by taking into
account their temporal dynamics. We provide an extensive command line interface
in addition to a Python API for users and developers, both of which are
comprehensively documented and publicly available at
https://github.com/auDeep/auDeep. Experimental results indicate that auDeep
features are competitive with state-of-the art audio classification
Tetraphosphabenzenes Obtained via a Triphosphacyclobutadiene Intermediate
An acyl triphosphirene ligand transfers an O atom to Nb to liberate the putative triphosphacyclobutadiene intermediate [RCP3{W(CO)5}2], which engages in [2+4]-cycloaddition reactions with an organic diene and a phosphaalkyne (see scheme; P orange, O red, W violet, C white). The latter reaction yields the Dewar isomer of a tetraphosphabenzene, which can be converted to a tetraphosphabenzvalene containing a Z-diphosphene.National Science Foundation (U.S.) (grant CHE-719157
Continuous emotion recognition in speech: do we need recurrence?
Emotion recognition in speech is a meaningful task in affective computing and human-computer interaction. As human emotion is a frequently changing state, it is usually represented as a densely sampled time series of emotional dimensions, typically arousal and valence. For this, recurrent neural network (RNN) architectures are employed by default when it comes to modelling the contours with deep learning approaches. However, the amount of temporal context required is questionable, and it has not yet been clarified whether the consideration of long-term dependencies is actually beneficial. In this contribution, we demonstrate that RNNs are not necessary to accomplish the task of time-continuous emotion recognition. Indeed, results gained indicate that deep neural networks incorporating less complex convolutional layers can provide more accurate models. We highlight the pros and cons of recurrent and non-recurrent approaches and evaluate our methods on the public SEWA database, which was used as a benchmark in the 2017 and 2018 editions of the Audio-Visual Emotion Challenge.ISSN: 1990-9772, Pages 2808-281
An investigation of cross-cultural semi-supervised learning for continuous affect recognition
One of the keys for supervised learning techniques to succeed resides in the access to vast amounts of labelled training data. The process of data collection, however, is expensive, time- consuming, and application dependent. In the current digital era, data can be collected continuously. This continuity renders data annotation into an endless task, which potentially, in problems such as emotion recognition, requires annotators with different cultural backgrounds. Herein, we study the impact of utilising data from different cultures in a semi-supervised learning ap- proach to label training material for the automatic recognition of arousal and valence. Specifically, we compare the performance of culture-specific affect recognition models trained with man- ual or cross-cultural automatic annotations. The experiments performed in this work use the dataset released for the Cross- cultural Emotion Sub-challenge of the Audio/Visual Emotion Challenge (AVEC) 2019. The results obtained convey that the cultures used for training impact on the system performance. Furthermore, in most of the scenarios assessed, affect recogni- tion models trained with hybrid solutions, combining manual and automatic annotations, surpass the baseline model, which was exclusively trained with manual annotations
Squeeze for sneeze: compact neural networks for cold and flu recognition
A paper in INTERSPEECH 202
Enhancing transferability of black-box adversarial attacks via lifelong learning for speech emotion recognition models
A paper in INTERSPEECH 2020
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