357 research outputs found
Towards More Efficient DNN-Based Speech Enhancement Using Quantized Correlation Mask
Many studies on deep learning-based speech enhancement (SE) utilizing the computational auditory scene analysis method typically employs the ideal binary mask or the ideal ratio mask to reconstruct the enhanced speech signal. However, many SE applications in real scenarios demand a desirable balance between denoising capability and computational cost. In this study, first, an improvement over the ideal ratio mask to attain more superior SE performance is proposed through introducing an efficient adaptive correlation-based factor for adjusting the ratio mask. The proposed method exploits the correlation coefficients among the noisy speech, noise and clean speech to effectively re-distribute the power ratio of the speech and noise during the ratio mask construction phase. Second, to make the supervised SE system more computationally-efficient, quantization techniques are considered to reduce the number of bits needed to represent floating numbers, leading to a more compact SE model. The proposed quantized correlation mask is utilized in conjunction with a 4-layer deep neural network (DNN-QCM) comprising dropout regulation, pre-training and noise-aware training to derive a robust and high-order mapping in enhancement, and to improve generalization capability in unseen conditions. Results show that the quantized correlation mask outperforms the conventional ratio mask representation and the other SE algorithms used for comparison. When compared to a DNN with ideal ratio mask as its learning targets, the DNN-QCM provided an improvement of approximately 6.5% in the short-time objective intelligibility score and 11.0% in the perceptual evaluation of speech quality score. The introduction of the quantization method can reduce the neural network weights to a 5-bit representation from a 32-bit, while effectively suppressing stationary and non-stationary noise. Timing analyses also show that with the techniques incorporated in the proposed DNN-QCM system to increase its compac..
PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model
Autoregressive models for text sometimes generate repetitive and low-quality
output because errors accumulate during the steps of generation. This issue is
often attributed to exposure bias - the difference between how a model is
trained, and how it is used during inference. Denoising diffusion models
provide an alternative approach in which a model can revisit and revise its
output. However, they can be computationally expensive and prior efforts on
text have led to models that produce less fluent output compared to
autoregressive models, especially for longer text and paragraphs. In this
paper, we propose PLANNER, a model that combines latent semantic diffusion with
autoregressive generation, to generate fluent text while exercising global
control over paragraphs. The model achieves this by combining an autoregressive
"decoding" module with a "planning" module that uses latent diffusion to
generate semantic paragraph embeddings in a coarse-to-fine manner. The proposed
method is evaluated on various conditional generation tasks, and results on
semantic generation, text completion and summarization show its effectiveness
in generating high-quality long-form text in an efficient manner.Comment: Accepted by NeurIPS 202
An analysis-ready and quality controlled resource for pediatric brain white-matter research
We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets
Statistical Atmospheric Parameter Retrieval Largely Benefits from Spatial-Spectral Image Compression
The Infrared Atmospheric Sounding Interferometer
(IASI) is flying on board of the Metop satellite series, which is
part of the EUMETSAT Polar System (EPS). Products obtained
from IASI data represent a significant improvement in the
accuracy and quality of the measurements used for meteorological models. Notably, IASI collects rich spectral information to
derive temperature and moisture profiles –among other relevant
trace gases–, essential for atmospheric forecasts and for the
understanding of weather. Here, we investigate the impact of
near-lossless and lossy compression on IASI L1C data when
statistical retrieval algorithms are later applied. We search for
those compression ratios that yield a positive impact on the
accuracy of the statistical retrievals. The compression techniques
help reduce certain amount of noise on the original data and,
at the same time, incorporate spatial-spectral feature relations in
an indirect way without increasing the computational complexity.
We observed that compressing images, at relatively low bitrates, improves results in predicting temperature and dew point
temperature, and we advocate that some amount of compression
prior to model inversion is beneficial. This research can benefit
the development of current and upcoming retrieval chains in
infrared sounding and hyperspectral sensors
An analysis-ready and quality controlled resource for pediatric brain white-matter research
We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets
MetaRec: Meta-Learning Meets Recommendation Systems
Artificial neural networks (ANNs) have recently received increasing attention as powerful modeling tools to improve the performance of recommendation systems. Meta-learning, on the other hand, is a paradigm that has re-surged in popularity within the broader machine learning community over the past several years. In this thesis, we will explore the intersection of these two domains and work on developing methods for integrating meta-learning to design more accurate and flexible recommendation systems.
In the present work, we propose a meta-learning framework for the design of collaborative filtering methods in recommendation systems, drawing from ideas, models, and solutions from modern approaches in both the meta-learning and recommendation system literature, applying them to recommendation tasks to obtain improved generalization performance.
Our proposed framework, MetaRec, includes and unifies the main state-of-the-art models in recommendation systems, extending them to be flexibly configured and efficiently operate with limited data. We empirically test the architectures created under our MetaRec framework on several recommendation benchmark datasets using a plethora of evaluation metrics and find that by taking a meta-learning approach to the collaborative filtering problem, we observe notable gains in predictive performance
Developing deep learning computational tools for cancer using omics data
Dissertação de mestrado em Computer ScienceThere has been an increasing investment in cancer research that generated an enormous
amount of biological and clinical data, especially after the advent of the next-generation
sequencing technologies. To analyze the large datasets provided by omics data of cancer
samples, scientists have successfully been recurring to machine learning algorithms, identifying
patterns and developing models by using statistical techniques to make accurate
predictions.
Deep learning is a branch of machine learning, best known by its applications in artificial
intelligence (computer vision, speech recognition, natural language processing and
robotics). In general, deep learning models differ from machine learning “shallow” methods
(single hidden layer) because they recur to multiple layers of abstraction. In this way, it
is possible to learn high level features and complex relations in the given data.
Given the context specified above, the main target of this work is the development and
evaluation of deep learning methods for the analysis of cancer omics datasets, covering both
unsupervised methods for feature generation from different types of data, and supervised
methods to address cancer diagnostics and prognostic predictions.
We worked with a Neuroblastoma (NB) dataset from two different platforms (RNA-Seq
and microarrays) and developed both supervised (Deep Neural Networks (DNN), Multi-Task
Deep Neural Network (MT-DNN)) and unsupervised (Stacked Denoising Autoencoders (SDA))
deep architectures, and compared them with shallow traditional algorithms.
Overall we achieved promising results with deep learning on both platforms, meaning
that it is possible to retrieve the advantages of deep learning models on cancer omics data.
At the same time we faced some difficulties related to the complexity and computational
power requirements, as well as the lack of samples to truly benefit from the deep architectures.
There was generated code that can be applied to other datasets, wich is available in a
github repository https://github.com/lmpeixoto/deepl_learning [49].Nos Ăşltimos anos tem havido um investimento significativo na pesquisa de cancro, o
que gerou uma quantidade enorme de dados biolĂłgicos e clĂnicos, especialmente apĂłs o
aparecimento das tecnologias de sequenciação denominadas de “próxima-geração”. Para
analisar estes dados, a comunidade cientĂfica tem recorrido, e com sucesso, a algoritmos
de aprendizado de máquina, identificando padrões e desenvolvendo modelos com recurso
a mĂ©todos estatĂsticos. Com estes modelos Ă© possĂvel fazer previsĂŁo de resultados. O aprendizado
profundo, um ramo do aprendizado de máquina, tem sido mais notório pelas suas
aplicações em inteligência artificial (reconhecimento de imagens e voz, processamento de
linguagem natural e robĂłtica). De um modo geral, os modelos de aprendizado profundo
diferem dos métodos clássicos do aprendizado de máquina por recorrerem a várias camadas
de abstração. Desta forma, Ă© possĂvel “aprender” as representações complexas e
não lineares, com vários graus de liberdade dos dados analisados. Neste contexto, o objetivo
principal deste trabalho é desenvolver e avaliar métodos de aprendizado profundo para
analisar dados ómicos do cancro. Pretendem-se desenvolver tanto métodos supervisionados
como não-supervisionados e utilizar diferentes tipos de dados, construindo soluções
para diagnóstico e prognóstico do cancro. Para isso trabalhámos com uma matriz de dados
de Neuroblastoma, proveniente de duas plataformas diferentes (RNA-seq e microarrays),
nos quais aplicámos algumas arquiteturas de aprendizado profundo, tanto como métodos
supervisionados e não-supervisionados, e com as quais comparámos com algoritmos tradicionais
de aprendizado de máquina. No geral conseguimos obter resultados promissores
nas duas plataformas, o que significou ser possĂvel beneficiar das vantagens dos modelos
do aprendizado profundo nos dados ómicos de cancro. Ao mesmo tempo encontrámos
algumas dificuldades, de modo especial relacionadas com a complexidade dos modelos e
o poder computacional exigido, bem como o baixo nĂşmero de amostras disponĂveis. Na
sequencia deste trabalho foi gerado código que pode ser aplicado a outros dados e está
disponĂvel num repositĂłrio do github https://github.com/lmpeixoto/deepl_learning
[49]
NOVEL APPLICATIONS OF MACHINE LEARNING IN BIOINFORMATICS
Technological advances in next-generation sequencing and biomedical imaging have led to a rapid increase in biomedical data dimension and acquisition rate, which is challenging the conventional data analysis strategies. Modern machine learning techniques promise to leverage large data sets for finding hidden patterns within them, and for making accurate predictions. This dissertation aims to design novel machine learning-based models to transform biomedical big data into valuable biological insights. The research presented in this dissertation focuses on three bioinformatics domains: splice junction classification, gene regulatory network reconstruction, and lesion detection in mammograms.
A critical step in defining gene structures and mRNA transcript variants is to accurately identify splice junctions. In the first work, we built the first deep learning-based splice junction classifier, DeepSplice. It outperforms the state-of-the-art classification tools in terms of both classification accuracy and computational efficiency. To uncover transcription factors governing metabolic reprogramming in non-small-cell lung cancer patients, we developed TFmeta, a machine learning approach to reconstruct relationships between transcription factors and their target genes in the second work. Our approach achieves the best performance on benchmark data sets. In the third work, we designed deep learning-based architectures to perform lesion detection in both 2D and 3D whole mammogram images
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
- …