496 research outputs found
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.Comment: 13 pages, APSIPA 201
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction
of multiple target variables of diverse type. Due to its enormous application
potential, it has developed into an active and rapidly expanding research field
that combines several subfields of machine learning, including multivariate
regression, multi-label classification, multi-task learning, dyadic prediction,
zero-shot learning, network inference, and matrix completion. In this paper, we
present a unifying view on MTP problems and methods. First, we formally discuss
commonalities and differences between existing MTP problems. To this end, we
introduce a general framework that covers the above subfields as special cases.
As a second contribution, we provide a structured overview of MTP methods. This
is accomplished by identifying a number of key properties, which distinguish
such methods and determine their suitability for different types of problems.
Finally, we also discuss a few challenges for future research
Towards A Robust Group-level Emotion Recognition via Uncertainty-Aware Learning
Group-level emotion recognition (GER) is an inseparable part of human
behavior analysis, aiming to recognize an overall emotion in a multi-person
scene. However, the existing methods are devoted to combing diverse emotion
cues while ignoring the inherent uncertainties under unconstrained
environments, such as congestion and occlusion occurring within a group.
Additionally, since only group-level labels are available, inconsistent emotion
predictions among individuals in one group can confuse the network. In this
paper, we propose an uncertainty-aware learning (UAL) method to extract more
robust representations for GER. By explicitly modeling the uncertainty of each
individual, we utilize stochastic embedding drawn from a Gaussian distribution
instead of deterministic point embedding. This representation captures the
probabilities of different emotions and generates diverse predictions through
this stochasticity during the inference stage. Furthermore,
uncertainty-sensitive scores are adaptively assigned as the fusion weights of
individuals' face within each group. Moreover, we develop an image enhancement
module to enhance the model's robustness against severe noise. The overall
three-branch model, encompassing face, object, and scene component, is guided
by a proportional-weighted fusion strategy and integrates the proposed
uncertainty-aware method to produce the final group-level output. Experimental
results demonstrate the effectiveness and generalization ability of our method
across three widely used databases.Comment: 11 pages,3 figure
A REVIEW ON MULTIPLE-FEATURE-BASED ADAPTIVE SPARSE REPRESENTATION (MFASR) AND OTHER CLASSIFICATION TYPES
A new technique Multiple-feature-based adaptive sparse representation (MFASR) has been demonstrated for Hyperspectral Images (HSI's) classification. This method involves mainly in four steps at the various stages. The spectral and spatial information reflected from the original Hyperspectral Images with four various features. A shape adaptive (SA) spatial region is obtained in each pixel region at the second step. The algorithm namely sparse representation has applied to get the coefficients of sparse for each shape adaptive region in the form of matrix with multiple features. For each test pixel, the class label is determined with the help of obtained coefficients. The performances of MFASR have much better classification results than other classifiers in the terms of quantitative and qualitative percentage of results. This MFASR will make benefit of strong correlations that are obtained from different extracted features and this make use of effective features and effective adaptive sparse representation. Thus, the very high classification performance was achieved through this MFASR technique
EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion Recognition
Skin lesion recognition using deep learning has made remarkable progress, and
there is an increasing need for deploying these systems in real-world
scenarios. However, recent research has revealed that deep neural networks for
skin lesion recognition may overly depend on disease-irrelevant image artifacts
(i.e. dark corners, dense hairs), leading to poor generalization in unseen
environments. To address this issue, we propose a novel domain generalization
method called EPVT, which involves embedding prompts into the vision
transformer to collaboratively learn knowledge from diverse domains.
Concretely, EPVT leverages a set of domain prompts, each of which plays as a
domain expert, to capture domain-specific knowledge; and a shared prompt for
general knowledge over the entire dataset. To facilitate knowledge sharing and
the interaction of different prompts, we introduce a domain prompt generator
that enables low-rank multiplicative updates between domain prompts and the
shared prompt. A domain mixup strategy is additionally devised to reduce the
co-occurring artifacts in each domain, which allows for more flexible decision
margins and mitigates the issue of incorrectly assigned domain labels.
Experiments on four out-of-distribution datasets and six different biased ISIC
datasets demonstrate the superior generalization ability of EPVT in skin lesion
recognition across various environments. Our code and dataset will be released
at https://github.com/SiyuanYan1/EPVT.Comment: 12 pages, 5 figure
Tensor Regression
Regression analysis is a key area of interest in the field of data analysis
and machine learning which is devoted to exploring the dependencies between
variables, often using vectors. The emergence of high dimensional data in
technologies such as neuroimaging, computer vision, climatology and social
networks, has brought challenges to traditional data representation methods.
Tensors, as high dimensional extensions of vectors, are considered as natural
representations of high dimensional data. In this book, the authors provide a
systematic study and analysis of tensor-based regression models and their
applications in recent years. It groups and illustrates the existing
tensor-based regression methods and covers the basics, core ideas, and
theoretical characteristics of most tensor-based regression methods. In
addition, readers can learn how to use existing tensor-based regression methods
to solve specific regression tasks with multiway data, what datasets can be
selected, and what software packages are available to start related work as
soon as possible. Tensor Regression is the first thorough overview of the
fundamentals, motivations, popular algorithms, strategies for efficient
implementation, related applications, available datasets, and software
resources for tensor-based regression analysis. It is essential reading for all
students, researchers and practitioners of working on high dimensional data.Comment: 187 pages, 32 figures, 10 table
Machine Learning for Multiclass Classification and Prediction of Alzheimer\u27s Disease
Alzheimer\u27s disease (AD) is an irreversible neurodegenerative disorder and a common form of dementia. This research aims to develop machine learning algorithms that diagnose and predict the progression of AD from multimodal heterogonous biomarkers with a focus placed on the early diagnosis. To meet this goal, several machine learning-based methods with their unique characteristics for feature extraction and automated classification, prediction, and visualization have been developed to discern subtle progression trends and predict the trajectory of disease progression.
The methodology envisioned aims to enhance both the multiclass classification accuracy and prediction outcomes by effectively modeling the interplay between the multimodal biomarkers, handle the missing data challenge, and adequately extract all the relevant features that will be fed into the machine learning framework, all in order to understand the subtle changes that happen in the different stages of the disease. This research will also investigate the notion of multitasking to discover how the two processes of multiclass classification and prediction relate to one another in terms of the features they share and whether they could learn from one another for optimizing multiclass classification and prediction accuracy.
This research work also delves into predicting cognitive scores of specific tests over time, using multimodal longitudinal data. The intent is to augment our prospects for analyzing the interplay between the different multimodal features used in the input space to the predicted cognitive scores. Moreover, the power of modality fusion, kernelization, and tensorization have also been investigated to efficiently extract important features hidden in the lower-dimensional feature space without being distracted by those deemed as irrelevant.
With the adage that a picture is worth a thousand words, this dissertation introduces a unique color-coded visualization system with a fully integrated machine learning model for the enhanced diagnosis and prognosis of Alzheimer\u27s disease. The incentive here is to show that through visualization, the challenges imposed by both the variability and interrelatedness of the multimodal features could be overcome. Ultimately, this form of visualization via machine learning informs on the challenges faced with multiclass classification and adds insight into the decision-making process for a diagnosis and prognosis
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