Method converts emotions in nonparallel speech data.
problem Lack of parallel data for speech emotion conversion.
method Unsupervised style transfer technique for nonparallel training.
result Effectiveness demonstrated on nonparallel corpora with four emotions.
Although voice conversion (VC) algorithms have achieved remarkable success along with the development of machine learning, superior performance is still difficult to achieve when using nonparallel data. In this paper, we propose using a cycle-consistent adversarial network (CycleGAN) for nonparallel data-based VC train…
A-StarGAN improves nonparallel voice conversion speed and realism.
problem Nonparallel voice conversion without parallel data.
method Augmented classifier StarGAN (A-StarGAN) for nonparallel voice conversion.
result A-StarGAN generates realistic-sounding speech quickly and efficiently.
Two SVM frameworks are introduced for classification problems.
problem Classifying data with nonparallel hyperplanes.
method Two SVM frameworks: separate and simultaneous construction of hyperplanes.
result Max-min distance-based NSVM for multiclass classification.
Totally nonparallel immersions require at least 4n-1 dimensions for n-manifolds.
problem Finding the minimum dimension for totally nonparallel immersions of manifolds.
method Analogous to vector field problems and symmetric bilinear maps, studying the space of nonsingular symmetric bilinear maps and applying the removal-of-singularities h-principle.
result Every n-manifold admits a totally nonparallel immersion into R^(4n-1), one dimension less than previously known.
TSVQR captures heterogeneous and asymmetric data using quantile regression.
problem Capturing heterogeneous and asymmetric information in modern data.
method Twin Support Vector Quantile Regression (TSVQR) with two nonparallel planes for quantile levels.
result TSVQR outperforms previous methods in capturing and learning from data.
New NHCAs improve multi-category classification efficiency.
problem Efficient multi-category classification for real-world problems.
method Twin SVM (TWSVM), Generalized eigenvalue proximal SVM (GEPSVM), Regularized GEPSVM (RegGEPSVM), and Improved GEPSVM (IGEPSVM) with OAA, BT, and TDS approaches.
result TDS-TWSVM outperforms other methods in classification accuracy.
Embeddings of pairs of disjoint nonparallel primitive simple closed curves in the boundary of a genus two handlebody are classified. Briefly, two disjoint primitives either lie on opposite ends of a product F×I, or they lie on opposite ends of a kind of "twisted" product $F \widetilde{\boldsymbol{…
The complete local classification and geometric description of n-dimensional submanifolds F with recurrent nonparallel second fundamental form in the spaces of constant curvature M(c) are obtained in this article.
Develops NPSVC++ to improve NPSVC performance through representation learning.
problem Feature suboptimality and class dependency in NPSVC training.
method Multi-objective optimization and end-to-end learning of NPSVC and its features.
result NPSVC++ ensures feature optimality across classes, overcoming training issues.
It is shown that for given positive integers g and b, there is a number C(g,b), such that any orientable compact irreducible 3-manifold of Heegaard genus g has at most C(g,b) disjoint, nonparallel incompressible surfaces with first Betti number b_1 < b.
In this paper, we analyze the geometric structure of an Euclidean submanifold whose osculating spaces form a nonconstant family of proper subspaces of the same dimension. We prove that if the rate of change of the osculating spaces is small, then the submanifold must be a (submanifold of a) ruled submanifold of a very …
In this paper we describe the notion of an annular end of a Riemann surface being of finite type with respect to some harmonic function and prove some theoretical results relating the conformal structure of such an annular end to the level sets of the harmonic function. We then apply these results to understand and cha…
By adopting the polynomial interpolation method, we propose an approach to hedge against the interest-rate risk of the default-free bonds by measuring the nonparallel movement of the yield-curve, such as the translation, the rotation and the twist. The empirical analysis shows that our hedging strategies are comparable…
We develop a parallel variational inference (VI) procedure for use in data-distributed settings, where each machine only has access to a subset of data and runs VI independently, without communicating with other machines. This type of "embarrassingly parallel" procedure has recently been developed for MCMC inference al…
Efficiently trains large-scale ordinal regression models using DCD.
problem Efficiently training large-scale ordinal regression models.
method Dual coordinate descent method (DCD) for training and a new prediction function.
result Extensive experiments show the DCD method is suitable for large-scale data.
We present the Voice Conversion Challenge 2018, designed as a follow up to the 2016 edition with the aim of providing a common framework for evaluating and comparing different state-of-the-art voice conversion (VC) systems. The objective of the challenge was to perform speaker conversion (i.e. transform the vocal ident…
In this paper we examine different aspects of the geometry of closed conformal vector fields on Riemannian manifolds. We begin by getting obstructions to the existence of closed conformal and nonparallel vector fields on complete manifolds with nonpositive Ricci curvature, thus generalizing a theorem of T. K. Pan. Then…
The study finds counterexamples to a conjecture about incompressible planar surfaces in hyperbolic link exteriors.
problem Finding counterexamples to a conjecture about incompressible planar surfaces in hyperbolic link exteriors.
method Constructing examples of hyperbolic links and analyzing their exteriors to find incompressible spanning planar surfaces.
result Examples of 3-component hyperbolic links with exterior containing incompressible spanning planar surfaces with nonmeridional and nonintegral boundary slopes.
Data preprocessing improves data quality for robust data mining.
problem Noisy and incomplete data hinders data mining models.
method Overview of data cleaning, transformation, and preprocessing methods.
result Preprocessing significantly affects data mining model performance.
Big data sets must be carefully partitioned into statistically similar data subsets that can be used as representative samples for big data analysis tasks. In this paper, we propose the random sample partition (RSP) data model to represent a big data set as a set of non-overlapping data subsets, called RSP data blocks,…
A new method for handling imbalanced big data using ensembles and smart data.
problem Imbalanced data distribution in big data scenarios.
method Smart Data driven Decision Trees Ensemble (SD_DeTE) methodology.
result SD_DeTE outperforms Random Forest in handling imbalanced binary classification problems in big data.
Prevents sensitive data generation in diffusion models using labeled and unlabeled data.
problem Generating sensitive data in diffusion models using unlabeled data.
method Positive-Unlabeled Diffusion Models, approximating ELBO with labeled and unlabeled data.
result Prevents the generation of sensitive data without compromising image quality.
Study reveals Data Shapley's inconsistent performance in data selection tasks.
problem Inconsistency of Data Shapley's performance in data selection across different settings.
method Hypothesis testing framework and identification of utility functions.
result Data Shapley's performance is no better than random selection without specific constraints.
Survey on data collection challenges in machine learning.
problem Data scarcity and need for labeled data in machine learning.
method Comprehensive study of data acquisition, labeling, and improvement techniques.
result Identification of research challenges in data collection.
PRRO generates synthetic tabular data that improves SL performance and class distribution.
problem Low SL utility of synthetic data due to class imbalance and overlooked data relationships.
method Data pruning and column reordering to optimize SL utility.
result Synthetic data generated with PRRO enhances predictive performance and class distribution.
Defines data science as a natural ecosystem with challenges and missions.
problem Challenges and missions in data science due to 5D complexities and data life cycle phases.
method Systemic and data-centric view of data science as a fusion of data universe and its challenges, formalizing a general-purpose architecture.
result Essential data science as a natural ecosystem integrating specific disciplines and high-impact applications.
Synthetic data enhances analytics but requires careful volume management.
problem Accuracy of statistical methods on synthetic data vs. raw data.
method Synthetic Data Generation for Analytics framework using tabular diffusion models.
result Error rate decreases with more synthetic data but may stabilize or increase.
DPASF stream preprocesses Big Data streams efficiently.
problem Efficient preprocessing of streaming Big Data.
method Implemented six preprocessing algorithms in Apache Flink.
result Preprocessing improves data accuracy in streaming Big Data.
This paper introduces C-DSL to improve data mining outcomes by considering context.
problem Data collection ambiguities, data imbalance, hidden biases, lack of domain info, and data incompleteness.
method Developed Context-Driven Data Science Lifecycle (C-DSL) to address data quality issues.
result Tangible improvements to data mining outcomes were achieved through C-DSL.
Proposes using probabilistic models for privacy-preserving synthetic data.
problem Designing high-quality synthetic data for privacy preservation.
method Formulate the problem through probabilistic modelling, choosing a model for the data.
result Statistical discoveries can be reliably reproduced from synthetic data.
Unlabeled data helps stop active learning better than labeled data.
problem Reducing the need for manual annotation in text classification.
method Compared stopping methods based on labeled, unlabeled, and training data.
result Stopping methods using unlabeled data are more effective.
New test ensures quality of shared data in machine learning.
problem Ensuring quality of external data in machine learning tasks.
method Distribution-free two-sample testing procedures grounded in conformal outlier detection.
result Identifies valuable external data agents for model personalization.
Paper creates fair synthetic data ensuring equal predictions across sensitive attributes.
problem Ensuring fair predictions across sensitive attributes in synthetic data.
method Equalizing target probability distributions across sensitive attributes in synthetic data generation.
result Synthetic data provides strong fair predictions, equal across all thresholds.
A new method classifies multiple correlated data streams simultaneously.
problem Classifying multiple correlated data streams in practical scenarios.
method Double-Coupling Support Vector Machines (DC-SVM) considers both internal and external correlations.
result The proposed method outperforms traditional methods on artificial and real-world data streams.
DPA preserves data distribution in reduced dimensions.
problem Loss of data distribution in dimension reduction.
method DPA combines encoder and decoder to match data distribution.
result DPA successfully reconstructs data distribution.
Efficient synthetic data generation improves model performance on tabular data.
problem Improving model robustness and performance with scarce or low-quality data.
method Hardness characterization to identify high-value training points, generating synthetic data only from these points.
result Synthetic data generated from hardest points outperforms non-targeted methods on tabular datasets.
For most problems in science and engineering we can obtain data sets that describe the observed system from various perspectives and record the behavior of its individual components. Heterogeneous data sets can be collectively mined by data fusion. Fusion can focus on a specific target relation and exploit directly ass…
DAERNN models censored data using neural networks with data augmentation.
problem Handling censored data in expectile regression.
method Data augmentation based Expectile Regression Neural Networks (ERNNs).
result DAERNN outperforms existing censored ERNNs methods and achieves comparable predictive performance to fully observed data.
This paper quantifies uncertainty in Data Shapley using statistical inference.
problem Uncertainty in data valuation due to dynamic data distribution.
method Established relationship with U-statistics and quantified uncertainty using statistical inference.
result Confidence intervals for Data Shapley estimations are provided.
Generative Adversarial Networks create time series data from images.
problem Generating realistic time series data from images.
method Wasserstein GANs with gradient penalty for stability, synthesizing sinusoidal, PPG, and ECG data.
result Successfully generated time series data using image-based GANs.
DCoM uses deep neural networks to detect semantic data types from raw column values.
problem Detecting semantic data types from dirty and unseen data.
method DCoM employs multi-input NLP-based deep neural networks trained on 686,765 data columns.
result DCoM outperforms existing methods significantly on 78 different semantic data types.
Causal inference from observational data is the goal of many data analyses in the health and social sciences. However, academic statistics has often frowned upon data analyses with a causal objective. The introduction of the term "data science" provides a historic opportunity to redefine data analysis in such a way tha…
GANs generate training data for machine learning tasks.
problem Imbalanced data sets and sensitive information.
method Generative Adversarial Networks (GANs) to create artificial training data.
result A Decision Tree classifier trained on GAN-generated data achieved similar or better accuracy and recall than on original data.
Task-agnostic data valuation without validation requirements.
problem Valuing data without specific task assumptions.
method Estimating data diversity and relevance through queries without raw data.
result Estimates capture the diversity and relevance of seller's data for the buyer.
Framework generates private synthetic data for unlabeled mixed-type data.
problem Generating private synthetic data for unlabeled mixed-type data.
method Combines autoencoders and GANs for differential privacy.
result Learned model generates synthetic data with similar statistical properties.
New algorithm improves data imputation for complex multimodal data sets.
problem Artifacts in imputation methods for multimodal distributions.
method Combines kNN and KDE for probabilistic estimates. result Lower imputation errors and higher likelihood estimates.
New algorithms for clustering and synthetic data generation of heterogeneous tabular datasets.
problem Clustering and generating synthetic data from heterogeneous tabular datasets with hidden cluster structure.
method Developed MMM and MMMsynth algorithms for clustering and synthetic data generation.
result MMMsynth algorithm outperforms other literature tabular-data generators and approaches real data performance.