Search Results for author:
Found 38 papers, 3 papers with code
Alibaba’s Submission for the WMT 2020 APE Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual BERT
We also apply an imitation learning strategy to augment a reasonable amount of pseudo APE training data, potentially preventing the model to overfit on the limited real training data and boosting the performance on held-out data.
Alibaba Submission to the WMT20 Parallel Corpus Filtering Task
In the filtering task, three main methods are applied to evaluate the quality of the parallel corpus, i. e. a) Dual Bilingual GPT-2 model, b) Dual Conditional Cross-Entropy Model and c) IBM word alignment model.
DMSA: Dynamic Multi-scale Unsupervised Semantic Segmentation Based on Adaptive Affinity
The proposed method in this paper proposes an end-to-end unsupervised semantic segmentation architecture DMSA based on four loss functions.
Bayesian Matrix Decomposition and Applications
The sole aim of this book is to give a self-contained introduction to concepts and mathematical tools in Bayesian matrix decomposition in order to seamlessly introduce matrix decomposition techniques and their applications in subsequent sections.
Feature Selection via the Intervened Interpolative Decomposition and its Application in Diversifying Quantitative Strategies
In this paper, we propose a probabilistic model for computing an interpolative decomposition (ID) in which each column of the observed matrix has its own priority or importance, so that the end result of the decomposition finds a set of features that are representative of the entire set of features, and the selected features also have higher priority than others.
Constraining Pseudo-label in Self-training Unsupervised Domain Adaptation with Energy-based Model
Deep learning is usually data starved, and the unsupervised domain adaptation (UDA) is developed to introduce the knowledge in the labeled source domain to the unlabeled target domain.
Robust Bayesian Nonnegative Matrix Factorization with Implicit Regularizers
We introduce a probabilistic model with implicit norm regularization for learning nonnegative matrix factorization (NMF) that is commonly used for predicting missing values and finding hidden patterns in the data, in which the matrix factors are latent variables associated with each data dimension.
Subtype-Aware Dynamic Unsupervised Domain Adaptation
In TPN, while the closeness of class centers between source and target domains is explicitly enforced in a latent space, the underlying fine-grained subtype structure and the cross-domain within-class compactness have not been fully investigated.
A Hybrid Approach on Conditional GAN for Portfolio Analysis
The limitation of the CGAN or ACGAN framework stands in putting too much emphasis on generating series and finding the internal trends of the series rather than predicting the future trends.
A note on VIX for postprocessing quantitative strategies
In this note, we introduce how to use Volatility Index (VIX) for postprocessing quantitative strategies so as to increase the Sharpe ratio and reduce trading risks.
Comparative Study of Inference Methods for Interpolative Decomposition
In this paper, we propose a probabilistic model with automatic relevance determination (ARD) for learning interpolative decomposition (ID), which is commonly used for low-rank approximation, feature selection, and identifying hidden patterns in data, where the matrix factors are latent variables associated with each data dimension.
Autoencoding Conditional GAN for Portfolio Allocation Diversification
Over the decades, the Markowitz framework has been used extensively in portfolio analysis though it puts too much emphasis on the analysis of the market uncertainty rather than on the trend prediction.
Bayesian Low-Rank Interpolative Decomposition for Complex Datasets
In this paper, we introduce a probabilistic model for learning interpolative decomposition (ID), which is commonly used for feature selection, low-rank approximation, and identifying hidden patterns in data, where the matrix factors are latent variables associated with each data dimension.
Flexible and Hierarchical Prior for Bayesian Nonnegative Matrix Factorization
In this paper, we introduce a probabilistic model for learning nonnegative matrix factorization (NMF) that is commonly used for predicting missing values and finding hidden patterns in the data, in which the matrix factors are latent variables associated with each data dimension.
Gradient Descent, Stochastic Optimization, and Other Tales
In deep neural networks, the gradient followed by a single sample or a batch of samples is employed to save computational resources and escape from saddle points.
AdaSmooth: An Adaptive Learning Rate Method based on Effective Ratio
It is well known that we need to choose the hyper-parameters in Momentum, AdaGrad, AdaDelta, and other alternative stochastic optimizers.
Reducing overestimating and underestimating volatility via the augmented blending-ARCH model
SVR-GARCH model tends to "backward eavesdrop" when forecasting the financial time series volatility in which case it tends to simply produce the prediction by deviating the previous volatility.
Exploring Classic Quantitative Strategies
It aims to build a solid foundation on how and why the techniques work.
Matrix Decomposition and Applications
In 1954, Alston S. Householder published Principles of Numerical Analysis, one of the first modern treatments on matrix decomposition that favored a (block) LU decomposition-the factorization of a matrix into the product of lower and upper triangular matrices.
Dual camera snapshot hyperspectral imaging system via physics informed learning
Various methods using CNNs have been developed in recent years to reconstruct HSIs, but most of the supervised deep learning methods aimed to fit a brute-force mapping relationship between the captured compressed image and standard HSIs.
A survey on Bayesian inference for Gaussian mixture model
The sole aim of this survey is to give a self-contained introduction to concepts and mathematical tools in Bayesian inference for finite and infinite Gaussian mixture model in order to seamlessly introduce their applications in subsequent sections.
Revisit the Fundamental Theorem of Linear Algebra
This survey is meant to provide an introduction to the fundamental theorem of linear algebra and the theories behind them.
Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation
The UDA for ordinal classification requires inducing non-trivial ordinal distribution prior to the latent space.
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference
In this work, we propose a domain generalization (DG) approach to learn on several labeled source domains and transfer knowledge to a target domain that is inaccessible in training.
A rigorous introduction to linear models
We then describe linear models from different perspectives and find the properties and theories behind the models.
Imaging vibrations of locally gated, electromechanical few layer graphene resonators with a moving vacuum enclosure
With it we characterize the lowest frequency mode of a FLG resonator by measuring its frequency response as a function of position on the membrane.
Mesoscale and Nanoscale Physics
Identity-aware Facial Expression Recognition in Compressed Video
In the up to two orders of magnitude compressed domain, we can explicitly infer the expression from the residual frames and possible to extract identity factors from the I frame with a pre-trained face recognition network.
Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis
Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids.
Energy-constrained Self-training for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to transfer the knowledge on a labeled source domain distribution to perform well on an unlabeled target domain.
Alibaba's Neural Machine Translation Systems for WMT18
We participated in 5 translation directions including English ↔ Russian, English ↔ Turkish in both directions and English → Chinese.
Alibaba Submission to the WMT18 Parallel Corpus Filtering Task
This paper describes the Alibaba Machine Translation Group submissions to the WMT 2018 Shared Task on Parallel Corpus Filtering.
CompNet: Neural networks growing via the compact network morphism
We explored $CompNet$, in which case we morph a well-trained neural network to a deeper one where network function can be preserved and the added layer is compact.
Reducing over-clustering via the powered Chinese restaurant process
Dirichlet process mixture (DPM) models tend to produce many small clusters regardless of whether they are needed to accurately characterize the data - this is particularly true for large data sets.
An Equivalence of Fully Connected Layer and Convolutional Layer
The article is helpful for the beginners of the neural network to understand how fully connected layer and the convolutional layer work in the backend.
Hyperprior on symmetric Dirichlet distribution
In this article we introduce how to put vague hyperprior on Dirichlet distribution, and we update the parameter of it by adaptive rejection sampling (ARS).
Machine learning modeling for time series problem: Predicting flight ticket prices
Based on the data over a 103 day period, we trained our models, getting the best model - which is AdaBoost-Decision Tree Classification.
