187 research outputs found
FloWaveNet : A Generative Flow for Raw Audio
Most modern text-to-speech architectures use a WaveNet vocoder for
synthesizing high-fidelity waveform audio, but there have been limitations,
such as high inference time, in its practical application due to its ancestral
sampling scheme. The recently suggested Parallel WaveNet and ClariNet have
achieved real-time audio synthesis capability by incorporating inverse
autoregressive flow for parallel sampling. However, these approaches require a
two-stage training pipeline with a well-trained teacher network and can only
produce natural sound by using probability distillation along with auxiliary
loss terms. We propose FloWaveNet, a flow-based generative model for raw audio
synthesis. FloWaveNet requires only a single-stage training procedure and a
single maximum likelihood loss, without any additional auxiliary terms, and it
is inherently parallel due to the characteristics of generative flow. The model
can efficiently sample raw audio in real-time, with clarity comparable to
previous two-stage parallel models. The code and samples for all models,
including our FloWaveNet, are publicly available.Comment: 9 pages, ICML'201
Languages and earnings management
We predict that managers of firms in countries where languages do not require speakers to grammatically mark future events perceive future consequences of earnings management to be more imminent, and therefore they are less likely to engage in earnings management. Using data from 38 countries, we find that accrual-based earnings management and real earnings management are less prevalent where there is weaker time disassociation in the language. Our study is the first to examine the relation between the grammatical structure of languages and financial reporting characteristics, and it extends the literature on the effect of informal institutions on corporate actions
Relationships between the Institutional Environment and Corporate Governance Practices: Implications for Emerging and Developed Countries
Changing corporate governance practices requires both formal adoption of best practices as well as changing the supporting institutional environment. We identify which elements of the institutional environment are most closely related to changes in corporate governance practices. We examine the influence of changes in institutional environments on changes in corporate governance practices by examining data from 37 countries. For emerging countries, we find that changes in rule of law are followed by changes in corporate governance practices. When changes in control of corruption are combined with changes in government effectiveness, significant changes in corporate governance practices are also realized. This differs from the pathway to improved corporate governance practices for developed nations. Developed nations require a combination of changes in rule of law and changes in regulatory quality
Recasting Continual Learning as Sequence Modeling
In this work, we aim to establish a strong connection between two significant
bodies of machine learning research: continual learning and sequence modeling.
That is, we propose to formulate continual learning as a sequence modeling
problem, allowing advanced sequence models to be utilized for continual
learning. Under this formulation, the continual learning process becomes the
forward pass of a sequence model. By adopting the meta-continual learning (MCL)
framework, we can train the sequence model at the meta-level, on multiple
continual learning episodes. As a specific example of our new formulation, we
demonstrate the application of Transformers and their efficient variants as MCL
methods. Our experiments on seven benchmarks, covering both classification and
regression, show that sequence models can be an attractive solution for general
MCL.Comment: NeurIPS 202
An Empirical Examination of Consumer Behavior for Search and Experience Goods in Sentiment Analysis
With the explosive increase of user-generated content such as product reviews and social media, sentiment analysis has emerged as an area of interest. Sentiment analysis is a useful method to analyze product reviews, and product feature extraction is an important task in sentiment analysis, during which one identifies features of products from reviews. Product features are categorized by product type, such as search goods or experience goods, and their characteristics are totally different. Thus, we examine whether the classification performance differs by product type. The findings show that the optimal threshold varies by product type, and simply decreasing the threshold to cover many features does not guarantee improvement of the classification performance
Progressive Deblurring of Diffusion Models for Coarse-to-Fine Image Synthesis
Recently, diffusion models have shown remarkable results in image synthesis
by gradually removing noise and amplifying signals. Although the simple
generative process surprisingly works well, is this the best way to generate
image data? For instance, despite the fact that human perception is more
sensitive to the low frequencies of an image, diffusion models themselves do
not consider any relative importance of each frequency component. Therefore, to
incorporate the inductive bias for image data, we propose a novel generative
process that synthesizes images in a coarse-to-fine manner. First, we
generalize the standard diffusion models by enabling diffusion in a rotated
coordinate system with different velocities for each component of the vector.
We further propose a blur diffusion as a special case, where each frequency
component of an image is diffused at different speeds. Specifically, the
proposed blur diffusion consists of a forward process that blurs an image and
adds noise gradually, after which a corresponding reverse process deblurs an
image and removes noise progressively. Experiments show that the proposed model
outperforms the previous method in FID on LSUN bedroom and church datasets.
Code is available at https://github.com/sangyun884/blur-diffusion
A Review System Based On Product Features In A Mobile Environment
With the rapid growth of the mobile commerce, firms have been trying to get their online channels optimized for the mobile devices. However, many contents on online shopping sites are still focused on a desktop PC environment. Especially, consumer reviews are difficult to browse and grasp via a mobile device. Usually, it is not helpful to simply reduce the size of fonts or photos to fit to mobile devices without a fundamental transformation of the review presentation. In this study, we suggest a feature-based summarization process of consumer reviews in mobile environment. Further, we illustrate an implementation of the process by applying opinion mining techniques to product reviews crawled from a major shopping site in Korean. Finally, a plan for a controlled laboratory experiment is proposed to validate the effectiveness of the suggested review framework in this study
Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations
Long-term time series forecasting (LTSF) is a challenging task that has been
investigated in various domains such as finance investment, health care,
traffic, and weather forecasting. In recent years, Linear-based LTSF models
showed better performance, pointing out the problem of Transformer-based
approaches causing temporal information loss. However, Linear-based approach
has also limitations that the model is too simple to comprehensively exploit
the characteristics of the dataset. To solve these limitations, we propose
LTSF-DNODE, which applies a model based on linear ordinary differential
equations (ODEs) and a time series decomposition method according to data
statistical characteristics. We show that LTSF-DNODE outperforms the baselines
on various real-world datasets. In addition, for each dataset, we explore the
impacts of regularization in the neural ordinary differential equation (NODE)
framework.Comment: Accepted at IEEE BigData 202
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