480,071 research outputs found
Dynamic Pooling for the Combination of Forecasts Generated Using Multi Level Learning
In this paper we provide experimental results and
extensions to our previous theoretical findings concerning the
combination of forecasts that have been diversified by three
different methods: with parameters learned at different data
aggregation levels, by thick modeling and by the use of different
forecasting methods. An approach of error variance based
pooling as proposed by Aiolfi and Timmermann has been compared
with flat combinations as well as an alternative pooling
approach in which we consider information about the used
diversification. An advantage of our approach is that it leads to
the generation of novel multi step multi level forecast generation
structures that carry out the combination in different steps of
pooling corresponding to the different types of diversification.
We describe different evolutionary approaches in order to
evolve the order of pooling of the diversification dimensions.
Extensions of such evolutions allow the generation of more
flexible multi level multi step combination structures containing
better adaptive capabilities. We could prove a significant error
reduction comparing results of our generated combination
structures with results generated with the algorithm of Aiolfi
and Timmermann as well as with flat combination for the
application of Revenue Management seasonal forecasting
An illustration of local structural equation modeling for longitudinal data:Examining differences in competence development in secondary schools
In this chapter, we discuss how a combination of longitudinal modeling and local structural equation modeling (LSEM) can be used to study how students’ context influence their growth in educational achievement. LSEM is a nonparametric approach that allows for the moderation of a structural equation model over a continuous variable (e.g., socio-economic status; cultural identity; age). Thus, it does not require the categorization of continuous moderators as applied in multi-group approaches. In contrast to regression-based approaches, it does not impose a particular functional form (e.g., linear) on the mean-level differences and can spot differences in the variance-covariance structure. LSEM can be used to detect nonlinear moderation effects, to examine sources of measurement invariance violations, and to study moderation effects on all parameters in the model. We showcase how LSEM can be implemented with longitudinal of the National Educational Panel Study (NEPS) using the R-package sirt. In more detail, we examine the effect of parental education on math and reading competence in secondary school across three measurement occasions, comparing LSEM to regression based approaches and multi-group confirmatory factor analysis. Results provide further evidence of the strong influence of the educational background of the family. This chapter offers a new approach to study inter-individual differences in educational development.</p
PM-MMUT: Boosted Phone-Mask Data Augmentation using Multi-Modeling Unit Training for Phonetic-Reduction-Robust E2E Speech Recognition
Consonant and vowel reduction are often encountered in speech, which might
cause performance degradation in automatic speech recognition (ASR). Our
recently proposed learning strategy based on masking, Phone Masking Training
(PMT), alleviates the impact of such phenomenon in Uyghur ASR. Although PMT
achieves remarkably improvements, there still exists room for further gains due
to the granularity mismatch between the masking unit of PMT (phoneme) and the
modeling unit (word-piece). To boost the performance of PMT, we propose
multi-modeling unit training (MMUT) architecture fusion with PMT (PM-MMUT). The
idea of MMUT framework is to split the Encoder into two parts including
acoustic feature sequences to phoneme-level representation (AF-to-PLR) and
phoneme-level representation to word-piece-level representation (PLR-to-WPLR).
It allows AF-to-PLR to be optimized by an intermediate phoneme-based CTC loss
to learn the rich phoneme-level context information brought by PMT.
Experimental results on Uyghur ASR show that the proposed approaches outperform
obviously the pure PMT. We also conduct experiments on the 960-hour Librispeech
benchmark using ESPnet1, which achieves about 10% relative WER reduction on all
the test set without LM fusion comparing with the latest official ESPnet1
pre-trained model.Comment: Accepted to INTERSPEECH 202
Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search
Despite substantial interest in applications of neural networks to
information retrieval, neural ranking models have only been applied to standard
ad hoc retrieval tasks over web pages and newswire documents. This paper
proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network)
a novel neural ranking model specifically designed for ranking short social
media posts. We identify document length, informal language, and heterogeneous
relevance signals as features that distinguish documents in our domain, and
present a model specifically designed with these characteristics in mind. Our
model uses hierarchical convolutional layers to learn latent semantic
soft-match relevance signals at the character, word, and phrase levels. A
pooling-based similarity measurement layer integrates evidence from multiple
types of matches between the query, the social media post, as well as URLs
contained in the post. Extensive experiments using Twitter data from the TREC
Microblog Tracks 2011--2014 show that our model significantly outperforms prior
feature-based as well and existing neural ranking models. To our best
knowledge, this paper presents the first substantial work tackling search over
social media posts using neural ranking models.Comment: AAAI 2019, 10 page
Learning and comparing functional connectomes across subjects
Functional connectomes capture brain interactions via synchronized
fluctuations in the functional magnetic resonance imaging signal. If measured
during rest, they map the intrinsic functional architecture of the brain. With
task-driven experiments they represent integration mechanisms between
specialized brain areas. Analyzing their variability across subjects and
conditions can reveal markers of brain pathologies and mechanisms underlying
cognition. Methods of estimating functional connectomes from the imaging signal
have undergone rapid developments and the literature is full of diverse
strategies for comparing them. This review aims to clarify links across
functional-connectivity methods as well as to expose different steps to perform
a group study of functional connectomes
- …