A brief review on multi-task learning
KH Thung, CY Wee - Multimedia Tools and Applications, 2018 - Springer
Abstract Multi-task learning (MTL), which optimizes multiple related learning tasks at the
same time, has been widely used in various applications, including natural language …
same time, has been widely used in various applications, including natural language …
[PDF][PDF] Unsupervised feature learning and deep learning: A review and new perspectives
The success of machine learning algorithms generally depends on data representation, and
we hypothesize that this is because different representations can entangle and hide more or …
we hypothesize that this is because different representations can entangle and hide more or …
A survey on multi-task learning
Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …
leverage useful information contained in multiple related tasks to help improve the …
Sparsity information and regularization in the horseshoe and other shrinkage priors
J Piironen, A Vehtari - 2017 - projecteuclid.org
The horseshoe prior has proven to be a noteworthy alternative for sparse Bayesian
estimation, but has previously suffered from two problems. First, there has been no …
estimation, but has previously suffered from two problems. First, there has been no …
Dtcdr: A framework for dual-target cross-domain recommendation
In order to address the data sparsity problem in recommender systems, in recent years,
Cross-Domain Recommendation (CDR) leverages the relatively richer information from a …
Cross-Domain Recommendation (CDR) leverages the relatively richer information from a …
Variational Bayes for high-dimensional linear regression with sparse priors
We study a mean-field spike and slab variational Bayes (VB) approximation to Bayesian
model selection priors in sparse high-dimensional linear regression. Under compatibility …
model selection priors in sparse high-dimensional linear regression. Under compatibility …
Deterministic and probabilistic wind power forecasting using a variational Bayesian-based adaptive robust multi-kernel regression model
Accurate wind power forecasting has great practical significance for the safe and
economical operation of power systems. In reality, wind power data are recorded at high …
economical operation of power systems. In reality, wind power data are recorded at high …
On the hyperprior choice for the global shrinkage parameter in the horseshoe prior
J Piironen, A Vehtari - Artificial intelligence and statistics, 2017 - proceedings.mlr.press
The horseshoe prior has proven to be a noteworthy alternative for sparse Bayesian
estimation, but as shown in this paper, the results can be sensitive to the prior choice for the …
estimation, but as shown in this paper, the results can be sensitive to the prior choice for the …
Episodic multi-task learning with heterogeneous neural processes
This paper focuses on the data-insufficiency problem in multi-task learning within an
episodic training setup. Specifically, we explore the potential of heterogeneous information …
episodic training setup. Specifically, we explore the potential of heterogeneous information …
On spike-and-slab priors for Bayesian equation discovery of nonlinear dynamical systems via sparse linear regression
This paper presents the use of spike-and-slab (SS) priors for discovering governing
differential equations of motion of nonlinear structural dynamic systems. The problem of …
differential equations of motion of nonlinear structural dynamic systems. The problem of …