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Found 14 papers, 5 papers with code
Predicting and Explaining Mobile UI Tappability with Vision Modeling and Saliency Analysis
We use a deep learning based approach to predict whether a selected element in a mobile UI screenshot will be perceived by users as tappable, based on pixels only instead of view hierarchies required by previous work.
Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning
Mobile User Interface Summarization generates succinct language descriptions of mobile screens for conveying important contents and functionalities of the screen, which can be useful for many language-based application scenarios.
Dataset Meta-Learning from Kernel-Ridge Regression
One of the most fundamental aspects of any machine learning algorithm is the training data used by the algorithm.
Dataset Meta-Learning from Kernel Ridge-Regression
One of the most fundamental aspects of any machine learning algorithm is the training data used by the algorithm.
You Look Twice: GaterNet for Dynamic Filter Selection in CNNs
The concept of conditional computation for deep nets has been proposed previously to improve model performance by selectively using only parts of the model conditioned on the sample it is processing.
Learning Sparse Deep Feedforward Networks via Tree Skeleton Expansion
An important characteristic of FNN structures learned this way is that they are sparse.
Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering
We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features.
Building Sparse Deep Feedforward Networks using Tree Receptive Fields
We use Chow-Liu's algorithm to learn a tree-structured probabilistic model for the units at the current level, use the tree to identify subsets of units that are strongly correlated, and introduce a new unit with receptive field over the subsets.
Learning Parsimonious Deep Feed-forward Networks
Convolutional neural networks and recurrent neural networks are designed with network structures well suited to the nature of spacial and sequential data respectively.
A Novel Document Generation Process for Topic Detection based on Hierarchical Latent Tree Models
It is the new state-of-the-art for the hierarchical topic detection.
Sparse Boltzmann Machines with Structure Learning as Applied to Text Analysis
We are interested in exploring the possibility and benefits of structure learning for deep models.
Latent Tree Models for Hierarchical Topic Detection
The variables at other levels are binary latent variables, with those at the lowest latent level representing word co-occurrence patterns and those at higher levels representing co-occurrence of patterns at the level below.
Progressive EM for Latent Tree Models and Hierarchical Topic Detection
It is as efficient as the state-of-the-art LDA-based method for hierarchical topic detection and finds substantially better topics and topic hierarchies.
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time.
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