395 citations · 969 across the 46 of their papers we have counts for
7 papers · 1 filter
Variable Grouping Based Bayesian Additive Regression Tree
Yuhao Su, Jie Ding
Using ensemble methods for regression has been a large success in obtaining high-accuracy prediction. Examples are Bagging, Random forest, Boosting, BART (Bayesian additive regress…
Deep Clustering of Compressed Variational Embeddings
Suya Wu, Enmao Diao, Jie Ding +1
Motivated by the ever-increasing demands for limited communication bandwidth and low-power consumption, we propose a new methodology, named joint Variational Autoencoders with Bern…
Perception-Distortion Trade-off with Restricted Boltzmann Machines
Chris Cannella, Jie Ding, Mohammadreza Soltani +1
In this work, we introduce a new procedure for applying Restricted Boltzmann Machines (RBMs) to missing data inference tasks, based on linearization of the effective energy functio…
Supervised Encoding for Discrete Representation Learning
Cat P. Le, Yi Zhou, Jie Ding +1
Classical supervised classification tasks search for a nonlinear mapping that maps each encoded feature directly to a probability mass over the labels. Such a learning framework ty…
Speech Emotion Recognition with Dual-Sequence LSTM Architecture
Jianyou Wang, Michael Xue, Ryan Culhane +3
Speech Emotion Recognition (SER) has emerged as a critical component of the next generation human-machine interfacing technologies. In this work, we propose a new dual-level model…
Restricted Recurrent Neural Networks
Enmao Diao, Jie Ding, Vahid Tarokh
Recurrent Neural Network (RNN) and its variations such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), have become standard building blocks for learning online dat…
DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression
Enmao Diao, Jie Ding, Vahid Tarokh
We propose a new architecture for distributed image compression from a group of distributed data sources. The work is motivated by practical needs of data-driven codec design, low…