41 citations · 203 across the 38 of their papers we have counts for
6 papers · 1 filter
Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations
Woo-Jeoung Nam, Jaesik Choi, Seong-Whan Lee
The clear transparency of Deep Neural Networks (DNNs) is hampered by complex internal structures and nonlinear transformations along deep hierarchies. In this paper, we propose a n…
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior
Anh Tong, Toan Tran, Hung Bui +1
Choosing a proper set of kernel functions is an important problem in learning Gaussian Process (GP) models since each kernel structure has different model complexity and data fitne…
Improved Predictive Deep Temporal Neural Networks with Trend Filtering
Youngjin Park, Deokjun Eom, Byoungki Seo +1
Forecasting with multivariate time series, which aims to predict future values given previous and current several univariate time series data, has been studied for decades, with on…
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems
Anh Tong, Jaesik Choi
Recent advances in Deep Gaussian Processes (DGPs) show the potential to have more expressive representation than that of traditional Gaussian Processes (GPs). However, there exists…
HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism
Jay H. Park, Gyeongchan Yun, Chang M. Yi +5
Deep Neural Network (DNN) models have continuously been growing in size in order to improve the accuracy and quality of the models. Moreover, for training of large DNN models, the…
Interpretation of Deep Temporal Representations by Selective Visualization of Internally Activated Nodes
Sohee Cho, Ginkyeng Lee, Wonjoon Chang +1
Recently deep neural networks demonstrate competitive performances in classification and regression tasks for many temporal or sequential data. However, it is still hard to underst…