
Automatic Correction of Internal Units in Generative Neural Networks
Generative Adversarial Networks (GANs) have shown satisfactory performan...
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Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior
Choosing a proper set of kernel functions is an important problem in lea...
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Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations
The clear transparency of Deep Neural Networks (DNNs) is hampered by com...
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Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems
Recent advances in Deep Gaussian Processes (DGPs) show the potential to ...
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Improved Predictive Deep Temporal Neural Networks with Trend Filtering
Forecasting with multivariate time series, which aims to predict future ...
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HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism
Deep Neural Network (DNN) models have continuously been growing in size ...
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Interpretation of Deep Temporal Representations by Selective Visualization of Internally Activated Units
Recently deep neural networks demonstrate competitive performances in cl...
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An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks
Deep generative neural networks (DGNNs) have achieved realistic and high...
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IDEALEM: Statistical Similarity Based Data Reduction
Many applications such as scientific simulation, sensing, and power grid...
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Why Do Masked Neural Language Models Still Need Common Sense Knowledge?
Currently, contextualized word representations are learned by intricate ...
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A Single MultiTask Deep Neural Network with PostProcessing for Object Detection with Reasoning and Robotic Grasp Detection
Recently, robotic grasp detection (GD) and object detection (OD) with re...
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Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes
In the analysis of sequential data, the detection of abrupt changes is i...
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Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks
As Deep Neural Networks (DNNs) have demonstrated superhuman performance ...
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Hawkes Process Kernel Structure Parametric Search with Renormalization Factors
Hawkes Processes are a type of point process for modeling selfexcitatio...
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Discovering Explainable Latent Covariance Structure for Multiple Time Series
Analyzing time series data is important to predict future events and cha...
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Automatic Generation of Probabilistic Programming from Time Series Data
Probabilistic programming languages represent complex data with intermin...
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Global Deconvolutional Networks for Semantic Segmentation
Semantic image segmentation is a principal problem in computer vision, w...
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The Automatic Statistician: A Relational Perspective
Gaussian Processes (GPs) provide a general and analytically tractable wa...
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Lifted Relational Variational Inference
Hybrid continuousdiscrete models naturally represent many realworld ap...
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Lifted Inference for Relational Continuous Models
Relational Continuous Models (RCMs) represent joint probability densitie...
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Jaesik Choi
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