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05/31/2023
Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning
For robotic vehicles to navigate robustly and safely in unseen environme...
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04/23/2023
The Disharmony Between BN and ReLU Causes Gradient Explosion, but is Offset by the Correlation Between Activations
Deep neural networks based on batch normalization and ReLU-like activati...
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02/22/2023
Stress and Adaptation: Applying Anna Karenina Principle in Deep Learning for Image Classification
Image classification with deep neural networks has reached state-of-art ...
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09/01/2022
Why Do Neural Language Models Still Need Commonsense Knowledge to Handle Semantic Variations in Question Answering?
Many contextualized word representations are now learned by intricate ne...
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06/17/2022
Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images
Evaluation metrics in image synthesis play a key role to measure perform...
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02/17/2022
Variational Neural Temporal Point Process
A temporal point process is a stochastic process that predicts which typ...
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01/17/2022
Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks?
Even though image generation with Generative Adversarial Networks has be...
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12/16/2021
An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks
Despite significant improvements on the image generation performance of ...
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04/13/2021
Automatic Correction of Internal Units in Generative Neural Networks
Generative Adversarial Networks (GANs) have shown satisfactory performan...
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12/21/2020
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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12/07/2020
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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10/19/2020
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems
Recent advances in Deep Gaussian Processes (DGPs) show the potential to ...
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10/16/2020
Improved Predictive Deep Temporal Neural Networks with Trend Filtering
Forecasting with multivariate time series, which aims to predict future ...
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05/28/2020
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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04/27/2020
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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12/12/2019
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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11/16/2019
IDEALEM: Statistical Similarity Based Data Reduction
Many applications such as scientific simulation, sensing, and power grid...
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11/08/2019
Why Do Masked Neural Language Models Still Need Common Sense Knowledge?
Currently, contextualized word representations are learned by intricate ...
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09/16/2019
A Single Multi-Task Deep Neural Network with Post-Processing for Object Detection with Reasoning and Robotic Grasp Detection
Recently, robotic grasp detection (GD) and object detection (OD) with re...
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05/30/2019
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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04/01/2019
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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05/24/2018
Hawkes Process Kernel Structure Parametric Search with Renormalization Factors
Hawkes Processes are a type of point process for modeling self-excitatio...
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03/28/2017
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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07/04/2016
Automatic Generation of Probabilistic Programming from Time Series Data
Probabilistic programming languages represent complex data with intermin...
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02/12/2016
Global Deconvolutional Networks for Semantic Segmentation
Semantic image segmentation is a principal problem in computer vision, w...
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11/26/2015
The Automatic Statistician: A Relational Perspective
Gaussian Processes (GPs) provide a general and analytically tractable wa...
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10/16/2012
Lifted Relational Variational Inference
Hybrid continuous-discrete models naturally represent many real-world ap...
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03/15/2012