
Hierarchical Autoregressive Modeling for Neural Video Compression
Recent work by Marino et al. (2020) showed improved performance in seque...
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ISTANAS: Efficient and Consistent Neural Architecture Search by Sparse Coding
Neural architecture search (NAS) aims to produce the optimal sparse solu...
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Improving Inference for Neural Image Compression
We consider the problem of lossy image compression with deep latent vari...
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Bayesian differential programming for robust systems identification under uncertainty
This paper presents a machine learning framework for Bayesian systems id...
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Spatial Pyramid Based Graph Reasoning for Semantic Segmentation
The convolution operation suffers from a limited receptive filed, while ...
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Exact artificial boundary conditions of 1D semidiscretized peridynamics
The peridynamic theory reformulates the equations of continuum mechanics...
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VariableBitrate Neural Compression via Bayesian Arithmetic Coding
Deep Bayesian latent variable models have enabled new approaches to both...
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Lifted Hybrid Variational Inference
A variety of lifted inference algorithms, which exploit model symmetry t...
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Dynamical System Inspired Adaptive Time Stepping Controller for Residual Network Families
The correspondence between residual networks and dynamical systems motiv...
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SOGNet: Scene Overlap Graph Network for Panoptic Segmentation
The panoptic segmentation task requires a unified result from semantic a...
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ExpectationMaximization Attention Networks for Semantic Segmentation
Selfattention mechanism has been widely used for various tasks. It is d...
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Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from noninvasive clinical measurements using physicsinformed deep learning
Advances in computational science offer a principled pipeline for predic...
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Conditional deep surrogate models for stochastic, highdimensional, and multifidelity systems
We present a probabilistic deep learning methodology that enables the co...
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Physicsinformed deep generative models
We consider the application of deep generative models in propagating unc...
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Adversarial Uncertainty Quantification in PhysicsInformed Neural Networks
We present a deep learning framework for quantifying and propagating unc...
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Optimization Algorithm Inspired Deep Neural Network Structure Design
Deep neural networks have been one of the dominant machine learning appr...
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Joint Subbands Learning with Clique Structures for Wavelet Domain SuperResolution
Convolutional neural networks (CNNs) have recently achieved great succes...
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Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization
We propose a simple and easy to implement neural network compression alg...
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Convolutional Neural Networks with Alternately Updated Clique
Improving information flow in deep networks helps to ease the training d...
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Yibo Yang
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