Ying Nian Wu

According to our database1, Ying Nian Wu authored at least 132 papers between 1996 and 2021.

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Bibliography

2021
Learning Energy-Based Spatial-Temporal Generative ConvNets for Dynamic Patterns.
IEEE Trans. Pattern Anal. Mach. Intell., 2021

Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI.
CoRR, 2021

Planning on a (Risk) Budget: Safe Non-Conservative Planning in Probabilistic Dynamic Environments.
CoRR, 2021

SAS: Self-Augmented Strategy for Language Model Pre-training.
CoRR, 2021

Trajectory Prediction with Latent Belief Energy-Based Model.
CoRR, 2021

Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance.
CoRR, 2021

A HINT from Arithmetic: On Systematic Generalization of Perception, Syntax, and Semantics.
CoRR, 2021

HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem Solving.
CoRR, 2021

SCRIPT: Self-Critic PreTraining of Transformers.
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification.
Proceedings of the 38th International Conference on Machine Learning, 2021

Learning Energy-Based Models by Diffusion Recovery Likelihood.
Proceedings of the 9th International Conference on Learning Representations, 2021

Generative Text Modeling through Short Run Inference.
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021

Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Cooperative Training of Descriptor and Generator Networks.
IEEE Trans. Pattern Anal. Mach. Intell., 2020

Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis.
CoRR, 2020

Learning Energy-Based Models by Diffusion Recovery Likelihood.
CoRR, 2020

Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling.
CoRR, 2020

Learning Latent Space Energy-Based Prior Model for Molecule Generation.
CoRR, 2020

A Representational Model of Grid Cells Based on Matrix Lie Algebras.
CoRR, 2020

Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC.
CoRR, 2020

Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense.
CoRR, 2020

Generative PointNet: Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification.
CoRR, 2020

Learning Latent Space Energy-Based Prior Model.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Imposing implicit feasibility constraints on deformable image registration using a statistical generative model.
Proceedings of the Medical Imaging 2020: Image Processing, 2020

Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning.
Proceedings of the 37th International Conference on Machine Learning, 2020

Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference.
Proceedings of the Computer Vision - ECCV 2020, 2020

Inducing Hierarchical Compositional Model by Sparsifying Generator Network.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Flow Contrastive Estimation of Energy-Based Models.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Joint Training of Variational Auto-Encoder and Latent Energy-Based Model.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues.
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2020

Robust Transfer Learning with Pretrained Language Models through Adapters.
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2020

Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
A tale of two explanations: Enhancing human trust by explaining robot behavior.
Sci. Robotics, 2019

Replicating Neuroscience Observations on ML/MF and AM Face Patches by Deep Generative Model.
Neural Comput., 2019

Learning Deep Generative Models with Short Run Inference Dynamics.
CoRR, 2019

Representation Learning: A Statistical Perspective.
CoRR, 2019

Deep Unsupervised Clustering with Clustered Generator Model.
CoRR, 2019

Towards Interpretable Image Synthesis by Learning Sparsely Connected AND-OR Networks.
CoRR, 2019

Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge.
CoRR, 2019

On Learning Non-Convergent Short-Run MCMC Toward Energy-Based Model.
CoRR, 2019

Learning Trajectory Prediction with Continuous Inverse Optimal Control via Langevin Sampling of Energy-Based Models.
CoRR, 2019

Learning Vector Representation of Content and Matrix Representation of Change: Towards a Representational Model of V1.
CoRR, 2019

Multimodal Conditional Learning with Fast Thinking Policy-like Model and Slow Thinking Planner-like Model.
CoRR, 2019

Inducing Sparse Coding and And-Or Grammar from Generator Network.
CoRR, 2019

Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract).
CoRR, 2019

Network Transplanting (extended abstract).
CoRR, 2019

Interpretable CNNs.
CoRR, 2019

Learning Generator Networks for Dynamic Patterns.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2019

Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion.
Proceedings of the 7th International Conference on Learning Representations, 2019

Multi-Agent Tensor Fusion for Contextual Trajectory Prediction.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

Interpreting CNNs via Decision Trees.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

Unsupervised Disentangling of Appearance and Geometry by Deformable Generator Network.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

Learning Dynamic Generator Model by Alternating Back-Propagation through Time.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Mining deep And-Or object structures via cost-sensitive question-answer-based active annotations.
Comput. Vis. Image Underst., 2018

Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model.
CoRR, 2018

Explanatory Graphs for CNNs.
CoRR, 2018

Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering.
CoRR, 2018

Learning Grid-like Units with Vector Representation of Self-Position and Matrix Representation of Self-Motion.
CoRR, 2018

A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models.
CoRR, 2018

Interactive Agent Modeling by Learning to Probe.
CoRR, 2018

Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry.
CoRR, 2018

Unsupervised Learning of Neural Networks to Explain Neural Networks.
CoRR, 2018

Network Transplanting.
CoRR, 2018

Interpreting CNNs via Decision Trees.
CoRR, 2018

Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Replicating Active Appearance Model by Generator Network.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Learning Multi-view Generator Network for Shared Representation.
Proceedings of the 24th International Conference on Pattern Recognition, 2018

Interpretable Convolutional Neural Networks.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Learning Descriptor Networks for 3D Shape Synthesis and Analysis.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Learning Generative ConvNets via Multi-Grid Modeling and Sampling.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Interpreting CNN Knowledge via an Explanatory Graph.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Learning Multi-grid Generative ConvNets by Minimal Contrastive Divergence.
CoRR, 2017

A Cost-Sensitive Visual Question-Answer Framework for Mining a Deep And-OR Object Semantics from Web Images.
CoRR, 2017

Interactively Transferring CNN Patterns for Part Localization.
CoRR, 2017

Mining Object Parts from CNNs via Active Question-Answering.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Synthesizing Dynamic Patterns by Spatial-Temporal Generative ConvNet.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Generative Hierarchical Learning of Sparse FRAME Models.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

Alternating Back-Propagation for Generator Network.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Bayesian variable selection for finite mixture model of linear regressions.
Comput. Stat. Data Anal., 2016

Multi-Shot Mining Semantic Part Concepts in CNNs.
CoRR, 2016

Synthesizing Dynamic Textures and Sounds by Spatial-Temporal Generative ConvNet.
CoRR, 2016

Cooperative Training of Descriptor and Generator Networks.
CoRR, 2016

Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms.
CoRR, 2016

Learning Generative ConvNet with Continuous Latent Factors by Alternating Back-Propagation.
CoRR, 2016

A Theory of Generative ConvNet.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Learning FRAME Models Using CNN Filters.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Learning Sparse FRAME Models for Natural Image Patterns.
Int. J. Comput. Vis., 2015

Learning FRAME Models Using CNN Filters for Knowledge Visualization.
CoRR, 2015

Generative Modeling of Convolutional Neural Networks.
Proceedings of the 3rd International Conference on Learning Representations, 2015

Mining And-Or Graphs for Graph Matching and Object Discovery.
Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

2014
Statistical Independence.
Computer Vision, A Reference Guide, 2014

Histogram.
Computer Vision, A Reference Guide, 2014

Data Augmentation.
Computer Vision, A Reference Guide, 2014

Cross Entropy.
Computer Vision, A Reference Guide, 2014

Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD.
NeuroImage, 2014

Bayesian Variable Selection for Multi-response Linear Regression.
Proceedings of the Technologies and Applications of Artificial Intelligence, 2014

Learning Inhomogeneous FRAME Models for Object Patterns.
Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

Unsupervised Learning of Dictionaries of Hierarchical Compositional Models.
Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

2013
Cosegmentation and Cosketch by Unsupervised Learning.
Proceedings of the IEEE International Conference on Computer Vision, 2013

2011
Stochastic matching pursuit for Bayesian variable selection.
Stat. Comput., 2011

Image representation by active curves.
Proceedings of the IEEE International Conference on Computer Vision, 2011

2010
Learning Active Basis Model for Object Detection and Recognition.
Int. J. Comput. Vis., 2010

Wavelet, active basis, and shape script: a tour in the sparse land.
Proceedings of the 11th ACM SIGMM International Conference on Multimedia Information Retrieval, 2010

2009
Learning mixed templates for object recognition.
Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 2009

2007
Statistical Principles in Image Modeling.
Technometrics, 2007

Primal sketch: Integrating structure and texture.
Comput. Vis. Image Underst., 2007

A null space method for over-complete blind source separation.
Comput. Stat. Data Anal., 2007

Deformable Template As Active Basis.
Proceedings of the IEEE 11th International Conference on Computer Vision, 2007

2004
Information Scaling Laws in Natural Scenes.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2004

2003
Modeling Visual Patterns by Integrating Descriptive and Generative Methods.
Int. J. Comput. Vis., 2003

Dynamic Textures.
Int. J. Comput. Vis., 2003

Towards a Mathematical Theory of Primal Sketch and Sketchability.
Proceedings of the 9th IEEE International Conference on Computer Vision (ICCV 2003), 2003

2002
What Are Textons?
Proceedings of the Computer Vision, 2002

Statistical Modeling of Texture Sketch.
Proceedings of the Computer Vision, 2002

2001
Order Parameters for Detecting Target Curves in Images: When Does High Level Knowledge Help?
Int. J. Comput. Vis., 2001

Dynamic Textures.
Proceedings of the Eighth International Conference On Computer Vision (ICCV-01), Vancouver, British Columbia, Canada, July 7-14, 2001, 2001

Visual Learning by Integrating Descriptive and Generative Methods.
Proceedings of the Eighth International Conference On Computer Vision (ICCV-01), Vancouver, British Columbia, Canada, July 7-14, 2001, 2001

Dynamic Texture Recognition.
Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), 2001

2000
Exploring Texture Ensembles by Efficient Markov Chain Monte Carlo-Toward a 'Trichromacy' Theory of Texture.
IEEE Trans. Pattern Anal. Mach. Intell., 2000

Equivalence of Julesz Ensembles and FRAME Models.
Int. J. Comput. Vis., 2000

Order Parameters for Minimax Entropy Distributions: When Does High Level Knowledge Help?
Proceedings of the 2000 Conference on Computer Vision and Pattern Recognition (CVPR 2000), 2000

1999
From local features to global perception - A perspective of Gestalt psychology from Markov random field theory.
Neurocomputing, 1999

Equivalence of Julesz and Gibbs Texture Ensembles.
Proceedings of the International Conference on Computer Vision, 1999

1998
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling.
Int. J. Comput. Vis., 1998

1997
Minimax Entropy Principle and Its Application to Texture Modeling.
Neural Comput., 1997

Modeling images and textures by minimax entropy.
Proceedings of the Human Vision and Electronic Imaging II, 1997

1996
FRAME: Filters, Random fields, and Minimax Entropy - Towards a Unified Theory for Texture Modeling.
Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96), 1996


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