
A Representational Model of Grid Cells Based on Matrix Lie Algebras
The grid cells in the mammalian medial entorhinal cortex exhibit strikin...
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Learning Latent Space EnergyBased Prior Model
The generator model assumes that the observed example is generated by a ...
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Learning Energybased Model with Flowbased Backbone by Neural Transport MCMC
Learning energybased model (EBM) requires MCMC sampling of the learned ...
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Closed Loop NeuralSymbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning
The goal of neuralsymbolic computation is to integrate the connectionis...
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Joint Training of Variational AutoEncoder and Latent EnergyBased Model
This paper proposes a joint training method to learn both the variationa...
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Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense
Recent progress in deep learning is essentially based on a "big data for...
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Generative PointNet: EnergyBased Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification
We propose a generative model of unordered point sets, such as point clo...
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Learning Deep Generative Models with Short Run Inference Dynamics
This paper studies the fundamental problem of learning deep generative m...
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Flow Contrastive Estimation of EnergyBased Models
This paper studies a training method to jointly estimate an energybased...
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Representation Learning: A Statistical Perspective
Learning representations of data is an important problem in statistics a...
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MotionBased Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns
Dynamic patterns are characterized by complex spatial and motion pattern...
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Deep Unsupervised Clustering with Clustered Generator Model
This paper addresses the problem of unsupervised clustering which remain...
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Learning Energybased SpatialTemporal Generative ConvNets for Dynamic Patterns
Video sequences contain rich dynamic patterns, such as dynamic texture p...
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Towards Interpretable Image Synthesis by Learning Sparsely Connected ANDOR Networks
This paper proposes interpretable image synthesis by learning hierarchic...
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Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge
We report a neural architecture search framework, BioNAS, that is tailor...
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On Learning NonConvergent ShortRun MCMC Toward EnergyBased Model
This paper studies a curious phenomenon in learning energybased model (...
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Learning Trajectory Prediction with Continuous Inverse Optimal Control via Langevin Sampling of EnergyBased Models
Autonomous driving is a challenging multiagent domain which requires opt...
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MultiAgent Tensor Fusion for Contextual Trajectory Prediction
Accurate prediction of others' trajectories is essential for autonomous ...
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On the Anatomy of MCMCbased Maximum Likelihood Learning of EnergyBased Models
This study investigates the effects Markov Chain Monte Carlo (MCMC) samp...
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Multimodal Conditional Learning with Fast Thinking Policylike Model and Slow Thinking Plannerlike Model
This paper studies the supervised learning of the conditional distributi...
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Learning Vector Representation of Content and Matrix Representation of Change: Towards a Representational Model of V1
This paper entertains the hypothesis that the primary purpose of the cel...
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Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract)
This paper presents an unsupervised method to learn a neural network, na...
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Network Transplanting (extended abstract)
This paper focuses on a new task, i.e., transplanting a categoryandtas...
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Inducing Sparse Coding and AndOr Grammar from Generator Network
We introduce an explainable generative model by applying sparse operatio...
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Interpretable CNNs
This paper proposes a generic method to learn interpretable convolutiona...
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Divergence Triangle for Joint Training of Generator Model, Energybased Model, and Inference Model
This paper proposes the divergence triangle as a framework for joint tra...
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Learning Dynamic Generator Model by Alternating BackPropagation Through Time
This paper studies the dynamic generator model for spatialtemporal proc...
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Explanatory Graphs for CNNs
This paper introduces a graphical model, namely an explanatory graph, wh...
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Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering
In this paper, we present a method to mine objectpart patterns from con...
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Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation
Holistic 3D indoor scene understanding refers to jointly recovering the ...
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Learning Gridlike Units with Vector Representation of SelfPosition and Matrix Representation of SelfMotion
This paper proposes a model for learning gridlike units for spatial awa...
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A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models
The pattern theory of Grenander is a mathematical framework where the pa...
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Interactive Agent Modeling by Learning to Probe
The ability of modeling the other agents, such as understanding their in...
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Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry
We propose a deformable generator model to disentangle the appearance an...
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Unsupervised Learning of Neural Networks to Explain Neural Networks
This paper presents an unsupervised method to learn a neural network, na...
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Replicating Active Appearance Model by Generator Network
A recent Cell paper [Chang and Tsao, 2017] reports an interesting discov...
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Network Transplanting
This paper focuses on a novel problem, i.e., transplanting a categoryan...
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Learning Descriptor Networks for 3D Shape Synthesis and Analysis
This paper proposes a 3D shape descriptor network, which is a deep convo...
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Interpreting CNNs via Decision Trees
This paper presents a method to learn a decision tree to quantitatively ...
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Interpretable Convolutional Neural Networks
This paper proposes a method to modify traditional convolutional neural ...
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Learning Multigrid Generative ConvNets by Minimal Contrastive Divergence
This paper proposes a minimal contrastive divergence method for learning...
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A CostSensitive Visual QuestionAnswer Framework for Mining a Deep AndOR Object Semantics from Web Images
This paper presents a costsensitive QuestionAnswering (QA) framework f...
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Interpreting CNN Knowledge via an Explanatory Graph
This paper learns a graphical model, namely an explanatory graph, which ...
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Interactively Transferring CNN Patterns for Part Localization
In the scenario of one/multishot learning, conventional endtoend lear...
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Mining Object Parts from CNNs via Active QuestionAnswering
Given a convolutional neural network (CNN) that is pretrained for objec...
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Growing Interpretable Part Graphs on ConvNets via MultiShot Learning
This paper proposes a learning strategy that extracts objectpart concep...
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Cooperative Training of Descriptor and Generator Networks
This paper studies the cooperative training of two probabilistic models ...
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Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Nonnegative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Spar
Brain networks in fMRI are typically identified using spatial independen...
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Alternating BackPropagation for Generator Network
This paper proposes an alternating backpropagation algorithm for learni...
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Synthesizing Dynamic Patterns by SpatialTemporal Generative ConvNet
Video sequences contain rich dynamic patterns, such as dynamic texture p...
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Ying Nian Wu
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Professor of Department of Statistics at University of California, Los Angeles since 2006, Associate Professor of Department of Statistics at University of California, Los Angeles from 20012006, Associate Professor of Department of Statistics at University of California, Los Angeles from 19992001, Assistant Professor, Department of Statistics, University of Michigan from 19971999, Summers 1996, 1997: Visitor, Bell labs, Ph.D. in Statistics, Harvard, November 1996.