
Interpretable CNNs
This paper proposes a generic method to learn interpretable convolutiona...
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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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Learning Energybased SpatialTemporal Generative ConvNets for Dynamic Patterns
Video sequences contain rich dynamic patterns, such as dynamic texture p...
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Explanatory Graphs for CNNs
This paper introduces a graphical model, namely an explanatory graph, wh...
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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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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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Towards Interpretable Image Synthesis by Learning Sparsely Connected ANDOR Networks
This paper proposes interpretable image synthesis by learning hierarchic...
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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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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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On Learning NonConvergent ShortRun MCMC Toward EnergyBased Model
This paper studies a curious phenomenon in learning energybased model (...
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MultiAgent Tensor Fusion for Contextual Trajectory Prediction
Accurate prediction of others' trajectories is essential for autonomous ...
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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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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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Network Transplanting (extended abstract)
This paper focuses on a new task, i.e., transplanting a categoryandtas...
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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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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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Learning Dynamic Generator Model by Alternating BackPropagation Through Time
This paper studies the dynamic generator model for spatialtemporal proc...
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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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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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Growing Interpretable Part Graphs on ConvNets via MultiShot Learning
This paper proposes a learning strategy that extracts objectpart concep...
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A Theory of Generative ConvNet
We show that a generative random field model, which we call generative C...
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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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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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Learning Mixtures of Bernoulli Templates by TwoRound EM with Performance Guarantee
Dasgupta and Shulman showed that a tworound variant of the EM algorithm...
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Generative Modeling of Convolutional Neural Networks
The convolutional neural networks (CNNs) have proven to be a powerful to...
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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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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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Network Transplanting
This paper focuses on a novel problem, i.e., transplanting a categoryan...
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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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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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Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry
We propose a deformable generator model to disentangle the appearance an...
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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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Inducing Sparse Coding and AndOr Grammar from Generator Network
We introduce an explainable generative model by applying sparse operatio...
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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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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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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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Deep Unsupervised Clustering with Clustered Generator Model
This paper addresses the problem of unsupervised clustering which remain...
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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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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.