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09/10/2023
Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood
Training energy-based models (EBMs) with maximum likelihood estimation o...
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03/01/2023
Understanding the Diffusion Objective as a Weighted Integral of ELBOs
Diffusion models in the literature are optimized with various objectives...
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10/06/2022
On Distillation of Guided Diffusion Models
Classifier-free guided diffusion models have recently been shown to be h...
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10/05/2022
Imagen Video: High Definition Video Generation with Diffusion Models
We present Imagen Video, a text-conditional video generation system base...
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04/04/2021
Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis
How to effectively represent camera pose is an essential problem in 3D c...
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02/22/2021
A Theory of Label Propagation for Subpopulation Shift
One of the central problems in machine learning is domain adaptation. Un...
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09/22/2020
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot
Network pruning is a method for reducing test-time computational resourc...
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06/18/2020
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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06/12/2020
Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC
Learning energy-based model (EBM) requires MCMC sampling of the learned ...
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12/02/2019
Flow Contrastive Estimation of Energy-Based Models
This paper studies a training method to jointly estimate an energy-based...
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11/26/2019
Representation Learning: A Statistical Perspective
Learning representations of data is an important problem in statistics a...
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11/26/2019
Motion-Based 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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06/19/2019
Convergence of Adversarial Training in Overparametrized Networks
Neural networks are vulnerable to adversarial examples, i.e. inputs that...
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05/28/2019
A Gram-Gauss-Newton Method Learning Overparameterized Deep Neural Networks for Regression Problems
First-order methods such as stochastic gradient descent (SGD) are curren...
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01/24/2019
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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12/27/2018
Learning Dynamic Generator Model by Alternating Back-Propagation Through Time
This paper studies the dynamic generator model for spatial-temporal proc...
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10/12/2018
Learning Grid-like Units with Vector Representation of Self-Position and Matrix Representation of Self-Motion
This paper proposes a model for learning grid-like units for spatial awa...
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10/09/2018
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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06/16/2018
Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry
We propose a deformable generator model to disentangle the appearance an...
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04/02/2018
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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09/26/2017
Learning Multi-grid Generative ConvNets by Minimal Contrastive Divergence
This paper proposes a minimal contrastive divergence method for learning...
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09/29/2016