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11/30/2022
Taming Hyperparameter Tuning in Continuous Normalizing Flows Using the JKO Scheme
A normalizing flow (NF) is a mapping that transforms a chosen probabilit...
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09/29/2022
Improving Generative Flow Networks with Path Regularization
Generative Flow Networks (GFlowNets) are recently proposed models for le...
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05/18/2022
Neural ODE Control for Trajectory Approximation of Continuity Equation
We consider the controllability problem for the continuity equation, cor...
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04/11/2022
A numerical algorithm for inverse problem from partial boundary measurement arising from mean field game problem
In this work, we consider a novel inverse problem in mean-field games (M...
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03/11/2022
Parameter Inference of Time Series by Delay Embeddings and Learning Differentiable Operators
A common issue in dealing with real-world dynamical systems is identifyi...
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10/13/2021
How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies
We present and review an algorithmic and theoretical framework for impro...
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06/02/2021
Learn to Predict Equilibria via Fixed Point Networks
Systems of interacting agents can often be modeled as contextual games, ...
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03/23/2021
Fixed Point Networks: Implicit Depth Models with Jacobian-Free Backprop
A growing trend in deep learning replaces fixed depth models by approxim...
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08/05/2020
Projecting to Manifolds via Unsupervised Learning
We present a new framework, called adversarial projections, for solving ...
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05/01/2020
Exploring Private Federated Learning with Laplacian Smoothing
Federated learning aims to protect data privacy by collaboratively learn...
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11/02/2019
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo
As an important Markov Chain Monte Carlo (MCMC) method, stochastic gradi...
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03/13/2019
Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets
Training activation quantized neural networks involves minimizing a piec...
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02/06/2019
CESMA: Centralized Expert Supervises Multi-Agents
We consider the reinforcement learning problem of training multiple agen...
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08/15/2018
Blended Coarse Gradient Descent for Full Quantization of Deep Neural Networks
Quantized deep neural networks (QDNNs) are attractive due to their much ...
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06/17/2018
Laplacian Smoothing Gradient Descent
We propose a very simple modification of gradient descent and stochastic...
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01/19/2018
BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized Weights
We propose BinaryRelax, a simple two-phase algorithm, for training deep ...
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10/21/2017
Stochastic Backward Euler: An Implicit Gradient Descent Algorithm for k-means Clustering
In this paper, we propose an implicit gradient descent algorithm for the...
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04/09/2015
A Multiphase Image Segmentation Based on Fuzzy Membership Functions and L1-norm Fidelity
In this paper, we propose a variational multiphase image segmentation mo...
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06/30/2014
Sparse Recovery via Differential Inclusions
In this paper, we recover sparse signals from their noisy linear measure...
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02/04/2011