Guodong Zhang | DeepAI

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∙ 03/27/2023

Finite Strain Topology Optimization with Nonlinear Stability Constraints

This paper proposes a computational framework for the design optimizatio...

0 Guodong Zhang, et al. ∙

∙ 02/20/2023

Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation

Skip connections and normalisation layers form two standard architectura...

0 Bobby He, et al. ∙

∙ 01/18/2023

Understand Code Style: Efficient CNN-based Compiler Optimization Recognition System

Compiler optimization level recognition can be applied to vulnerability ...

0 Shouguo Yang, et al. ∙

∙ 03/15/2022

Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers

Training very deep neural networks is still an extremely challenging tas...

0 Guodong Zhang, et al. ∙

∙ 11/03/2021

On the Application of Data-Driven Deep Neural Networks in Linear and Nonlinear Structural Dynamics

The use of deep neural network (DNN) models as surrogates for linear and...

0 Nan Feng, et al. ∙

∙ 08/27/2021

Learning to Give Checkable Answers with Prover-Verifier Games

Our ability to know when to trust the decisions made by machine learning...

0 Cem Anil, et al. ∙

∙ 07/21/2021

Differentiable Annealed Importance Sampling and the Perils of Gradient Noise

Annealed importance sampling (AIS) and related algorithms are highly eff...

14 Guodong Zhang, et al. ∙

∙ 06/10/2021

A Central Limit Theorem, Loss Aversion and Multi-Armed Bandits

This paper establishes a central limit theorem under the assumption that...

0 Zengjing Chen, et al. ∙

∙ 02/18/2021

Don't Fix What ain't Broke: Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization

Minimax optimization has recently gained a lot of attention as adversari...

0 Guodong Zhang, et al. ∙

∙ 10/05/2020

Computational frameworks for homogenization and multiscale stability analyses of nonlinear periodic metamaterials

This paper presents a consistent computational framework for multiscale ...

0 Guodong Zhang, et al. ∙

∙ 09/23/2020

A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints

The theory of integral quadratic constraints (IQCs) allows the certifica...

0 Guodong Zhang, et al. ∙

∙ 08/17/2020

On the Suboptimality of Negative Momentum for Minimax Optimization

Smooth game optimization has recently attracted great interest in machin...

0 Guodong Zhang, et al. ∙

∙ 02/18/2020

Picking Winning Tickets Before Training by Preserving Gradient Flow

Overparameterization has been shown to benefit both the optimization and...

2 Chaoqi Wang, et al. ∙

∙ 10/16/2019

On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach

Many tasks in modern machine learning can be formulated as finding equil...

16 Yuanhao Wang, et al. ∙

∙ 07/09/2019

Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model

Increasing the batch size is a popular way to speed up neural network tr...

3 Guodong Zhang, et al. ∙

∙ 07/03/2019

Benchmarking Model-Based Reinforcement Learning

Model-based reinforcement learning (MBRL) is widely seen as having the p...

28 Tingwu Wang, et al. ∙

∙ 05/27/2019

Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks

Natural gradient descent has proven effective at mitigating the effects ...

35 Guodong Zhang, et al. ∙

∙ 05/15/2019

EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis

Reducing the test time resource requirements of a neural network while p...

10 Chaoqi Wang, et al. ∙

∙ 04/25/2019

Computational Design of Finite Strain Auxetic Metamaterials via Topology Optimization and Nonlinear Homogenization

A novel computational framework for designing metamaterials with negativ...

0 Guodong Zhang, et al. ∙

∙ 03/14/2019

Functional Variational Bayesian Neural Networks

Variational Bayesian neural networks (BNNs) perform variational inferenc...

34 Shengyang Sun, et al. ∙

∙ 02/21/2019

Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise

The choice of batch-size in a stochastic optimization algorithm plays a ...

0 Yeming Wen, et al. ∙

∙ 11/30/2018

Eigenvalue Corrected Noisy Natural Gradient

Variational Bayesian neural networks combine the flexibility of deep lea...

5 Juhan Bae, et al. ∙

∙ 10/29/2018

Three Mechanisms of Weight Decay Regularization

Weight decay is one of the standard tricks in the neural network toolbox...

2 Guodong Zhang, et al. ∙

∙ 06/12/2018

Differentiable Compositional Kernel Learning for Gaussian Processes

The generalization properties of Gaussian processes depend heavily on th...

2 Shengyang Sun, et al. ∙

∙ 12/06/2017

Noisy Natural Gradient as Variational Inference

Combining the flexibility of deep learning with Bayesian uncertainty est...

0 Guodong Zhang, et al. ∙

∙ 03/17/2017

Deformable Convolutional Networks

Convolutional neural networks (CNNs) are inherently limited to model geo...

0 Jifeng Dai, et al. ∙

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