121 citations · 214 across the 22 of their papers we have counts for
11 papers · 1 filter
Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
Rui Zhang, Qi Meng, Rongchan Zhu +5
In scenarios with limited available data, training the function-to-function neural PDE solver in an unsupervised manner is essential. However, the efficiency and accuracy of existi…
Breaking Correlation Shift via Conditional Invariant Regularizer
Mingyang Yi, Ruoyu Wang, Jiachen Sun +2
Recently, generalization on out-of-distribution (OOD) data with correlation shift has attracted great attentions. The correlation shift is caused by the spurious attributes that co…
Incorporating NODE with Pre-trained Neural Differential Operator for Learning Dynamics
Shiqi Gong, Qi Meng, Yue Wang +4
Learning dynamics governed by differential equations is crucial for predicting and controlling the systems in science and engineering. Neural Ordinary Differential Equation (NODE),…
Improved OOD Generalization via Adversarial Training and Pre-training
Mingyang Yi, Lu Hou, Jiacheng Sun +4
Recently, learning a model that generalizes well on out-of-distribution (OOD) data has attracted great attention in the machine learning community. In this paper, after defining OO…
Reweighting Augmented Samples by Minimizing the Maximal Expected Loss
Mingyang Yi, Lu Hou, Lifeng Shang +3
Data augmentation is an effective technique to improve the generalization of deep neural networks. However, previous data augmentation methods usually treat the augmented samples e…
BN-invariant sharpness regularizes the training model to better generalization
Mingyang Yi, Huishuai Zhang, Wei Chen +2
It is arguably believed that flatter minima can generalize better. However, it has been pointed out that the usual definitions of sharpness, which consider either the maxima or the…
Characterization of Excess Risk for Locally Strongly Convex Population Risk
Mingyang Yi, Ruoyu Wang, Zhi-Ming Ma
We establish upper bounds for the expected excess risk of models trained by proper iterative algorithms which approximate the local minima. Unlike the results built upon the strong…
Dynamic of Stochastic Gradient Descent with State-Dependent Noise
Qi Meng, Shiqi Gong, Wei Chen +2
Stochastic gradient descent (SGD) and its variants are mainstream methods to train deep neural networks. Since neural networks are non-convex, more and more works study the dynamic…
Interpreting Basis Path Set in Neural Networks
Juanping Zhu, Qi Meng, Wei Chen +1
Based on basis path set, G-SGD algorithm significantly outperforms conventional SGD algorithm in optimizing neural networks. However, how the inner mechanism of basis paths work re…
Positively Scale-Invariant Flatness of ReLU Neural Networks
Mingyang Yi, Qi Meng, Wei Chen +2
It was empirically confirmed by Keskar et al.\cite{SharpMinima} that flatter minima generalize better. However, for the popular ReLU network, sharp minimum can also generalize well…
Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting
Yue Wang, Wei Chen, Yuting Liu +2
In reinforcement learning (RL) , one of the key components is policy evaluation, which aims to estimate the value function (i.e., expected long-term accumulated reward) of a policy…