3 citations · 12 across the 7 of their papers we have counts for
6 papers · 1 filter
Beyond Static Positional Encoding: Bayesian Time-aware Attention for Non-Stationary Long-term Time Series Forecasting
Xinjie Lan, Yunbo Mei, Leheng Zhou +1
Transformers have demonstrated strong performance in long-term time series forecasting (LTSF), yet modeling non-stationary temporal dependencies remains challenging. In this paper,…
A Probabilistic Representation of DNNs: Bridging Mutual Information and Generalization
Xinjie Lan, Kenneth Barner
Recently, Mutual Information (MI) has attracted attention in bounding the generalization error of Deep Neural Networks (DNNs). However, it is intractable to accurately estimate the…
A Probabilistic Representation of Deep Learning for Improving The Information Theoretic Interpretability
Xinjie Lan, Kenneth E. Barner
In this paper, we propose a probabilistic representation of MultiLayer Perceptrons (MLPs) to improve the information-theoretic interpretability. Above all, we demonstrate that the…
PAC-Bayesian Generalization Bounds for MultiLayer Perceptrons
Xinjie Lan, Xin Guo, Kenneth E. Barner
We study PAC-Bayesian generalization bounds for Multilayer Perceptrons (MLPs) with the cross entropy loss. Above all, we introduce probabilistic explanations for MLPs in two aspect…
Explicitly Bayesian Regularizations in Deep Learning
Xinjie Lan, Kenneth E. Barner
Generalization is essential for deep learning. In contrast to previous works claiming that Deep Neural Networks (DNNs) have an implicit regularization implemented by the stochastic…
A Probabilistic Representation of Deep Learning
Xinjie Lan, Kenneth E. Barner
In this work, we introduce a novel probabilistic representation of deep learning, which provides an explicit explanation for the Deep Neural Networks (DNNs) in three aspects: (i) n…