连接点：模式连通性是否是贝叶斯神经网络中可行样本推断的关键？

Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency

There has been a recent surge in research on adversarial perturbations that defeat Deep Neural Networks (DNNs) in machine vision; most of these perturbation-based attacks target object classifiers. Inspired by the observation that humans are able to recognize objects that appear out of place in a scene or along with other unlikely objects, we augment the DNN with a system that learns context consistency rules during training and checks for the violations of the same during testing. Our approach builds a set of auto-encoders, one for each object class, appropriately trained so as to output a di…

发现数据中的图结构和函数关系：连接数据点的Gaussian Process框架

Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots

针对超图结构未知的复杂数据发现问题，提出一种可解释的Gaussian Process框架，无需数据随机化或采样控制即可发现图结构并逼近未知函数，以多项式复杂度实现高效计算。

Connecting the Dots Between MLE and RL for Sequence Prediction

Sequence prediction models can be learned from example sequences with a variety of training algorithms. Maximum likelihood learning is simple and efficient, yet can suffer from compounding error at test time. Reinforcement learning such as policy gradient addresses the issue but can have prohibitively poor exploration efficiency. A rich set of other algorithms such as RAML, SPG, and data noising, have also been developed from different perspectives. This paper establishes a formal connection between these algorithms. We present a generalized entropy regularized policy optimization formulation,…

Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumption behind multivariate time series forecasting is that its variables depend on one another but, upon looking closely, it is fair to say that existing methods fail to fully exploit latent spatial dependencies between pairs of variables. In recent years, meanwhile, graph neural networks (GNNs) have shown high capability in handling relational dependencies. GNNs require well-defined graph structures for information propa…

探索脑启发式机器智能：通过振荡同步的全息蓝图连接图中的点

Explore Brain-Inspired Machine Intelligence for Connecting Dots on Graphs Through Holographic Blueprint of Oscillatory Synchronization

本文提出HoloGraph框架，通过振荡同步机制改进图神经网络，解决过平滑问题并提升图推理能力。

VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models

Machine learning (ML) is increasingly applied to Electronic Health Records (EHRs) to solve clinical prediction tasks. Although many ML models perform promisingly, issues with model transparency and interpretability limit their adoption in clinical practice. Directly using existing explainable ML techniques in clinical settings can be challenging. Through literature surveys and collaborations with six clinicians with an average of 17 years of clinical experience, we identified three key challenges, including clinicians' unfamiliarity with ML features, lack of contextual information, and the nee…