Robust Near-Isometric Matching via Structured Learning of Graphical Models

Graphical Generative Adversarial Networks

We propose Graphical Generative Adversarial Networks (Graphical-GAN) to model structured data. Graphical-GAN conjoins the power of Bayesian networks on compactly representing the dependency structures among random variables and that of generative adversarial networks on learning expressive dependency functions. We introduce a structured recognition model to infer the posterior distribution of latent variables given observations. We generalize the Expectation Propagation (EP) algorithm to learn the generative model and recognition model jointly. Finally, we present two important instances of Gr…

Deep Structured Models For Group Activity Recognition

This paper presents a deep neural-network-based hierarchical graphical model for individual and group activity recognition in surveillance scenes. Deep networks are used to recognize the actions of individual people in a scene. Next, a neural-network-based hierarchical graphical model refines the predicted labels for each class by considering dependencies between the classes. This refinement step mimics a message-passing step similar to inference in a probabilistic graphical model. We show that this approach can be effective in group activity recognition, with the deep graphical model improvin…

基于结构化图模型与Transformer的模型预测仿真

Model Predictive Simulation Using Structured Graphical Models and Transformers

本文提出模型预测仿真（MPS）方法，将Transformer预测与概率图模型结合，通过高斯-牛顿MAP推断优化多智能体轨迹，在Waymo SimAgents挑战中显著降低碰撞率且无需额外训练。

Inducing Interpretable Representations with Variational Autoencoders

We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representations through approximate variational inference. This allows us to both perform reasoning (e.g. classification) under the structural constraints of a given graphical model, and use deep generative models to deal with messy, high-dimensional domains where it is often difficult to model all the variation. Learning in this framework is carried out end-to-end with a variational objective, applying to both unsupervised and se…