Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Chen, Xinshi"'
Graph neural networks (GNNs) enable the analysis of graphs using deep learning, with promising results in capturing structured information in graphs. This paper focuses on creating a small graph to represent the original graph, so that GNNs trained o
Externí odkaz:
http://arxiv.org/abs/2206.13697
Probabilistic Graphical Models (PGMs) are generative models of complex systems. They rely on conditional independence assumptions between variables to learn sparse representations which can be visualized in a form of a graph. Such models are used for
Externí odkaz:
http://arxiv.org/abs/2205.11610
Dynamic graphs with ordered sequences of events between nodes are prevalent in real-world industrial applications such as e-commerce and social platforms. However, representation learning for dynamic graphs has posed great computational challenges du
Externí odkaz:
http://arxiv.org/abs/2112.07768
We consider the problem of discovering $K$ related Gaussian directed acyclic graphs (DAGs), where the involved graph structures share a consistent causal order and sparse unions of supports. Under the multi-task learning setting, we propose a $l_1/l_
Externí odkaz:
http://arxiv.org/abs/2111.02545
Recently, there has been a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks. In many cases, a reasoning task can be solved by an iterative algorithm. This algorithm is often unrol
Externí odkaz:
http://arxiv.org/abs/2006.13401
There is a recent surge of interest in designing deep architectures based on the update steps in traditional algorithms, or learning neural networks to improve and replace traditional algorithms. While traditional algorithms have certain stopping cri
Externí odkaz:
http://arxiv.org/abs/2006.05082
Publikováno v:
International Conference on Learning Representations 2020, https://openreview.net/forum?id=S1eALyrYDH
In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints in the problem. The key idea of E2Efold is to directly predict the RN
Externí odkaz:
http://arxiv.org/abs/2002.05810
Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic graphical models, can be used to address many knowledge graph problems. However, inference in MLN is computationally intensive, making the industrial-scale applicatio
Externí odkaz:
http://arxiv.org/abs/2001.11850
Autor:
Chen, Xinshi
To better understand and improve the behavior of neural networks, a recent line of works bridged the connection between ordinary differential equations (ODEs) and deep neural networks (DNNs). The connections are made in two folds: (1) View DNN as ODE
Externí odkaz:
http://arxiv.org/abs/1911.00502
Effectively combining logic reasoning and probabilistic inference has been a long-standing goal of machine learning: the former has the ability to generalize with small training data, while the latter provides a principled framework for dealing with
Externí odkaz:
http://arxiv.org/abs/1906.02111