Thus, we propose Graph Condesation via Receptive Field Distribution Matching (GCDM), which is accomplished by optimizing the synthetic graph through the use of a distribution matching loss quantified by maximum mean discrepancy (MMD).
Our model, uGLAD, builds upon and extends the state-of-the-art model GLAD to the unsupervised setting.
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.
We propose a surprisingly simple but effective two-time-scale (2TS) model for learning user representations for recommendation.
Recently, there has been a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks.
Similar to algorithms, the optimal depth of a deep architecture may be different for different input instances, either to avoid ``over-thinking'', or because we want to compute less for operations converged already.
The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled algorithm for constrained programming as the template for deep architectures to enforce constraints.
In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN.
Finally, two applications of using a continuous model will be shown in Section 5 and 6 to demonstrate some of its advantages over traditional neural networks.
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 noisy data.
Recently, there is a surge of interest to learn algorithms directly based on data, and in this case, learn to map empirical covariance to the sparse precision matrix.
We present a particle flow realization of Bayes' rule, where an ODE-based neural operator is used to transport particles from a prior to its posterior after a new observation.
There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems.