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Found 8 papers, 3 papers with code
Information Obfuscation of Graph Neural Networks
While the advent of Graph Neural Networks (GNNs) has greatly improved node and graph representation learning in many applications, the neighborhood aggregation scheme exposes additional vulnerabilities to adversaries seeking to extract node-level information about sensitive attributes.
Towards modular and programmable architecture search
We release an implementation of our language with this paper.
Recurrent Predictive State Policy Networks
Predictive state policy networks consist of a recursive filter, which keeps track of a belief about the state of the environment, and a reactive policy that directly maps beliefs to actions, to maximize the cumulative reward.
Learning General Latent-Variable Graphical Models with Predictive Belief Propagation
Learning general latent-variable probabilistic graphical models is a key theoretical challenge in machine learning and artificial intelligence.
Predictive State Recurrent Neural Networks
We present a new model, Predictive State Recurrent Neural Networks (PSRNNs), for filtering and prediction in dynamical systems.
Practical Learning of Predictive State Representations
Unfortunately it is not obvious how to apply apply an EM style algorithm in the context of PSRs as the Log Likelihood is not well defined for all PSRs.
Supervised Learning for Dynamical System Learning
To address this problem, we present a new view of dynamical system learning: we show how to learn dynamical systems by solving a sequence of ordinary supervised learning problems, thereby allowing users to incorporate prior knowledge via standard techniques such as L1 regularization.
Hilbert Space Embeddings of Predictive State Representations
The essence is to represent the state as a nonparametric conditional embedding operator in a Reproducing Kernel Hilbert Space (RKHS) and leverage recent work in kernel methods to estimate, predict, and update the representation.
