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Found 7 papers, 1 papers with code
Learning Locally Interacting Discrete Dynamical Systems: Towards Data-Efficient and Scalable Prediction
Locally interacting dynamical systems, such as epidemic spread, rumor propagation through crowd, and forest fire, exhibit complex global dynamics originated from local, relatively simple, and often stochastic interactions between dynamic elements.
Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN
We show that the LNP can leverage diversity in neuronal timescales to design a sparse Heterogeneous RSNN (HRSNN).
Has the Deep Neural Network learned the Stochastic Process? A Wildfire Perspective
This paper presents the first systematic study of evalution of Deep Neural Network (DNN) designed and trained to predict the evolution of a stochastic dynamical system, using wildfire prediction as a case study.
Unsupervised 3D Object Learning through Neuron Activity aware Plasticity
We present an unsupervised deep learning model for 3D object classification.
Forecasting Local Behavior of Self-organizing Many-agent System without Reconstruction
As an example, we consider a forest fire model where we aim to predict when a particular tree agent will start burning.
Forecasting Evolution of Clusters in Game Agents with Hebbian Learning
In this light, clustering the agents in the game has been used for various purposes such as the efficient control of the agents in multi-agent reinforcement learning and game analytic tools for the game users.
Unsupervised Hebbian Learning on Point Sets in StarCraft II
Learning the evolution of real-time strategy (RTS) game is a challenging problem in artificial intelligent (AI) system.
