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Found 8 papers, 4 papers with code
RADNet: A Deep Neural Network Model for Robust Perception in Moving Autonomous Systems
For the high ranking camera videos we show that the accuracy of action detection is decreased.
Unraveled Multilevel Transformation Networks for Predicting Sparsely-Observed Spatiotemporal Dynamics
In this paper, we address the problem of predicting complex, nonlinear spatiotemporal dynamics when available data is recorded at irregularly-spaced sparse spatial locations.
A Deep Learning Approach for Predicting Spatiotemporal Dynamics From Sparsely Observed Data
In this paper, we consider the problem of learning prediction models for spatiotemporal physical processes driven by unknown partial differential equations (PDEs).
Neural Identification for Control
The proposed method relies on the Lyapunov stability theory to generate a stable closed-loop dynamics hypothesis and corresponding control law.
Physics-Incorporated Convolutional Recurrent Neural Networks for Source Identification and Forecasting of Dynamical Systems
Spatio-temporal dynamics of physical processes are generally modeled using partial differential equations (PDEs).
MagNet: Discovering Multi-agent Interaction Dynamics using Neural Network
We present the MagNet, a neural network-based multi-agent interaction model to discover the governing dynamics and predict evolution of a complex multi-agent system from observations.
SAFE-DNN: A Deep Neural Network with Spike Assisted Feature Extraction for Noise Robust Inference
We present a Deep Neural Network with Spike Assisted Feature Extraction (SAFE-DNN) to improve robustness of classification under stochastic perturbation of inputs.
Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms
We evaluate our proposed method for various machine learning tasks including object detection on MS-COCO 2014 dataset, multiple object tracking problem on MOT-Challenge dataset, and human activity classification on UCF 101 dataset.
