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Found 2 papers, 1 papers with code
Neural Differential Recurrent Neural Network with Adaptive Time Steps
We propose an RNN-based model, called RNN-ODE-Adap, that uses a neural ODE to represent the time development of the hidden states, and we adaptively select time steps based on the steepness of changes of the data over time so as to train the model more efficiently for the "spike-like" time series.
Direct Object Recognition Without Line-of-Sight Using Optical Coherence
Visual object recognition under situations in which the direct line-of-sight is blocked, such as when it is occluded around the corner, is of practical importance in a wide range of applications.
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