How to construct contrastive views for hypergraphs via augmentations? We provide the solutions in two folds.

We contributeNeuralSolver, a novel recurrent solver that can efficiently and consistently extrapolate, i.e., learn algorithms from smaller problems (in terms of observation size) and execute those algorithms in large problems.

Conversely, the opposite scenario is relatively benign.

Givenanincoming stream ofvideoframes, online action detection [14]isconcerned withthetask of classifying what is happening at each frame without seeing the future.

Deep generativemodels cangenerate high-fidelity audio conditioned onvarious types of representations (e.g., mel-spectrograms, Mel-frequency Cepstral Coefficients (MFCC)). Recently, such models have been used to synthesize audio waveforms conditioned on highly compressed representations.