Predict human activities in videos
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To facilitate the training, the network is learned with an auxiliary task of predicting future location in which the activity will happen.
SOTA for Activity Prediction on ActEV
This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties.
Bayesian Matrix Factorization (BMF) is a powerful technique for recommender systems because it produces good results and is relatively robust against overfitting.
We propose Macau, a powerful and flexible Bayesian factorization method for heterogeneous data.
No spatial coherence is forced on the glimpse locations, which gives the module liberty to explore different points at each frame and better optimize the process of scrutinizing visual information.
#4 best model for Action Recognition In Videos on NTU RGB+D
Uncharacteristic of state-of-the-art approaches, our representations and models generalize to completely different datasets, collected across several cities, and also across countries where people drive on opposite sides of the road (left-handed vs right-handed driving).
These data were trained on a deep learning model which was also integrated with the Attention mechanism to facilitate training and interpreting.
SOTA for Drug Discovery on egfr-inh
The ionosphere electromagnetic activity is a major factor of the quality of satellite telecommunications, Global Navigation Satellite Systems (GNSS) and other vital space applications.