17 papers with code • 1 benchmarks • 2 datasets
Predict human activities in videos
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.
To facilitate the training, the network is learned with an auxiliary task of predicting future location in which the activity will happen.
The derived uncertainty-based ranking loss is found to significantly boost model performance by improving the quality of relational features.
Process-aware Recommender systems can provide critical decision support functionality to aid business process execution by recommending what actions to take next.
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.
The ionosphere electromagnetic activity is a major factor of the quality of satellite telecommunications, Global Navigation Satellite Systems (GNSS) and other vital space applications.
Previous research of neuron activity analysis is mainly limited with effects from the spiking history of target neuron and the interaction with other neurons in the system while ignoring the influence of unknown stimuli.
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).
Attention-based Multi-Input Deep Learning Architecture for Biological Activity Prediction: An Application in EGFR Inhibitors
These data were trained on a deep learning model which was also integrated with the Attention mechanism to facilitate training and interpreting.
Can x2vec Save Lives? Integrating Graph and Language Embeddings for Automatic Mental Health Classification
Visualizing graph embeddings annotated with predictions of potentially suicidal individuals shows the integrated model could classify such individuals even if they are positioned far from the support group.