20 papers with code • 1 benchmarks • 2 datasets
Predict human activities in videos
Most implemented papers
Molecular De Novo Design through Deep Reinforcement Learning
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.
Peeking into the Future: Predicting Future Person Activities and Locations in Videos
To facilitate the training, the network is learned with an auxiliary task of predicting future location in which the activity will happen.
Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning
The derived uncertainty-based ranking loss is found to significantly boost model performance by improving the quality of relational features.
HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors
The main streams of human activity recognition (HAR) algorithms are developed based on RGB cameras which are suffered from illumination, fast motion, privacy-preserving, and large energy consumption.
DeepProcess: Supporting business process execution using a MANN-based recommender system
Process-aware Recommender systems can provide critical decision support functionality to aid business process execution by recommending what actions to take next.
Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points
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.
Ionospheric activity prediction using convolutional recurrent neural networks
The ionosphere electromagnetic activity is a major factor of the quality of satellite telecommunications, Global Navigation Satellite Systems (GNSS) and other vital space applications.
Data-driven Perception of Neuron Point Process with Unknown Unknowns
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.
INFER: INtermediate representations for FuturE pRediction
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.