Activity Prediction
24 papers with code • 1 benchmarks • 2 datasets
Predict human activities in videos
Home Action Genome
InfiniteRep
Latest papers with no code
Syntactic Structure Processing in the Brain while Listening
In this study, we investigate the predictive power of the brain encoding models in three settings: (i) individual performance of the constituency and dependency syntactic parsing based embedding methods, (ii) efficacy of these syntactic parsing based embedding methods when controlling for basic syntactic signals, (iii) relative effectiveness of each of the syntactic embedding methods when controlling for the other.
SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers
We investigate Siamese networks for learning related embeddings for augmented samples of molecular conformers.
Modeling Time-Series and Spatial Data for Recommendations and Other Applications
Moreover, to provide accurate sequence modeling frameworks, we design solutions for points-of-interest recommendation, i. e., models that can handle spatial mobility data of users to various POI check-ins and recommend candidate locations for the next check-in.
Random Copolymer inverse design system orienting on Accurate discovering of Antimicrobial peptide-mimetic copolymers
Herein, we develop a universal random copolymer inverse design system via multi-model copolymer representation learning, knowledge distillation and reinforcement learning.
Predicting User-specific Future Activities using LSTM-based Multi-label Classification
User-specific future activity prediction in the healthcare domain based on previous activities can drastically improve the services provided by the nurses.
VDDB: a comprehensive resource and machine learning platform for antiviral drug discovery
Virus infection is one of the major diseases that seriously threaten human health.
Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition
Human activity recognition (HAR) with wearables is promising research that can be widely adopted in many smart healthcare applications.
A Ligand-and-structure Dual-driven Deep Learning Method for the Discovery of Highly Potent GnRH1R Antagonist to treat Uterine Diseases
Gonadotrophin-releasing hormone receptor (GnRH1R) is a promising therapeutic target for the treatment of uterine diseases.
Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning
The results show that the recommendations from our proposed approach outperform in goal satisfaction and conformance compared to the existing state-of-the-art next best activity recommendation techniques.
Event Log Sampling for Predictive Monitoring
Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances.
