Activity Prediction
24 papers with code • 1 benchmarks • 2 datasets
Predict human activities in videos
Home Action Genome
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Latest papers with no code
MFBind: a Multi-Fidelity Approach for Evaluating Drug Compounds in Practical Generative Modeling
Current generative models for drug discovery primarily use molecular docking to evaluate the quality of generated compounds.
SNAP: Semantic Stories for Next Activity Prediction
To address this gap, we propose the novel SNAP method that leverages language foundation models by constructing semantic contextual stories from the process historical event logs and using them for the next activity prediction.
A Nonparametric Bayes Approach to Online Activity Prediction
We derive closed-form expressions for the number of new users expected in a given period, and a simple Monte Carlo algorithm targeting the posterior distribution of the number of days needed to attain a desired number of users; the latter is important for experimental planning.
Short-term prediction of construction waste transport activities using AI-Truck
Construction waste hauling trucks (or `slag trucks') are among the most commonly seen heavy-duty diesel vehicles in urban streets, which not only produce significant carbon, NO$_{\textbf{x}}$ and PM$_{\textbf{2. 5}}$ emissions but are also a major source of on-road and on-site fugitive dust.
Target-Free Compound Activity Prediction via Few-Shot Learning
Predicting the activities of compounds against protein-based or phenotypic assays using only a few known compounds and their activities is a common task in target-free drug discovery.
Black-box Attacks on Image Activity Prediction and its Natural Language Explanations
We show that we can create adversarial images that manipulate the explanations of an activity recognition model by having access only to its final output.
A Discussion on Generalization in Next-Activity Prediction
Next activity prediction aims to forecast the future behavior of running process instances.
SAF: Smart Aggregation Framework for Revealing Atoms Importance Rank and Improving Prediction Rates in Drug Discovery
The ability of our approach to rank the atoms' importance for a desired function can be used within any graph neural network to provide interpretability of the results and predictions at the node level.
ALMERIA: Boosting pairwise molecular contrasts with scalable methods
It has been implemented using scalable software and methods to exploit large volumes of data -- in the order of several terabytes -- , offering a very quick response even for a large batch of queries.
Enhancing Model Learning and Interpretation Using Multiple Molecular Graph Representations for Compound Property and Activity Prediction
The results indicate that combining atom graph representation with reduced molecular graph representation can yield promising model performance.
