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Found 10 papers, 1 papers with code
Explaining Chemical Toxicity using Missing Features
Chemical toxicity prediction using machine learning is important in drug development to reduce repeated animal and human testing, thus saving cost and time.
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
CogMol also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations.
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection
This paper looks into the problem of detecting network anomalies by analyzing NetFlow records.
Simulation and Calibration of a Fully Bayesian Marked Multidimensional Hawkes Process with Dissimilar Decays
We propose a simulation method for multidimensional Hawkes processes based on superposition theory of point processes.
Hawkes Processes with Stochastic Excitations
We propose an extension to Hawkes processes by treating the levels of self-excitation as a stochastic differential equation.
Bibliographic Analysis with the Citation Network Topic Model
Bibliographic analysis considers author's research areas, the citation network and paper content among other things.
Twitter-Network Topic Model: A Full Bayesian Treatment for Social Network and Text Modeling
Exploiting this additional information, we propose the Twitter-Network (TN) topic model to jointly model the text and the social network in a full Bayesian nonparametric way.
Nonparametric Bayesian Topic Modelling with the Hierarchical Pitman-Yor Processes
In this article, we present efficient methods for the use of these processes in this hierarchical context, and apply them to latent variable models for text analytics.
Bibliographic Analysis on Research Publications using Authors, Categorical Labels and the Citation Network
In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents, using a nonparametric extension of a combination of the Poisson mixed-topic link model and the author-topic model.
Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon
Although social media data like tweets are laden with opinions, their "dirty" nature (as natural language) has discouraged researchers from applying LDA-based opinion model for product review mining.
