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Found 14 papers, 7 papers with code
Atom-Level Optical Chemical Structure Recognition with Limited Supervision
Identifying the chemical structure from a graphical representation, or image, of a molecule is a challenging pattern recognition task that would greatly benefit drug development.
Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection
In this work, we propose ProbKT, a framework based on probabilistic logical reasoning that allows to train object detection models with arbitrary types of weak supervision.
Collaborative Drug Discovery: Inference-level Data Protection Perspective
Pharmaceutical industry can better leverage its data assets to virtualize drug discovery through a collaborative machine learning platform.
SparseChem: Fast and accurate machine learning model for small molecules
SparseChem provides fast and accurate machine learning models for biochemical applications.
Self-Labeling of Fully Mediating Representations by Graph Alignment
In this paper we investigate the scenario of domain adaptation from the source domain where we have access to the expensive labels $V$ to the target domain where only normal labels W are available.
Latent Convergent Cross Mapping
Discovering causal structures of temporal processes is a major tool of scientific inquiry because it helps us better understand and explain the mechanisms driving a phenomenon of interest, thereby facilitating analysis, reasoning, and synthesis for such systems.
Multilevel Gibbs Sampling for Bayesian Regression
Additionally, the use of correlated samples is investigated for variance reduction to improve the convergence of the Markov Chain.
ChemGrapher: Optical Graph Recognition of Chemical Compounds by Deep Learning
Many thousands of scientific articles in chemistry and pharmaceutical sciences have investigated chemical compounds, but in cases the details of the structure of these chemical compounds is published only as an images.
Expressive Graph Informer Networks
Applying machine learning to molecules is challenging because of their natural representation as graphs rather than vectors. Several architectures have been recently proposed for deep learning from molecular graphs, but they suffer from informationbottlenecks because they only pass information from a graph node to its direct neighbors.
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series
Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed (i. e., sampling is irregular both in time and across dimensions)-such as in the case of clinical patient data.
Ranked #2 on
Multivariate Time Series Forecasting
on MIMIC-III
SMURFF: a High-Performance Framework for Matrix Factorization
Bayesian Matrix Factorization (BMF) is a powerful technique for recommender systems because it produces good results and is relatively robust against overfitting.
Deep Ensemble Tensor Factorization for Longitudinal Patient Trajectories Classification
We present a generative approach to classify scarcely observed longitudinal patient trajectories.
Highly Scalable Tensor Factorization for Prediction of Drug-Protein Interaction Type
Method: to analyze the interaction types we propose factorization method Macau which allows us to combine different measurement types into a single tensor together with proteins and compounds.
Macau: Scalable Bayesian Multi-relational Factorization with Side Information using MCMC
We propose Macau, a powerful and flexible Bayesian factorization method for heterogeneous data.
