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Found 19 papers, 12 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.
Benchmarking Observational Studies with Experimental Data under Right-Censoring
Drawing causal inferences from observational studies (OS) requires unverifiable validity assumptions; however, one can falsify those assumptions by benchmarking the OS with experimental data from a randomized controlled trial (RCT).
BLIS-Net: Classifying and Analyzing Signals on Graphs
We make several crucial changes to the original geometric scattering architecture which we prove increase the ability of our network to capture information about the input signal and show that BLIS-Net achieves superior performance on both synthetic and real-world data sets based on traffic flow and fMRI data.
Manifold Filter-Combine Networks
We introduce a class of manifold neural networks (MNNs) that we call Manifold Filter-Combine Networks (MFCNs), that aims to further our understanding of MNNs, analogous to how the aggregate-combine framework helps with the understanding of graph neural networks (GNNs).
Inferring dynamic regulatory interaction graphs from time series data with perturbations
Complex systems are characterized by intricate interactions between entities that evolve dynamically over time.
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction
Finally, we show that parameters of our more general method can be configured to give results similar to PHATE (a state-of-the-art diffusion based manifold learning method) as well as SNE (an attraction/repulsion neighborhood based method that forms the basis of t-SNE).
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.
Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections
These models provide a continuous-time latent representation of the underlying dynamical system where new observations at arbitrary time points can be used to update the latent representation of the dynamical system.
Learning predictive checklists from continuous medical data
Checklists, while being only recently introduced in the medical domain, have become highly popular in daily clinical practice due to their combined effectiveness and great interpretability.
Deep Counterfactual Estimation with Categorical Background Variables
The standard approach to estimate counterfactuals resides in using a structural equation model that accurately reflects the underlying data generating process.
Predicting the impact of treatments over time with uncertainty aware neural differential equations
In this work, we propose Counterfactual ODE (CF-ODE), a novel method to predict the impact of treatments continuously over time using Neural Ordinary Differential Equations equipped with uncertainty estimates.
Learning dynamical systems from data: A simple cross-validation perspective, part III: Irregularly-Sampled Time Series
A simple and interpretable way to learn a dynamical system from data is to interpolate its vector-field with a kernel.
The magnitude vector of images
Furthermore, we demonstrate practical use cases of magnitude for machine learning applications and propose a novel magnitude model that consists of a computationally efficient magnitude computation and a learnable metric.
Topological Graph Neural Networks
Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures such as cycles.
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.
Longitudinal modeling of MS patient trajectories improves predictions of disability progression
In this work, we address the task of optimally extracting information from longitudinal patient data in the real-world setting with a special focus on the sporadic sampling problem.
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
Deep Ensemble Tensor Factorization for Longitudinal Patient Trajectories Classification
We present a generative approach to classify scarcely observed longitudinal patient trajectories.
