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Found 47 papers, 16 papers with code
Complete and Efficient Graph Transformers for Crystal Material Property Prediction
Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space.
Dynamic Incremental Optimization for Best Subset Selection
Best subset selection is considered the `gold standard' for many sparse learning problems.
Causal Bayesian Optimization via Exogenous Distribution Learning
A new CBO method is developed by leveraging the learned exogenous distribution.
Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data
World is looking for clean and renewable energy sources that do not pollute the environment, in an attempt to reduce greenhouse gas emissions that contribute to global warming.
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks
Bayesian inference for neural networks, or Bayesian deep learning, has the potential to provide well-calibrated predictions with quantified uncertainty and robustness.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT).
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
This is enabled by our approximations of infinite potential summations, where we extend the Ewald summation for several potential series approximations with provable error bounds.
Ranked #1 on
Band Gap
on Materials Project
Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian
We consider the prediction of the Hamiltonian matrix, which finds use in quantum chemistry and condensed matter physics.
A Latent Diffusion Model for Protein Structure Generation
Proteins are complex biomolecules that perform a variety of crucial functions within living organisms.
Comprehensive analysis of gene expression profiles to radiation exposure reveals molecular signatures of low-dose radiation response
There are various sources of ionizing radiation exposure, where medical exposure for radiation therapy or diagnosis is the most common human-made source.
DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness
We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors.
Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data
Medical events of interest, such as mortality, often happen at a low rate in electronic medical records, as most admitted patients survive.
VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition
At its core is an implicit variational distribution on binary gates that are dependent on previous observations, which will select the next subset of features to observe.
MoReL: Multi-omics Relational Learning
Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of interest when studying life and disease systems.
Efficient Active Learning for Gaussian Process Classification by Error Reduction
Moreover, as the EER is not smooth, it can not be combined with gradient-based optimization techniques to efficiently explore the continuous instance space for query synthesis.
COVID-Datathon: Biomarker identification for COVID-19 severity based on BALF scRNA-seq data
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emergence began in late 2019 and has since spread rapidly worldwide.
Optimal Decision Making in High-Throughput Virtual Screening Pipelines
The need for efficient computational screening of molecular candidates that possess desired properties frequently arises in various scientific and engineering problems, including drug discovery and materials design.
Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning
In this paper, we fill this gap by investigating knowledge transferability in the context of classification error estimation within a Bayesian paradigm.
SimCD: Simultaneous Clustering and Differential expression analysis for single-cell transcriptomic data
We develop a new method -- SimCD -- that explicitly models cell heterogeneity and dynamic differential changes in one unified hierarchical gamma-negative binomial (hGNB) model, allowing simultaneous cell clustering and differential expression analysis for scRNA-seq data.
Geometric Affinity Propagation for Clustering with Network Knowledge
This interest primarily stems from the amount of compressed information encoded in these exemplars that effectively reflect the major characteristics of the respective clusters.
Contextual Dropout: An Efficient Sample-Dependent Dropout Module
However, the quality of uncertainty estimation is highly dependent on the dropout probabilities.
Uncertainty-aware Active Learning for Optimal Bayesian Classifier
For pool-based active learning, in each iteration a candidate training sample is chosen for labeling by optimizing an acquisition function.
Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition
In many machine learning tasks, input features with varying degrees of predictive capability are usually acquired at some cost.
BayReL: Bayesian Relational Learning for Multi-omics Data Integration
High-throughput molecular profiling technologies have produced high-dimensional multi-omics data, enabling systematic understanding of living systems at the genome scale.
Quantifying the multi-objective cost of uncertainty
Various real-world applications involve modeling complex systems with immense uncertainty and optimizing multiple objectives based on the uncertain model.
NADS: Neural Architecture Distribution Search for Uncertainty Awareness
Machine learning (ML) systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a distribution different from training data.
Probabilistic Best Subset Selection via Gradient-Based Optimization
This paper proposes a novel optimization method to solve the exact L0-regularized regression problem, which is also known as the best subset selection.
Methodology
Bayesian Graph Neural Networks with Adaptive Connection Sampling
We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) that generalizes existing stochastic regularization methods for training GNNs.
Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator
Semantic hashing has become a crucial component of fast similarity search in many large-scale information retrieval systems, in particular, for text data.
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unknown contexts and activities may occur from time to time, requiring flexibility and adaptability of the algorithm.
Learnable Bernoulli Dropout for Bayesian Deep Learning
In this work, we propose learnable Bernoulli dropout (LBD), a new model-agnostic dropout scheme that considers the dropout rates as parameters jointly optimized with other model parameters.
ARSM Gradient Estimator for Supervised Learning to Rank
Learning-to-rank methods can generally be categorized into pointwise, pairwise, and listwise approaches.
Semi-Implicit Stochastic Recurrent Neural Networks
Stochastic recurrent neural networks with latent random variables of complex dependency structures have shown to be more successful in modeling sequential data than deterministic deep models.
Variational Graph Recurrent Neural Networks
Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant.
Ranked #2 on
Dynamic Link Prediction
on DBLP Temporal
Semi-Implicit Graph Variational Auto-Encoders
Compared to VGAE, the derived graph latent representations by SIG-VAE are more interpretable, due to more expressive generative model and more faithful inference enabled by the flexible semi-implicit construction.
Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images
In either way, the loss of local fine details or global contextual information results in limited segmentation accuracy.
Ranked #4 on
Land Cover Classification
on DeepGlobe
Optimal Clustering with Missing Values
Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements.
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models
This power-efficient sensing scheme can be achieved by deciding which group of sensors to use at a given time, requiring an accurate characterization of the trade-off between sensor energy usage and the uncertainty in ignoring certain sensor signals while monitoring.
Fast Exact Computation of Expected HyperVolume Improvement
The first grid-based algorithm has a complexity of $O(m\cdot n^m)$ with $n$ denoting the size of the nondominated set and $m$ the number of objectives.
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data
Second, compared to the number of involved molecules and system complexity, the number of available samples for studying complex disease, such as cancer, is often limited, especially considering disease heterogeneity.
Unsupervised CNN-based Co-Saliency Detection with Graphical Optimization
In this paper, we address co-saliency detection in a set of images jointly covering objects of a specific class by an unsupervised convolutional neural network (CNN).
Safe Active Feature Selection for Sparse Learning
More critically, SAIF has the safe guarantee as it has the convergence guarantee to the optimal solution to the original full LASSO problem.
Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain
Next-generation sequencing (NGS) to profile temporal changes in living systems is gaining more attention for deriving better insights into the underlying biological mechanisms compared to traditional static sequencing experiments.
Optimal Bayesian Transfer Learning
Transfer learning has recently attracted significant research attention, as it simultaneously learns from different source domains, which have plenty of labeled data, and transfers the relevant knowledge to the target domain with limited labeled data to improve the prediction performance.
Prognostics of Surgical Site Infections using Dynamic Health Data
Since existing prediction models of SSI have quite limited capacity to utilize the evolving clinical data, we develop the corresponding solution to equip these mHealth tools with decision-making capabilities for SSI prediction with a seamless assembly of several machine learning models to tackle the analytic challenges arising from the spatial-temporal data.
Detection of Cooperative Interactions in Logistic Regression Models
For the case when the underlying interaction graph is known to be acyclic, it is shown that a simple algorithm that is based on a maximum-weight spanning tree with respect to the plug-in estimates of the influences not only has strong theoretical performance guarantees, but can also outperform generic feature selection algorithms for recovering the interaction graph from i. i. d.
