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Found 24 papers, 6 papers with code
Enhancing Future Link Prediction in Quantum Computing Semantic Networks through LLM-Initiated Node Features
This study proposes initializing node features using LLMs to enhance node representations for link prediction tasks in graph neural networks.
Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate Data
The world is moving towards clean and renewable energy sources, such as wind energy, in an attempt to reduce greenhouse gas emissions that contribute to global warming.
Understanding Uncertainty-based Active Learning Under Model Mismatch
Instead of randomly acquiring training data points, Uncertainty-based Active Learning (UAL) operates by querying the label(s) of pivotal samples from an unlabeled pool selected based on the prediction uncertainty, thereby aiming at minimizing the labeling cost for model training.
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders
A critical challenge for pre-trained generative molecular design (GMD) models is to fine-tune them to be better suited for downstream design tasks aimed at optimizing specific molecular properties.
Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior for Arbitrary-scale Super-resolution
Grounded in operator learning, the proposed method is resolution-invariant.
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design
Deep generative models have been accelerating the inverse design process in material and drug design.
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.
Current and future directions in network biology
Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales.
Identifying Bayesian Optimal Experiments for Uncertain Biochemical Pathway Models
This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model.
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.
Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge
Understanding protein interactions and pathway knowledge is crucial for unraveling the complexities of living systems and investigating the underlying mechanisms of biological functions and complex diseases.
A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows
The ability to replicate predictions by machine learning (ML) or artificial intelligence (AI) models and results in scientific workflows that incorporate such ML/AI predictions is driven by numerous factors.
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.
Sequential Bayesian Neural Subnetwork Ensembles
Additionally, while sparse subnetworks of dense models have shown promise in matching the performance of their dense counterparts and even enhancing robustness, existing methods for inducing sparsity typically incur training costs comparable to those of training a single dense model, as they either gradually prune the network during training or apply thresholding post-training.
Neural Message Passing for Objective-Based Uncertainty Quantification and Optimal Experimental Design
In this work, we propose a novel scheme to reduce the computational cost for objective-UQ via MOCU based on a data-driven approach.
Multi-Objective Latent Space Optimization of Generative Molecular Design Models
Molecular design based on generative models, such as variational autoencoders (VAEs), has become increasingly popular in recent years due to its efficiency for exploring high-dimensional molecular space to identify molecules with desired properties.
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.
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.
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.
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.
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.
Optimal Experimental Design for Uncertain Systems Based on Coupled Differential Equations
We consider the optimal experimental design problem for an uncertain Kuramoto model, which consists of N interacting oscillators described by coupled ordinary differential equations.
Autism spectrum disorder: a neuro-immunometabolic hypothesis of the developmental origins
Fetal neuroinflammation and prenatal stress (PS) may contribute to lifelong neurological disabilities.
