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Found 12 papers, 6 papers with code
Diff-BBO: Diffusion-Based Inverse Modeling for Black-Box Optimization
Empirically, we redesign experiments on the Design-Bench benchmark for online settings and show that Diff-BBO achieves state-of-the-art performance.
Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling
To address these limitations, we propose Multi-fidelity Residual Neural Processes (MFRNP), a novel multi-fidelity surrogate modeling framework.
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
To constrain the optimization process to the data manifold, we reformulate the original optimization problem as a sampling problem from the product of the Boltzmann distribution defined by the objective function and the data distribution learned by the diffusion model.
MFBind: a Multi-Fidelity Approach for Evaluating Drug Compounds in Practical Generative Modeling
Current generative models for drug discovery primarily use molecular docking to evaluate the quality of generated compounds.
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
In particular, we are interested in discovering instance-level causal structures in an unsupervised manner.
Disentangled Multi-Fidelity Deep Bayesian Active Learning
To balance quality and cost, various domain areas of science and engineering run simulations at multiple levels of sophistication.
Multi-fidelity Hierarchical Neural Processes
MF-HNP is flexible enough to handle non-nested high dimensional data at different fidelity levels with varying input and output dimensions.
Multi defect detection and analysis of electron microscopy images with deep learning
Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis.
A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations
The system provides analysis of features observed in TEM including both static and dynamic properties using the YOLO-based defect detection module coupled to a geometry analysis module and a dynamic tracking module.
Deep Bayesian Active Learning for Accelerating Stochastic Simulation
We propose Interactive Neural Process (INP), a deep Bayesian active learning framework for learning deep surrogate models to accelerate stochastic simulations.
Quantifying Uncertainty in Deep Spatiotemporal Forecasting
Deep learning is gaining increasing popularity for spatiotemporal forecasting.
DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting
We introduce DeepGLEAM, a hybrid model for COVID-19 forecasting.
