:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Bajracharya, Pradeep, Toledo-Marín, Javier Quetzalcóatl, Fox, Geoffrey, Jha, Shantenu, Wang, Linwei
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2407.07674
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Exploring the Energy Landscape of RBMs: Reciprocal Space Insights into Bosons, Hierarchical Learning and Symmetry Breaking
by: Toledo-Marin, J. Quetzalcóatl, et al.
Published: (2025)

Surrogate modeling of Cellular-Potts Agent-Based Models as a segmentation task using the U-Net neural network architecture
by: Comlekoglu, Tien, et al.
Published: (2025)

PHASE: Physics-Integrated, Heterogeneity-Aware Surrogates for Scientific Simulations
by: Gao, Dawei, et al.
Published: (2025)

An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry
by: Wang, Tianle, et al.
Published: (2025)

Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions
by: Toledo-Marin, J. Quetzalcoatl, et al.
Published: (2024)

Study of Dropout in PointPillars with 3D Object Detection
by: Sun, Xiaoxiang, et al.
Published: (2024)

Zephyr quantum-assisted hierarchical Calo4pQVAE for particle-calorimeter interactions
by: Lu, Ian, et al.
Published: (2024)

Improved Dimensionality Reduction for Inverse Problems in Nuclear Fusion and High-Energy Astrophysics
by: Gorard, Jonathan, et al.
Published: (2025)

Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge
by: Park, Gilchan, et al.
Published: (2023)

CaloQVAE : Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models
by: Hoque, Sehmimul, et al.
Published: (2023)

Maximally Separated Active Learning
by: Kasarla, Tejaswi, et al.
Published: (2024)

Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
by: Cestero, Julen, et al.
Published: (2025)

Accelerating Reinforcement Learning Training Using Simulation Surrogate Models
by: Ghasemloo, Mohammadmahdi, et al.
Published: (2026)

Frugal Federated Learning for Violence Detection: A Comparison of LoRA-Tuned VLMs and Personalized CNNs
by: Thuau, Sébastien, et al.
Published: (2025)

Active Learning of Piecewise Gaussian Process Surrogates
by: Park, Chiwoo, et al.
Published: (2023)

Deep Intrinsic Coregionalization Multi-Output Gaussian Process Surrogate with Active Learning
by: Chang, Chun-Yi, et al.
Published: (2025)

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models
by: Zhang, Ruiqi, et al.
Published: (2025)

Leveraging Exogenous Signals for Hydrology Time Series Forecasting
by: He, Junyang, et al.
Published: (2025)

An MLCommons Scientific Benchmarks Ontology
by: Hawks, Ben, et al.
Published: (2025)

Bayesian Active Learning for Discrete Latent Variable Models
by: Jha, Aditi, et al.
Published: (2022)

Deep Learning Foundation and Pattern Models: Challenges in Hydrological Time Series
by: He, Junyang, et al.
Published: (2024)

Generative diffusion model surrogates for mechanistic agent-based biological models (Training Data)
by: Comlekoglu, Tien, et al.
Published: (2025)

Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery
by: Hu, Jeffrey, et al.
Published: (2026)

ASPEST: Bridging the Gap Between Active Learning and Selective Prediction
by: Chen, Jiefeng, et al.
Published: (2023)

Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints
by: Im, Hyungki, et al.
Published: (2025)

An Operator Learning Framework for Spatiotemporal Super-resolution of Scientific Simulations
by: Duruisseaux, Valentin, et al.
Published: (2023)

Mobi-$π$: Mobilizing Your Robot Learning Policy
by: Yang, Jingyun, et al.
Published: (2025)

Unsupervised Learning of Hybrid Latent Dynamics: A Learn-to-Identify Framework
by: Ye, Yubo, et al.
Published: (2024)

Learning Surrogate LPV State-Space Models with Uncertainty Quantification
by: Olucha, E. Javier, et al.
Published: (2026)

PAGE: Domain-Incremental Adaptation with Past-Agnostic Generative Replay for Smart Healthcare
by: Li, Chia-Hao, et al.
Published: (2024)

VC Theory for Inventory Policies
by: Xie, Yaqi, et al.
Published: (2024)

ABM-UDE: Developing Surrogates for Epidemic Agent-Based Models via Scientific Machine Learning
by: Murgai, Sharv, et al.
Published: (2026)

Hamiltonian Neural Networks for Robust Out-of-Time Credit Scoring
by: Marín, Javier
Published: (2024)

A new framework for Marketing Mix Modeling: Addressing Channel Influence Bias and Cross-Channel Effects
by: Marin, Javier
Published: (2023)

Evaluating Synthetic Tabular Data Generated To Augment Small Sample Datasets
by: Marin, Javier
Published: (2022)

Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions
by: Fox, Zachary R., et al.
Published: (2024)

Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting
by: Jafari, Alireza, et al.
Published: (2024)

Flight Delay Prediction using Hybrid Machine Learning Approach: A Case Study of Major Airlines in the United States
by: Jha, Rajesh Kumar, et al.
Published: (2024)

Uncertainty-Aware Distribution-to-Distribution Flow Matching for Scientific Imaging
by: Wu, Dongxia, et al.
Published: (2026)

A Benchmark Study on Calibration
by: Tao, Linwei, et al.
Published: (2023)