List of accepted papers

Oral presentations

• DeepRHP: A Hybrid Variational Autoencoder for Designing Random Heteropolymers as Protein Mimics

• InvRT: Solving Radar Inverse Problems with Transformers

• Automatic Electronic Circuit Topology Generation Based on Monte Carlo Tree Search

• Self-Supervised Learning for Modeling Gamma-ray Variability in Blazars

• Geometry-Complete Perceptron Networks for 3D Molecular Graphs

• Multi-Region Transfer Learning for Segmentation of Crop Field Boundaries in Satellite Images with Limited Labels

• Robustness in Fatigue Strength Estimation

• Speeding up Adaptive Stress Testing: Reinforcement Learning using Monte Carlo Tree Search with Neural Networks and Memoization

Poster presentations

Some of our presenters couldn’t attend the workshop in person. They kindly sent us a video presentation of their work. Please find them listed with Video Link in front of the corresponding paper below.

• Accelerating Linear Programming Solving by Exploiting the Performance Variability via Reinforcement Learning | Video Link

• On the Applicability of Synthetic Data for Re-Identification in Warehousing Logistics

• Knowledge-Guided Recurrent Neural Networks for Monthly Forest Carbon Uptake Estimation

• Comparing Search Algorithms on the Retrosynthesis Problem

• 3D Scalable Quantum Convolutional Neural Networks for Point Cloud Data Processing in Classification Applications

• Towards Seamless Management of AI Models in High-Performance Computing

• Prediction of Plasmonic Metasurface Nanofeatures Using a Modified VAE Regressor | Video Link

• Artificial Intelligence to Accelerate the Design and Discovery of Intelligent Metamaterials: The Way Forward

• Solving the HP model with Nested Monte Carlo Search

• Position Paper on Dataset Engineering to Accelerate Science

• Knowledge-augmented Risk Assessment (KaRA): a hybrid-intelligence framework for supporting knowledge-intensive risk assessment of prospect candidates

• Uncertainty estimation in seismic inversion using Invertible Neural Networks

• High Dimensional Bayesian Optimization with Reinforced Transformer Deep Kernels for industrial and scientific applications

• Toward Polar Sea-Ice Classification using Color-based Segmentation and Auto-labeling of Sentinel-2 Imagery to Train an Efficient Deep Learning Model

• The Case for Decentralized AI metadata tracking and lineage in Science and Engineering Workflows

• Deep Reinforcement Learning Exploration in Continuous Latent Space for Molecular Design

• Predicting Flood Severity in Indonesia based on Historical Flooding Events and Time-Series Information

• Benefits of Multiobjective Learning in Solar Energy Prediction | Video Link

• FedMDP: A Federated Learning Framework to Handle System and Model Heterogeneity in Resource-constrained Environments

• Spatio-Temporal Super-Resolution of Dynamical Systems using Physics-Informed Deep-Learning