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LM-IGTD: a 2D image generator for low-dimensional and mixed-type tabular data to leverage the potential of convolutional neural networks arxiv.org/abs/2406.14566 .CV .AI

DragPoser: Motion Reconstruction from Variable Sparse Tracking Signals via Latent Space Optimization arxiv.org/abs/2406.14567 .GR .AI .CV

PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models arxiv.org/abs/2406.14571 .AR .AI .LG

CMDS: Cross-layer Dataflow Optimization for DNN Accelerators Exploiting Multi-bank Memories arxiv.org/abs/2406.14574 .AR .DC

Faster Metallic Surface Defect Detection Using Deep Learning with Channel Shuffling arxiv.org/abs/2406.14582 .CV

Modeling & Evaluating the Performance of Convolutional Neural Networks for Classifying Steel Surface Defects arxiv.org/abs/2406.14583 .CV

Proceedings of the 13th edition of the conference on Random Generation of Combinatorial Structures. Polyominoes and Tilings arxiv.org/abs/2406.14588 .DM .DS

Physics-informed neural networks for parameter learning of wildfire spreading arxiv.org/abs/2406.14591 .LG .CE

Leveraging Pedagogical Theories to Understand Student Learning Process with Graph-based Reasonable Knowledge Tracing arxiv.org/abs/2406.12896 .AI .CY .LG

Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainability arxiv.org/abs/2406.12897 .LG .AI .CV

Factor Graph Optimization of Error-Correcting Codes for Belief Propagation Decoding arxiv.org/abs/2406.12900 .IT .IT .AI .LG

Can AI Beat Undergraduates in Entry-level Java Assignments? Benchmarking Large Language Models on JavaBench arxiv.org/abs/2406.12902 .LG .AI .PL .SE

Meent: Differentiable Electromagnetic Simulator for Machine Learning arxiv.org/abs/2406.12904 .comp-ph .optics .LG

PufferLib: Making Reinforcement Learning Libraries and Environments Play Nice arxiv.org/abs/2406.12905 .LG .AI .MA

Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lens arxiv.org/abs/2406.12908 .ME .ML .LG .AI

Scalable Training of Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN arxiv.org/abs/2406.12909 .comp-ph .LG

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