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Found 7 papers, 3 papers with code
Scaling Experiments in Self-Supervised Cross-Table Representation Learning
To analyze the scaling potential of deep tabular representation learning models, we introduce a novel Transformer-based architecture specifically tailored to tabular data and cross-table representation learning by utilizing table-specific tokenizers and a shared Transformer backbone.
Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models
Generalized Additive Models (GAMs) have recently experienced a resurgence in popularity due to their interpretability, which arises from expressing the target value as a sum of non-linear transformations of the features.
Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management
In this work, we focus on developing dynamic inventory ordering policies for a multi-echelon, i. e. multi-stage, supply chain.
Towards Learning Self-Organized Criticality of Rydberg Atoms using Graph Neural Networks
Self-Organized Criticality (SOC) is a ubiquitous dynamical phenomenon believed to be responsible for the emergence of universal scale-invariant behavior in many, seemingly unrelated systems, such as forest fires, virus spreading or atomic excitation dynamics.
Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks
Specifically, we test generalization to meshes with different shapes and superposition of solutions for a different number of inhomogeneities.
Auto-Compressing Subset Pruning for Semantic Image Segmentation
We show a crucial interplay between providing a high-capacity model at the beginning of training and the compression pressure forcing the model to compress concepts into retained channels.
Optimizing Variational Quantum Circuits using Evolution Strategies
This version withdrawn by arXiv administrators because the submitter did not have the right to agree to our license at the time of submission.
