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Found 6 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.
Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentation
The STEGO method for unsupervised semantic segmentation contrastively distills feature correspondences of a DINO-pre-trained Vision Transformer and recently set a new state of the art.
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.
Spectral Reconstruction and Disparity from Spatio-Spectrally Coded Light Fields via Multi-Task Deep Learning
In this application, the spectrally coded light field camera can be interpreted as a single-shot spectral depth camera.
Microlens array grid estimation, light field decoding, and calibration
To quantify the performance of the algorithms, we propose an evaluation pipeline utilizing application-specific ray-traced white images with known microlens positions.
