18 citations · 20 across the 6 of their papers we have counts for
7 papers
A Closer Look at Benchmarking Self-Supervised Pre-training with Image Classification
Markus Marks, Manuel Knott, Neehar Kondapaneni +4
Self-supervised learning (SSL) is a machine learning approach where the data itself provides supervision, eliminating the need for external labels. The model is forced to learn abo…
Counterfactual Explanations for Deep Learning-Based Traffic Forecasting
Rushan Wang, Yanan Xin, Yatao Zhang +2
Deep learning models are widely used in traffic forecasting and have achieved state-of-the-art prediction accuracy. However, the black-box nature of those models makes the results…
Synthetic location trajectory generation using categorical diffusion models
Simon Dirmeier, Ye Hong, Fernando Perez-Cruz
Diffusion probabilistic models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data, for instance, for computer vision, a…
Anchor Data Augmentation
Nora Schneider, Shirin Goshtasbpour, Fernando Perez-Cruz
We propose a novel algorithm for data augmentation in nonlinear over-parametrized regression. Our data augmentation algorithm borrows from the literature on causality and extends t…
Diffusion models for probabilistic programming
Simon Dirmeier, Fernando Perez-Cruz
We propose Diffusion Model Variational Inference (DMVI), a novel method for automated approximate inference in probabilistic programming languages (PPLs). DMVI utilizes diffusion m…
A causal intervention framework for synthesizing mobility data and evaluating predictive neural networks
Ye Hong, Yanan Xin, Simon Dirmeier +2
Deep neural networks are increasingly utilized in mobility prediction tasks, yet their intricate internal workings pose challenges for interpretability, especially in comprehending…