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Found 7 papers, 4 papers with code
Neural Fields with Thermal Activations for Arbitrary-Scale Super-Resolution
We present a novel way to design neural fields such that points can be queried with an adaptive Gaussian PSF, so as to guarantee correct anti-aliasing at any desired output resolution.
Certified Data Removal in Sum-Product Networks
Data protection regulations like the GDPR or the California Consumer Privacy Act give users more control over the data that is collected about them.
Evaluating Machine Unlearning via Epistemic Uncertainty
There has been a growing interest in Machine Unlearning recently, primarily due to legal requirements such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act.
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
We show that the idea can be extended to uncertainty quantification: by modulating the network activations of a single deep network with FiLM, one obtains a model ensemble with high diversity, and consequently well-calibrated estimates of epistemic uncertainty, with low computational overhead in comparison.
Learning Graph Regularisation for Guided Super-Resolution
With the decision to employ the source as a constraint rather than only as an input to the prediction, our method differs from state-of-the-art deep architectures for guided super-resolution, which produce targets that, when downsampled, will only approximately reproduce the source.
Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Deep Ensembles
To demonstrate scalability, we provide Norway-wide maps for the five forest structure variables.
Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions
Computer-aided drug discovery is an essential component of modern drug development.
