Official Implementation of the paper "A U-Net Based Discriminator for Generative Adversarial Networks" (CVPR 2020)
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Updated
Apr 14, 2022 - Python
Official Implementation of the paper "A U-Net Based Discriminator for Generative Adversarial Networks" (CVPR 2020)
Code of the paper 'Neural Transformation Learning for Anomaly Detection' published in ICML 2021
Supplementary source code for the ECRTS 2019 paper 'Response-Time Analysis of ROS 2 Processing Chains under Reservation-Based Scheduling'
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
Coder of the paper 'Latent Outlier Exposure for Anomaly Detectin with Contaminated Data' published in ICML 2022
Companion code for the self-supervised anomaly detection algorithm proposed in the paper "Detecting Anomalies within Time Series using Local Neural Transformations" by Tim Schneider et al.
Meta-Learning of Neural Architectures for Few-Shot Learning
Code base for physics-based photorealistic rendering within the scope of Bosch BCAI AMIRA probject
Source code for Fathony, Sahu, Willmott, & Kolter, "Multiplicative Filter Networks", ICLR 2021.
Code accompanying Coling2020 publication on data augmentation for named entity recognition
Code of the paper 'Raising the Bar in Graph-level Anomaly Detection' published in IJCAI-2022
[CVPR 2022] What Matters For Meta-Learning Vision Regression Tasks?
A module for the network simulator ns-3 to allow simulations for Compute-First Networks and in-network compute systems.
Integration of selected post-quantum schemes into the embedded TLS library wolfSSL as part of our paper "Mixed Certificate Chains for the Transition to Post-Quantum Authentication in TLS 1.3"
Implementation of the paper "Understanding anomaly detection with deep invertible networks through hierarchies of distributions and features" (NeurIPS 2020)
Simultaneous task allocation and motion scheduling (STAAMS) solver based on constraint programming and optimization, implemented for the Robot Operating System (ROS)
Implementation of the PAC Bayesian GP learning method.
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