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Found 6 papers, 3 papers with code
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy
Deep Neural Networks (DNNs) are known to be strong predictors, but their prediction strategies can rarely be understood.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks
The field of video compression has developed some of the most sophisticated and efficient compression algorithms known in the literature, enabling very high compressibility for little loss of information.
Finding and Removing Clever Hans: Using Explanation Methods to Debug and Improve Deep Models
Based on a recent technique - Spectral Relevance Analysis - we propose the following technical contributions and resulting findings: (a) a scalable quantification of artifactual and poisoned classes where the machine learning models under study exhibit CH behavior, (b) several approaches denoted as Class Artifact Compensation (ClArC), which are able to effectively and significantly reduce a model's CH behavior.
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression
We present DeepCABAC, a novel context-adaptive binary arithmetic coder for compressing deep neural networks.
Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements
Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV-2 pandemic.
A Privacy Preserving System for Movie Recommendations Using Federated Learning
A recent distributed learning scheme called federated learning has made it possible to learn from personal user data without its central collection.
