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Found 6 papers, 1 papers with code
AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model
Interpretable deep learning aims at developing neural architectures whose decision-making processes could be understood by their users.
Causal Concept Embedding Models: Beyond Causal Opacity in Deep Learning
Causal opacity denotes the difficulty in understanding the "hidden" causal structure underlying a deep neural network's (DNN) reasoning.
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
Federated Learning (FL), a privacy-aware approach in distributed deep learning environments, enables many clients to collaboratively train a model without sharing sensitive data, thereby reducing privacy risks.
Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability
Anomaly detection (AD), also referred to as outlier detection, is a statistical process aimed at identifying observations within a dataset that significantly deviate from the expected pattern of the majority of the data.
Climbing the Ladder of Interpretability with Counterfactual Concept Bottleneck Models
"), and imagine alternative scenarios that could result in different predictions (the "What if?").
Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques
When models are calibrated, the MAE decreases to 7. 72 mmHg for SBP, 9. 45 mmHg for DBP and 8. 13 mmHg for MAP.
