107 citations · 469 across the 35 of their papers we have counts for
6 papers · 1 filter
Early-Exit Neural Networks with Nested Prediction Sets
Metod Jazbec, Patrick Forré, Stephan Mandt +2
Early-exit neural networks (EENNs) enable adaptive and efficient inference by providing predictions at multiple stages during the forward pass. In safety-critical applications, the…
Transferring climate change physical knowledge
Francesco Immorlano, Veronika Eyring, Thomas le Monnier de Gouville +5
Precise and reliable climate projections are required for climate adaptation and mitigation, but Earth system models still exhibit great uncertainties. Several approaches have been…
Understanding Pathologies of Deep Heteroskedastic Regression
Eliot Wong-Toi, Alex Boyd, Vincent Fortuin +1
Deep, overparameterized regression models are notorious for their tendency to overfit. This problem is exacerbated in heteroskedastic models, which predict both mean and residual n…
Deep Anomaly Detection on Tennessee Eastman Process Data
Fabian Hartung, Billy Joe Franks, Tobias Michels +15
This paper provides the first comprehensive evaluation and analysis of modern (deep-learning) unsupervised anomaly detection methods for chemical process data. We focus on the Tenn…
Zero-Shot Anomaly Detection via Batch Normalization
Aodong Li, Chen Qiu, Marius Kloft +3
Anomaly detection (AD) plays a crucial role in many safety-critical application domains. The challenge of adapting an anomaly detector to drift in the normal data distribution, esp…
Deep Anomaly Detection under Labeling Budget Constraints
Aodong Li, Chen Qiu, Marius Kloft +3
Selecting informative data points for expert feedback can significantly improve the performance of anomaly detection (AD) in various contexts, such as medical diagnostics or fraud…