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Found 8 papers, 2 papers with code
Training Private Models That Know What They Don't Know
Training reliable deep learning models which avoid making overconfident but incorrect predictions is a longstanding challenge.
$p$-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations
We introduce $p$-DkNN, a novel inference procedure that takes a trained deep neural network and analyzes the similarity structures of its intermediate hidden representations to compute $p$-values associated with the end-to-end model prediction.
Intrinsic Anomaly Detection for Multi-Variate Time Series
We introduce a novel, practically relevant variation of the anomaly detection problem in multi-variate time series: intrinsic anomaly detection.
Selective Classification Via Neural Network Training Dynamics
Selective classification is the task of rejecting inputs a model would predict incorrectly on through a trade-off between input space coverage and model accuracy.
Context-invariant, multi-variate time series representations
Modern time series corpora, in particular those coming from sensor-based data, exhibit characteristics that have so far not been adequately addressed in the literature on representation learning for time series.
The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models
In particular, we investigate the effectiveness of several forms of data binning, i. e. converting real-valued time series into categorical ones, when combined with feed-forward, recurrent neural networks, and convolution-based sequence models.
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
We might hope that when faced with unexpected inputs, well-designed software systems would fire off warnings.
Introduction to Tensor Decompositions and their Applications in Machine Learning
Tensors are multidimensional arrays of numerical values and therefore generalize matrices to multiple dimensions.
