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Found 12 papers, 6 papers with code
Conformal Drug Property Prediction with Density Estimation under Covariate Shift
Conformal Prediction (CP) is a promising tool for creating such prediction sets for molecular properties with a coverage guarantee.
Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models
Large language models (LLMs) specializing in natural language generation (NLG) have recently started exhibiting promising capabilities across a variety of domains.
Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control
We focus on a typical scenario where such requirements, separately encoding $\textit{value}$ and $\textit{cost}$, compete with each other.
Conformal Prediction Intervals with Temporal Dependence
We focus on the task of constructing valid prediction intervals (PIs) in time series regression with a cross-section.
Conformal Prediction with Temporal Quantile Adjustments
TQA adjusts the quantile to query in CP at each time $t$, accounting for both cross-sectional and longitudinal coverage in a theoretically-grounded manner.
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks
Most existing calibration methods either lack theoretical guarantees for producing calibrated outputs, reduce classification accuracy in the process, or only calibrate the predicted class.
Locally Valid and Discriminative Prediction Intervals for Deep Learning Models
Moreover, when combined with deep learning (DL) methods, it should be scalable and affect the DL model performance minimally.
SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models
Despite deep learning (DL) success in classification problems, DL classifiers do not provide a sound mechanism to decide when to refrain from predicting.
DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning
We present a comparison between two methods of cluster identification: the standard Matched Filter (MF) method in SZ cluster finding and a method using Convolutional Neural Networks (CNN).
In-Place Zero-Space Memory Protection for CNN
Convolutional Neural Networks (CNN) are being actively explored for safety-critical applications such as autonomous vehicles and aerospace, where it is essential to ensure the reliability of inference results in the presence of possible memory faults.
Clebsch–Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network
Recent work by Cohen et al. has achieved state-of-the-art results for learning spherical images in a rotation invariant way by using ideas from group representation theory and noncommutative harmonic analysis.
Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network
Recent work by Cohen \emph{et al.} has achieved state-of-the-art results for learning spherical images in a rotation invariant way by using ideas from group representation theory and noncommutative harmonic analysis.
