Search Results for author:
Found 13 papers, 3 papers with code
Combating Label Noise in Deep Learning Using Abstention
In the case of unstructured (arbitrary) label noise, abstention during training enables the DAC to be used as an effective data cleaner by identifying samples that are likely to have label noise.
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
In this work, we discuss a hitherto untouched aspect of mixup training -- the calibration and predictive uncertainty of models trained with mixup.
Ranked #1 on
Out-of-Distribution Detection
on STL-10
An Effective Baseline for Robustness to Distributional Shift
In this work we present a simple, but highly effective approach to deal with out-of-distribution detection that uses the principle of abstention: when encountering a sample from an unseen class, the desired behavior is to abstain from predicting.
Ranked #1 on
Out-of-Distribution Detection
on CIFAR-100
(using extra training data)
Knows When it Doesn’t Know: Deep Abstaining Classifiers
We introduce the deep abstaining classifier -- a deep neural network trained with a novel loss function that provides an abstention option during training.
Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports
We demonstrate an abstaining classifier in a multitask setting for classifying cancer pathology reports from the NCI SEER cancer registries on six tasks of interest.
A Simple and Effective Baseline for Out-of-Distribution Detection using Abstention
In this work we present a simple, but highly effective approach to deal with out-of-distribution detection that uses the principle of abstention: when encountering a sample from an unseen class, the desired behavior is to abstain from predicting.
Nucleon Momentum Fraction, Helicity and Transversity from 2+1-flavor Lattice QCD
Our final results, in the $\overline{\rm MS}$ scheme at 2 GeV, are $\langle x \rangle_{u-d} = 0. 160(16)(20)$, $\langle x \rangle_{\Delta u-\Delta d} = 0. 192(13)(20)$ and $\langle x \rangle_{\delta u-\delta d} = 0. 215(17)(20)$, where the first error is the overall analysis uncertainty assuming excited-state contributions have been removed, and the second is an additional systematic uncertainty due to possible residual excited-state contributions.
High Energy Physics - Lattice
Qubit regularization of asymptotic freedom
We provide strong evidence that the asymptotically free (1+1)-dimensional non-linear O(3) sigma model can be regularized using a quantum lattice Hamiltonian, referred to as the "Heisenberg-comb", that acts on a Hilbert space with only two qubits per spatial lattice site.
High Energy Physics - Lattice Strongly Correlated Electrons High Energy Physics - Theory Nuclear Theory Quantum Physics
State preparation and measurement in a quantum simulation of the O(3) sigma model
Recently, Singh and Chandrasekharan showed that fixed points of the non-linear O(3) sigma model can be reproduced near a quantum phase transition of a spin model with just two qubits per lattice site.
Quantum Physics High Energy Physics - Lattice
Uncertainty Bounds for Multivariate Machine Learning Predictions on High-Strain Brittle Fracture
Simulation of the crack network evolution on high strain rate impact experiments performed in brittle materials is very compute-intensive.
Contribution of the QCD $Θ$-term to nucleon electric dipole moment
Using the excited state spectrum from fits to the two-point function, we find $d_n^\Theta$ is small, $|d_n^\Theta| \lesssim 0. 01 \overline \Theta e$ fm, whereas for the proton we get $|d_p^\Theta| \sim 0. 02 \overline \Theta e$ fm.
High Energy Physics - Lattice High Energy Physics - Phenomenology
Precision Nucleon Charges and Form Factors Using 2+1-flavor Lattice QCD
Similarly, we find evidence that the $N\pi\pi $ excited state contributes to the correlation functions with the vector current, consistent with the vector meson dominance model.
High Energy Physics - Lattice High Energy Physics - Phenomenology
