Search Results for author: Tanmoy Bhattacharya

Found 13 papers, 3 papers with code

An Effective Baseline for Robustness to Distributional Shift

1 code implementation15 May 2021 Sunil Thulasidasan, Sushil Thapa, Sayera Dhaubhadel, Gopinath Chennupati, Tanmoy Bhattacharya, Jeff Bilmes

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 20 Newsgroups (using extra training data)

Out-of-Distribution Detection Robust classification

Precision Nucleon Charges and Form Factors Using 2+1-flavor Lattice QCD

no code implementations9 Mar 2021 Sungwoo Park, Rajan Gupta, Boram Yoon, Santanu Mondal, Tanmoy Bhattacharya, Yong-Chull Jang, Bálint Joó, Frank Winter

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

Contribution of the QCD $Θ$-term to nucleon electric dipole moment

no code implementations18 Jan 2021 Tanmoy Bhattacharya, Vincenzo Cirigliano, Rajan Gupta, Emanuele Mereghetti, Boram Yoon

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

A Simple and Effective Baseline for Out-of-Distribution Detection using Abstention

no code implementations1 Jan 2021 Sunil Thulasidasan, Sushil Thapa, Sayera Dhaubhadel, Gopinath Chennupati, Tanmoy Bhattacharya, Jeff Bilmes

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.

Out-of-Distribution Detection Text Classification

Uncertainty Bounds for Multivariate Machine Learning Predictions on High-Strain Brittle Fracture

no code implementations23 Dec 2020 Cristina Garcia-Cardona, M. Giselle Fernández-Godino, Daniel O'Malley, Tanmoy Bhattacharya

Simulation of the crack network evolution on high strain rate impact experiments performed in brittle materials is very compute-intensive.

Qubit regularization of asymptotic freedom

no code implementations3 Dec 2020 Tanmoy Bhattacharya, Alexander J. Buser, Shailesh Chandrasekharan, Rajan Gupta, Hersh Singh

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

Nucleon Momentum Fraction, Helicity and Transversity from 2+1-flavor Lattice QCD

no code implementations24 Nov 2020 Santanu Mondal, Rajan Gupta, Sungwoo Park, Boram Yoon, Tanmoy Bhattacharya, Bálint Joó, Frank Winter

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

State preparation and measurement in a quantum simulation of the O(3) sigma model

no code implementations28 Jun 2020 Alexander J. Buser, Tanmoy Bhattacharya, Lukasz Cincio, Rajan Gupta

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

Combating Label Noise in Deep Learning Using Abstention

2 code implementations27 May 2019 Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff Bilmes, Gopinath Chennupati, Jamal Mohd-Yusof

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.

General Classification Image Classification +1

Knows When it Doesn’t Know: Deep Abstaining Classifiers

no code implementations ICLR 2019 Sunil Thulasidasan, Tanmoy Bhattacharya, Jeffrey Bilmes, Gopinath Chennupati, Jamal Mohd-Yusof

We introduce the deep abstaining classifier -- a deep neural network trained with a novel loss function that provides an abstention option during training.

Cannot find the paper you are looking for? You can Submit a new open access paper.