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Grammatical Error Correction (GEC) should not focus only on high accuracy of corrections but also on interpretability for language learning. However, existing neural-based GEC models mainly aim at improving accuracy, and their…

Computation and Language · Computer Science 2022-03-15 Masahiro Kaneko , Sho Takase , Ayana Niwa , Naoaki Okazaki

In various chemical systems enthalpy-entropy compensation (EEC) is a well-known rule of behavior, although the physical roots of it are still not completely understood. It has been frequently questioned whether EEC is a truly physical…

Statistical Mechanics · Physics 2015-06-04 E. B. Starikov , B. Norden

Counterfactual explanations (CEs) are a practical tool for demonstrating why machine learning classifiers make particular decisions. For CEs to be useful, it is important that they are easy for users to interpret. Existing methods for…

Machine Learning · Computer Science 2021-03-17 Lisa Schut , Oscar Key , Rory McGrath , Luca Costabello , Bogdan Sacaleanu , Medb Corcoran , Yarin Gal

Counterfactual explanations (CEs) offer a human-understandable way to explain decisions by identifying specific changes to the input parameters of a base or present model that would lead to a desired change in the outcome. For optimization…

Optimization and Control · Mathematics 2026-01-06 Felix Engelhardt , Jannis Kurtz , Ş. İlker Birbil , Ted Ralphs

Unfortunately, the article "A Comparative Study to Benchmark Cross-project Defect Prediction Approaches" has a problem in the statistical analysis which was pointed out almost immediately after the pre-print of the article appeared online.…

Software Engineering · Computer Science 2017-07-31 Steffen Herbold , Alexander Trautsch , Jens Grabowski

I have noted a number of errors, most of them quite minor, in the {\em Encyclopedia of Cosmology} (New York and London: Garland), 1993, Norriss S. Hetherington, ed. The majority occur in my mathematically verbose article, ``Fundamental…

Astrophysics · Physics 2007-05-23 Kevin Krisciunas

In this paper we present a heuristic method to provide individual explanations for those elements in a dataset (data points) which are wrongly predicted by a given classifier. Since the general case is too difficult, in the present work we…

Machine Learning · Computer Science 2023-02-21 Sheng Zhou , Pierre Blanchart , Michel Crucianu , Marin Ferecatu

While the expected calibration error (ECE), which employs binning, is widely adopted to evaluate the calibration performance of machine learning models, theoretical understanding of its estimation bias is limited. In this paper, we present…

Machine Learning · Computer Science 2025-05-27 Futoshi Futami , Masahiro Fujisawa

Manuscript astro-ph/0504486 is a clear example of the fundamental conceptual flaw that had persisted for the past 100 years. Namely, the failure to realize that mass-energy equivalence, as applied to a propagating light particle (photon),…

General Physics · Physics 2007-05-23 Ezzat G. Bakhoum

The repeated community-wide reuse of test sets in popular benchmark problems raises doubts about the credibility of reported test-error rates. Verifying whether a learned model is overfitted to a test set is challenging as independent test…

Machine Learning · Computer Science 2019-11-15 Roman Werpachowski , András György , Csaba Szepesvári

It is well known that mean-field theories fail to reproduce the experimentally known critical exponents. The traditional argument which explain this failure of mean-field theories near a critical point is the Ginsburg criterion in which…

Statistical Mechanics · Physics 2014-03-05 Navinder Singh

This paper has been withdrawn by the author because there are some typos in proofs.

Commutative Algebra · Mathematics 2013-07-30 M. J. Nikmehr , F. Heydari

The paper entitled ``Against Many-Worlds Interpretations'' by A. Kent, which has recently been submitted to the e-Print archive (gr-qc/9703089) contained some misconceptions. The claims on Everett's many-worlds interpretation are quoted and…

General Relativity and Quantum Cosmology · Physics 2007-05-23 Toshifumi Sakaguchi

Loss functions play a central role in supervised classification. Cross-entropy (CE) is widely used, whereas the mean absolute error (MAE) loss can offer robustness but is difficult to optimize. Interpolating between the CE and MAE losses,…

Machine Learning · Statistics 2026-04-29 Kartheek Bondugula , Santiago Mazuelas , Aritz Pérez , Anqi Liu

A form of generalisation error known as Off Training Set (OTS) error was recently introduced in [Wolpert, 1996b], along with a theorem showing that small training set error does not guarantee small OTS error, unless assumptions are made…

Machine Learning · Computer Science 2019-12-13 Jonathan Baxter

This article discusses several erroneous claims which appear in textbooks on numerical methods and computational physics. These are not typos or mistakes an individual author has made, but widespread misconceptions. In an attempt to stop…

Physics Education · Physics 2020-12-25 Alexandros Gezerlis , Martin Williams

Data-sparse settings such as robotic manipulation, molecular physics, and galaxy morphology classification are some of the hardest domains for deep learning. For these problems, equivariant networks can help improve modeling across…

Machine Learning · Computer Science 2026-01-30 Edward Berman , Jacob Ginesin , Marco Pacini , Robin Walters

We address the issue of the fallacy of the gauge concept in electromagnetism. The role of groupthink in the perpetuation of this concept in the physics literature is also discussed. Brief, elementary arguments suffice to demonstrate the…

General Physics · Physics 2014-12-09 Paul J Cote , Mark A Johnson

There is a gap in Theorem 2.2 of the paper of Du (\cite{D_2010}). In this paper, we shall state the gap and repair it.

Functional Analysis · Mathematics 2011-02-14 Thabet Abdeljawad , Erdal Karapınar

This article corrects two mistakes in the article "Coarse homology theories" [math.AT/0106183].

Algebraic Topology · Mathematics 2014-10-01 Paul D. Mitchener