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"Topic": models, code, and papers

Undecidability of Underfitting in Learning Algorithms

Feb 09, 2021
Sonia Sehra, David Flores, George D. Montanez

Using recent machine learning results that present an information-theoretic perspective on underfitting and overfitting, we prove that deciding whether an encodable learning algorithm will always underfit a dataset, even if given unlimited training time, is undecidable. We discuss the importance of this result and potential topics for further research, including information-theoretic and probabilistic strategies for bounding learning algorithm fit.

* Accepted at The 2nd International Conference on Computing and Data Science (CONF-CDS 2021) 

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Rat big, cat eaten! Ideas for a useful deep-agent protolanguage

Mar 17, 2020
Marco Baroni

Deep-agent communities developing their own language-like communication protocol are a hot (or at least warm) topic in AI. Such agents could be very useful in machine-machine and human-machine interaction scenarios long before they have evolved a protocol as complex as human language. Here, I propose a small set of priorities we should focus on, if we want to get as fast as possible to a stage where deep agents speak a useful protolanguage.

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CausalML: Python Package for Causal Machine Learning

Mar 02, 2020
Huigang Chen, Totte Harinen, Jeong-Yoon Lee, Mike Yung, Zhenyu Zhao

CausalML is a Python implementation of algorithms related to causal inference and machine learning. Algorithms combining causal inference and machine learning have been a trending topic in recent years. This package tries to bridge the gap between theoretical work on methodology and practical applications by making a collection of methods in this field available in Python. This paper introduces the key concepts, scope, and use cases of this package.

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New methods for SVM feature selection

May 24, 2019
Tangui Aladjidi, François Pasqualini

Support Vector Machines have been a popular topic for quite some time now, and as they develop, a need for new methods of feature selection arises. This work presents various approaches SVM feature selection developped using new tools such as entropy measurement and K-medoid clustering. The work focuses on the use of one-class SVM's for wafer testing, with a numerical implementation in R.

* 5 pages, 2 figures 

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Test Collections for Patent-to-Patent Retrieval and Patent Map Generation in NTCIR-4 Workshop

Apr 10, 2004
Atsushi Fujii, Makoto Iwayama, Noriko Kando

This paper describes the Patent Retrieval Task in the Fourth NTCIR Workshop, and the test collections produced in this task. We perform the invalidity search task, in which each participant group searches a patent collection for the patents that can invalidate the demand in an existing claim. We also perform the automatic patent map generation task, in which the patents associated with a specific topic are organized in a multi-dimensional matrix.

* Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC-2004), pp.1643-1646, May. 2004. 
* 4 pages, Proceedings of the 4th International Conference on Language Resources and Evaluation (to appear) 

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Combining Expression and Content in Domains for Dialog Managers

Aug 13, 1998
Bernd Ludwig, Guenther Goerz, Heinrich Niemann

We present work in progress on abstracting dialog managers from their domain in order to implement a dialog manager development tool which takes (among other data) a domain description as input and delivers a new dialog manager for the described domain as output. Thereby we will focus on two topics; firstly, the construction of domain descriptions with description logics and secondly, the interpretation of utterances in a given domain.

* Proceedings of DL '98, pp. 126-130, Trento, Italy 
* 5 pages, uses conference.sty 

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Blind Image Deblurring: a Review

Jan 22, 2022
Zhengrong Xue

This is a review on blind image deblurring. First, we formulate the blind image deblurring problem and explain why it is challenging. Next, we bring some psychological and cognitive studies on the way our human vision system deblurs. Then, relying on several previous reviews, we discuss the topic of metrics and datasets, which is non-trivial to blind deblurring. Finally, we introduce some typical optimization-based methods and learning-based methods.

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Neural networks for option pricing and hedging: a literature review

Nov 13, 2019
Johannes Ruf, Weiguan Wang

Neural networks have been used as a nonparametric method for option pricing and hedging since the early 1990s. Far over a hundred papers have been published on this topic. This note intends to provide a comprehensive review. Papers are compared in terms of input features, output variables, benchmark models, performance measures, data partition methods, and underlying assets. Furthermore, related work and regularisation techniques are discussed.

* 30 pages, 5 tables 

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Extraction of Salient Sentences from Labelled Documents

Feb 28, 2015
Misha Denil, Alban Demiraj, Nando de Freitas

We present a hierarchical convolutional document model with an architecture designed to support introspection of the document structure. Using this model, we show how to use visualisation techniques from the computer vision literature to identify and extract topic-relevant sentences. We also introduce a new scalable evaluation technique for automatic sentence extraction systems that avoids the need for time consuming human annotation of validation data.

* arXiv admin note: substantial text overlap with arXiv:1406.3830 

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