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"Recommendation": models, code, and papers

Collaborative Filtering with Recurrent Neural Networks

Jan 03, 2017
Robin Devooght, Hugues Bersini

We show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach. In particular we study how the long short-term memory (LSTM) can be applied to collaborative filtering, and how it compares to standard nearest neighbors and matrix factorization methods on movie recommendation. We show that the LSTM is competitive in all aspects, and largely outperforms other methods in terms of item coverage and short term predictions.


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Perspectives on individual animal identification from biology and computer vision

Feb 28, 2021
Maxime Vidal, Nathan Wolf, Beth Rosenberg, Bradley P. Harris, Alexander Mathis

Identifying individual animals is crucial for many biological investigations. In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. Here, we review current advances of computer vision identification techniques to provide both computer scientists and biologists with an overview of the available tools and discuss their applications. We conclude by offering recommendations for starting an animal identification project, illustrate current limitations and propose how they might be addressed in the future.

* 12 pages, 1 figure, 2 boxes and 1 table 

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Lightweight Residual Network for The Classification of Thyroid Nodules

Nov 16, 2019
Ponugoti Nikhila, Sabari Nathan, Elmer Jeto Gomes Ataide, Alfredo Illanes, Dr. Michael Friebe, Srichandana Abbineni

Ultrasound is a useful technique for diagnosing thyroid nodules. Benign and malignant nodules that automatically discriminate in the ultrasound pictures can provide diagnostic recommendations or, improve diagnostic accuracy in the absence of specialists. The main issue here is how to collect suitable features for this particular task. We suggest here a technique for extracting features from ultrasound pictures based on the Residual U-net. We attempt to introduce significant semantic characteristics to the classification. Our model gained 95% classification accuracy.

* 1 Page , 1 Figure , IEEE EMBS 

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Multiview Variational Graph Autoencoders for Canonical Correlation Analysis

Oct 30, 2020
Yacouba Kaloga, Pierre Borgnat, Sundeep Prabhakar Chepuri, Patrice Abry, Amaury Habrard

We present a novel multiview canonical correlation analysis model based on a variational approach. This is the first nonlinear model that takes into account the available graph-based geometric constraints while being scalable for processing large scale datasets with multiple views. It is based on an autoencoder architecture with graph convolutional neural network layers. We experiment with our approach on classification, clustering, and recommendation tasks on real datasets. The algorithm is competitive with state-of-the-art multiview representation learning techniques.

* 4 pages, 3 figures, submitted 

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Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions

Jul 13, 2020
Stephen Roller, Y-Lan Boureau, Jason Weston, Antoine Bordes, Emily Dinan, Angela Fan, David Gunning, Da Ju, Margaret Li, Spencer Poff, Pratik Ringshia, Kurt Shuster, Eric Michael Smith, Arthur Szlam, Jack Urbanek, Mary Williamson

We present our view of what is necessary to build an engaging open-domain conversational agent: covering the qualities of such an agent, the pieces of the puzzle that have been built so far, and the gaping holes we have not filled yet. We present a biased view, focusing on work done by our own group, while citing related work in each area. In particular, we discuss in detail the properties of continual learning, providing engaging content, and being well-behaved -- and how to measure success in providing them. We end with a discussion of our experience and learnings, and our recommendations to the community.


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Building Safer AGI by introducing Artificial Stupidity

Aug 11, 2018
Michaël Trazzi, Roman V. Yampolskiy

Artificial Intelligence (AI) achieved super-human performance in a broad variety of domains. We say that an AI is made Artificially Stupid on a task when some limitations are deliberately introduced to match a human's ability to do the task. An Artificial General Intelligence (AGI) can be made safer by limiting its computing power and memory, or by introducing Artificial Stupidity on certain tasks. We survey human intellectual limits and give recommendations for which limits to implement in order to build a safe AGI.


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Towards Semantic Modeling of Contradictions and Disagreements: A Case Study of Medical Guidelines

Aug 02, 2017
Wlodek Zadrozny, Hossein Hematialam, Luciana Garbayo

We introduce a formal distinction between contradictions and disagreements in natural language texts, motivated by the need to formally reason about contradictory medical guidelines. This is a novel and potentially very useful distinction, and has not been discussed so far in NLP and logic. We also describe a NLP system capable of automated finding contradictory medical guidelines; the system uses a combination of text analysis and information retrieval modules. We also report positive evaluation results on a small corpus of contradictory medical recommendations.

* 5 pages, 1 figure, accepted at 12th International Conference on Computational Semantics (IWCS-2017) 

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On the relation between accuracy and fairness in binary classification

May 21, 2015
Indre Zliobaite

Our study revisits the problem of accuracy-fairness tradeoff in binary classification. We argue that comparison of non-discriminatory classifiers needs to account for different rates of positive predictions, otherwise conclusions about performance may be misleading, because accuracy and discrimination of naive baselines on the same dataset vary with different rates of positive predictions. We provide methodological recommendations for sound comparison of non-discriminatory classifiers, and present a brief theoretical and empirical analysis of tradeoffs between accuracy and non-discrimination.

* Accepted for presentation to the 2nd workshop on Fairness, Accountability, and Transparency in Machine Learning (http://www.fatml.org/

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Automatically Restructuring Practice Guidelines using the GEM DTD

Jun 08, 2007
Amanda Bouffier, Thierry Poibeau

This paper describes a system capable of semi-automatically filling an XML template from free texts in the clinical domain (practice guidelines). The XML template includes semantic information not explicitly encoded in the text (pairs of conditions and actions/recommendations). Therefore, there is a need to compute the exact scope of conditions over text sequences expressing the required actions. We present a system developed for this task. We show that it yields good performance when applied to the analysis of French practice guidelines.

* Proceedings of Biomedical Natural Language Processing (BioNLP) (2007) - 

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