natural-language-processing-7

Specificity

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in specificity-7

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Dataset

Best Model

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Libraries

i

Use these libraries to find specificity-7 models and implementations

yu-group/imodels-experiments

2 papers 15

Datasets

MACSum

Subtasks

No subtasks available.

Most implemented papers

Personalizing Dialogue Agents: I have a dog, do you have pets too?

Arthur Szlam, Douwe Kiela, Saizheng Zhang, J. Weston, Emily Dinan, Jack Urbanek•Sun Jan 21 2018

This work collects data and train models tocondition on their given profile information; and information about the person they are talking to, resulting in improved dialogues, as measured by next utterance prediction.

1631

Content

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Cellular automata as convolutional neural networks

W. Gilpin•Sat Sep 08 2018

This work shows that any CA may readily be represented using a convolutional neural network with a network-in-network architecture, and investigates how the trained networks internally represent the CA rules using an information-theoretic technique based on distributions of layer activation patterns.

111 0

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Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography

Li Shen•Mon Dec 31 2018

A deep learning algorithm that can accurately detect breast cancer on screening mammograms using an "end-to-end" training approach that leverages training datasets with either complete clinical annotation or only the cancer status of the whole image, eliminating the reliance on rarely available lesion annotations.

9 0

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Predicting Splicing from Primary Sequence with Deep Learning

Kishore Jaganathan, Sofia Panagiotopoulou, J. McRae, S. Darbandi, David G. Knowles, Yang I. Li, J. Kosmicki, Juan Arbelaez, Wenwu Cui, Grace B. Schwartz, E. Chow, E. Kanterakis, Hong Gao, Amirali Kia, S. Batzoglou, Stephan J Sanders, K. Farh•Mon Dec 31 2018

A deep neural network is described that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing.

2020 0

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POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)

Jannis Born, G. Brändle, M. Cossio, Marion Disdier, Julie Goulet, J. Roulin, Nina Wiedemann•Fri Apr 24 2020

A more prominent role of point-of-care ultrasound imaging to guide COVID-19 detection is advocated, and an open-access web service is provided that deploys the predictive model, allowing to perform predictions on ultrasound lung images.

160 0

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Neural vector spaces for unsupervised information retrieval

Van Gysel, de Rijke•Sun Dec 31 2017

This dissertation aims to provide a history of web exceptionalism from 1989 to 2002, a period chosen in order to explore its roots as well as specific cases up to and including the year in which descriptions of “Web 2.0” began to circulate.

77 0

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A Tsetlin Machine with Multigranular Clauses

Ole-Christoffer Granmo, M. G. Olsen, S. Gorji, Adrian Phoulady•Sun Sep 15 2019

The Multigranular Tsetlin Machine (MTM) is introduced, which eliminates the specificity hyperparameter, used by the TM to control the granularity of the conjunctive clauses that it produces for recognizing patterns, and encodes varying specificity as part of the clauses, rendering the clauses multigsranular.

22 0

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3D Convolutional Neural Networks for Stalled Brain Capillary Detection

Alexandr A Kalinin, R. Solovyev, Tatiana Gabruseva•Sat Apr 03 2021

This work describes a deep learning-based approach for automatic detection of stalled capillaries in brain images based on 3D convolutional neural networks that outperformed other methods and demonstrated state-of-the-art results.

73 0

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How transferable are features in deep neural networks?

J. Clune, Yoshua Bengio, J. Yosinski, Hod Lipson•Wed Nov 05 2014

This paper quantifies the generality versus specificity of neurons in each layer of a deep convolutional neural network and reports a few surprising results, including that initializing a network with transferred features from almost any number of layers can produce a boost to generalization that lingers even after fine-tuning to the target dataset.

8982 0

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