Pith. sign in

REVIEW 5 cited by

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1302.4389 v4 pith:RLXFHEDX submitted 2013-02-18 stat.ML cs.LG

Maxout Networks

Ian J. Goodfellow , David Warde-Farley , Mehdi Mirza , Aaron Courville , Yoshua Bengio This is my paper
classification stat.ML cs.LG
keywords dropoutmodelmaxoutapproximateaveragingbecausecalledtechnique
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout. We define a simple new model called maxout (so named because its output is the max of a set of inputs, and because it is a natural companion to dropout) designed to both facilitate optimization by dropout and improve the accuracy of dropout's fast approximate model averaging technique. We empirically verify that the model successfully accomplishes both of these tasks. We use maxout and dropout to demonstrate state of the art classification performance on four benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Deep Residual Learning for Image Recognition

    cs.CV 2015-12 accept novelty 8.0

    Residual networks reformulate layers to learn residual functions, enabling effective training of up to 152-layer models that achieve 3.57% error on ImageNet and win ILSVRC 2015.

  2. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

    cs.LG 2015-11 accept novelty 8.0

    DCGANs with architectural constraints learn a hierarchy of representations from object parts to scenes in both generator and discriminator across image datasets.

  3. Multiple-Identity Image Attacks Against Face-based Identity Verification

    cs.CV 2019-06 unverdicted novelty 6.0

    The paper shows that multiple-identity image attacks succeed due to modest angular separation between matching (~90°) and non-matching (40-60°) face representations, with image morphing and representation inversion re...

  4. Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning

    cs.LG 2019-07 unverdicted novelty 5.0

    Graph Laplacian interpolating activation replaces softmax in DNNs and improves natural accuracy, robust accuracy, and data efficiency.

  5. Machine Reading Comprehension: a Literature Review

    cs.CL 2019-06 unverdicted novelty 1.0

    A 2019 survey of machine reading comprehension corpora and methods.