Pith. sign in

REVIEW 1 cited by

TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning

Not yet reviewed by Pith; the record is open.

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 1612.04251 v1 pith:6MOLMATL submitted 2016-12-13 cs.DC cs.LG

classification cs.DCcs.LG
keywords learningmachinehigh-levellearnmoduletensorflowdistributedalgorithms
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

TF.Learn is a high-level Python module for distributed machine learning inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to simplify the process of creating, configuring, training, evaluating, and experimenting a machine learning model. TF.Learn integrates a wide range of state-of-art machine learning algorithms built on top of TensorFlow's low level APIs for small to large-scale supervised and unsupervised problems. This module focuses on bringing machine learning to non-specialists using a general-purpose high-level language as well as researchers who want to implement, benchmark, and compare their new methods in a structured environment. Emphasis is put on ease of use, performance, documentation, and API consistency.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning

    cs.MM 2019-08 conditional novelty 6.0 of 10

    Comyco trains an ABR policy by imitating an oracle solver's actions computed with future network knowledge and VMAF-based QoE, achieving 1700x fewer samples and 7.5-16.79% higher QoE than baselines.

Pith tools