proceedings.mlr.press

Registriran

Posnetek zaslona spletnega mesta

Datum posodobitve analize strani: 2026/03/31 21:14:56

Zadnji datum posodobitve whois: 2026/06/08 14:19:16

Status domene

Registriran

Plačano do

14.05.2027

Na voljo od

13.06.2027

SEO ocena

65.07%

Rezultat dosežen

146

Največji rezultat

Glavne informacije

ℹ️

Naslov: Proceedings of Machine Learning Research | The Proceedings of Machine Learning Research (formerly JMLR Workshop and Conference Proceedings) is a series aimed specifically at publishing machine learning research presented at workshops and conferences. Each volume is separately titled and associated with a particular workshop or conference. Volumes are published online on the PMLR web site. The Series Editors are Neil D. Lawrence and Mark Reid.

Opis: The Proceedings of Machine Learning Research (formerly JMLR Workshop and Conference Proceedings) is a series aimed specifically at publishing machine learning research presented at workshops and conferences. Each volume is separately titled and associated with a particular workshop or conference. Volumes are published online on the PMLR web site. The Series Editors are Neil D. Lawrence and Mark Reid.

Ključne besede: unknown

Kodiranje strani: utf-8

Velikost datoteke strani: 47 KB

Informacije o strežniku

🖥️

IP: 185.199.108.153

Lokacija: United States,US,,,37.751,-97.822,America/Chicago

Http strežnik: github.com

Kodiranje: utf-8

Seznam metaoznak

🏷️

charset: utf-8
equiv: X-UA-Compatible content: IE=edge
name: viewport content: width=device-width, initial-scale=1
name: generator content: Jekyll v3.10.0
property: og:title content: Proceedings of Machine Learning Research
property: og:locale content: en_US
name: description content: The Proceedings of Machine Learning Research (formerly JMLR Workshop and Conference Proceedings) is a series aimed specifically at publishing machine learning research presented at workshops and conferences. Each volume is separately titled and associated with a particular workshop or conference. Volumes are published online on the PMLR web site. The Series Editors are Neil D. Lawrence and Mark Reid.
property: og:description content: The Proceedings of Machine Learning Research (formerly JMLR Workshop and Conference Proceedings) is a series aimed specifically at publishing machine learning research presented at workshops and conferences. Each volume is separately titled and associated with a particular workshop or conference. Volumes are published online on the PMLR web site. The Series Editors are Neil D. Lawrence and Mark Reid.
property: og:url content: https://proceedings.mlr.press/
property: og:site_name content: Proceedings of Machine Learning Research
property: og:type content: website
name: twitter:card content: summary
property: twitter:title content: Proceedings of Machine Learning Research

Notranje povezave

🔗

Zunanje povezave

🌐

Whois informacije

📄

domain_name: mlr.press

update_date: 2026-04-19T08:45:51.004Z

update_time: 1776588351

creation_date: 2015-05-14T09:24:40.000Z

creation_time: 1431595480

expiration_date: 2027-05-14T23:59:59.000Z

Surovi podatki Whois

📋

            Domain Name: MLR.PRESS
Registrar: NameCheap, Inc.
Domain Status: client transfer prohibited
Creation Date: 2015-05-14T09:24:40.000Z
Registry Expiry Date: 2027-05-14T23:59:59.000Z
Updated Date: 2026-04-19T08:45:51.004Z
Name Server: DNS1.REGISTRAR-SERVERS.COM
Name Server: DNS2.REGISTRAR-SERVERS.COM
REGISTRAR Contact: NameCheap, Inc.
>>> Last update of RDAP database: 2026-06-08T14:19:15Z

SEO revizija

🔍

Tehnični SEO

✓

Koda odziva

200

Stanje 200 OK - stran se pravilno naloži.

✓

Kodiranje znakov

Page: utf-8, Header: utf-8

Kodiranje znakov, skladno med HTML in glavami.

✓

Velikost strani

49012 bytes

Velikost strani sprejemljiva za hitro nalaganje.

✓

Viri

16 total

Optimalno število virov.

✓

Oznake Hreflang

0 hreflang tags

Dodajte oznake hreflang, če imate večjezično vsebino.

Robots.txt

Missing

Dodajte datoteko robots.txt za nadzor indeksiranja iskalnika.

Sitemap

Not found

Dodajte sitemap.xml in se sklicujte nanj v robots.txt.

✓

HTTPS

Yes

Varna povezava HTTPS je omogočena.

✓

Stiskanje

gzip

Omogočeno stiskanje Gzip ali Zstd za hitrejše nalaganje.

✓

Predpomnjenje

max-age=600

Glave za nadzor predpomnilnika so pravilno nastavljene.

✓

Hitrost strani

0.66 ms

Odlična hitrost nalaganja.

SEO na strani

Naslov

Proceedings of Machine Learning Research | The Proceedings of Machine Learning Research (formerly JMLR Workshop and Conference Proceedings) is a series aimed specifically at publishing machine learning research presented at workshops and conferences. Each volume is separately titled and associated with a particular workshop or conference. Volumes are published online on the PMLR web site. The Series Editors are Neil D. Lawrence and Mark Reid.

Naslov je predolg. Zmanjšajte na 30–60 znakov, da preprečite obrezovanje.

Meta opis

The Proceedings of Machine Learning Research (formerly JMLR Workshop and Conference Proceedings) is a series aimed specifically at publishing machine learning research presented at workshops and conferences. Each volume is separately titled and associated with a particular workshop or conference. Volumes are published online on the PMLR web site. The Series Editors are Neil D. Lawrence and Mark Reid. Lenght:403

Meta opis je predolg. Zmanjšajte na 100-160 znakov.

✓

Naslov H1

1 found - "Proceedings of Machine Learning Research"

Dobro – najden je bil en naslov H1.

✓

Štetje besed

1741

Dobra dolžina vsebine (priporočeno 500–2000 besed).

Canonical Tag

Dodajte kanonično oznako, da preprečite težave s podvojeno vsebino.

✓

Podvojena meta

[]

Ni podvojenih metaoznak.

✓

Ključne besede

unknown

Nastavljene meta ključne besede (opomba: večji iskalniki jih ne uporabljajo).

Vsebina in UX

✓

Jezik

Jezikovni atribut je pravilno nastavljen.

Slike

3 total, 1 missing ALT

Dodajte besedilo ALT slikam za dostopnost in SEO.

✓

Viewport

width=device-width, initial-scale=1

Metaoznaka vidnega polja je pravilno nastavljena za mobilne naprave.

Odpri graf

Missing: og:image

Dodajte manjkajoče oznake OpenGraph za skupno rabo v družabnih medijih:og:image

✓

Strukturirani podatki

1 JSON-LD scripts

Najdeni so strukturirani podatki (JSON-LD).

Položaji v Google

Iskalne fraze - Google

🔍

Položaj	Fraza	Stran	Izrezek
1	new anns	/v188/maile22a/maile...	When, where, and how to add new neurons to ANNs Автор: K Maile · 2022 · Цитируется: 29 — By dynamically building ANNs throughout learning, we can aim towards networks which learn not only parameters but also their architectures for specific tasks, ...
2(-1)	h m c n	/v80/wehrmann18a.htm...	Hierarchical Multi-Label Classification Networks Автор: J Wehrmann · 2018 · Цитируется: 431 — In this paper, we propose novel neural network architectures for HMC called HMCN , capable of simultaneously optimizing local and global loss functions.
4	з р ь	/r3/meek01a/meek01a....	р р х ь в ю ж ж ь р р р лж Автор: C Meek · 2001 · Цитируется: 42 — л в рсь в сшв ь ь ь тжс п я с п жр р р сть я п р ж т п яс п жся ь в фх╧ ж сж ь щ в р╦ я э яыя п ╧ п сс ╦ р я р яыя в ж ть ср п р ь т╧.
6	ю е ж	/r3/meek01a/meek01a....	р р х ь в ю ж ж ь р р р лж Автор: C Meek · 2001 · Цитируется: 44 — л в рсь в сшв ь ь ь тжс п я с п жр р р сть я п р ж т п яс п жся ь в фх╧ ж сж ь щ в р╦ я э яыя п ╧ п сс ╦ р я р яыя в ж ть ср п р ь т╧.
7	e x 1 n n 1	/v84/xue18a/xue18a.p...	Achieving the time of 1-NN, but the accuracy of k-NN Автор: L Xue · Цитируется: 29 — We propose a simple approach which, given distributed computing resources, can nearly achieve the accuracy of k- NN prediction, while.
8	н л д ь	/r3/meek01b/meek01b....	р ж ьвып сь птт с ь е зжы р рж д л Автор: C Meek · 2001 · Цитируется: 8 — д ь э д рв ж ь ь р ж ╩ щ л ╨в с ь я. Є в ып з ж ь я ж т ╦ьвж ╨вщ ш сшь ... п рс ь рж ьра р п н ьэ к ь с в р сжх р ь е╨вв жх щь вж з рм жс эг ╨ ъҐ ...
9	f t m l	/v70/zheng17a.html	Follow the Moving Leader in Deep Learning Автор: S Zheng · 2017 · Цитируется: 34 — Unlike the FTRL family of algorithms, the recent samples are weighted more heavily in each iteration and so FTML can adapt more quickly to changes . We show that ...
33	2017 model	/v70/finn17a.html	Model-Agnostic Meta-Learning for Fast Adaptation of Deep ... Автор: C Finn · 2017 · Цитируется: 16539 — We propose an algorithm for meta-learning that is model -agnostic, in the sense that it is compatible with any model trained with gradient descent.
34	rt models	/v229/zitkovich23a.h...	RT-2: Vision-Language-Action Models Transfer Web ... Автор: B Zitkovich · 2023 · Цитируется: 1687 — We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization.
36	multi modeling	/v162/bao22c.html	Generative Modeling for Multi-task Visual Learning Автор: Z Bao · 2022 · Цитируется: 16 — We propose a general multi-task oriented generative modeling (MGM) framework , by coupling a discriminative multi-task network with a generative network.

Dodatne storitve

💎

proceedings.mlr.press