Main

related bits

0

processing priority

3

site type

0 (generic, awaiting analysis)

review version

11

html import

20 (imported)

Events

first seen date

2024-11-26 07:45:29

expired found date

-

created at

2024-11-26 07:45:29

updated at

2024-11-26 07:45:29

Domain name statistics

length

17

crc

920

tld

86

nm parts

0

nm random digits

0

nm rare letters

0

Connections

is subdomain of id

87719371 (github.io)

previous id

0

replaced with id

0

related id

-

dns primary id

0

dns alternative id

0

lifecycle status

0 (unclassified, or currently active)

Subdomains and pages

deleted subdomains

0

page imported products

0

page imported random

0

page imported parking

0

Error counters

count skipped due to recent timeouts on the same server IP

0

count content received but rejected due to 11-799

0

count dns errors

0

count cert errors

0

count timeouts

0

count http 429

0

count http 404

0

count http 403

0

count http 5xx

0

next operation date

-

Server

server bits

server ip

-

Mainpage statistics

mp import status

20

mp rejected date

-

mp saved date

-

mp size orig

15659

mp size raw text

5936

mp inner links count

0

mp inner links status

1 (no links)

Open Graph

title

description

image

site name

author

updated

2026-02-26 19:40:14

raw text

Marton Havasi Toggle navigation Marton Havasi Research Experience Publications Teaching Marton Havasi I am currently a Perception system engineer working on object detection (lidar + computer vision) for autonomous vehicles. My research background is in neural compression, probabilistic methods, reliable deep learning and interpretability. Quick links: Google scholar , LinkedIn Research My research focuses on probabilistic machine learning and its applications. I am interested in Bayesian inference and Bayesian deep learning [ 3 , 4 , 6 ]. I want to use Bayesian methods to understand model uncertainty in neural networks. Model uncertainty can then be used to build robust and deployable deep learning models that do not exhibit the typical failure modes, such as poor calibration and overconfident predictions of traditionally trained networks. Model uncertainty from Bayesian methods ...

Text analysis

redirect type

0 (-)

block type

0 (no issues)

detected language

1 (English)

category id

Zastosowania AI (149)

index version

1

spam phrases

0

Text statistics

text nonlatin

0

text cyrillic

0

text characters

4532

text words

726

text unique words

341

text lines

113

text sentences

33

text paragraphs

5

text words per sentence

22

text matched phrases

0

text matched dictionaries

0

RSS

rss path

rss status

1 (priority 1 already searched, no matches found)

rss found date

-

rss size orig

0

rss items

0

rss spam phrases

0

rss detected language

0 (awaiting analysis)

inbefore feed id

-

inbefore status

0 (new)

Sitemap

sitemap path

sitemap status

1 (priority 1 already searched, no matches found)

sitemap review version

1

sitemap urls count

0

sitemap urls adult

0

sitemap filtered products

0

sitemap filtered videos

0

sitemap found date

-

sitemap process date

-

sitemap first import date

-

sitemap last import date

-