Main

processing priority

3

site type

0 (generic, awaiting analysis)

review version

11

html import

20 (imported)

Events

first seen date

2024-11-17 12:10:25

expired found date

-

created at

2024-11-17 12:10:25

updated at

2024-11-17 12:10:25

Domain name statistics

length

34

crc

14073

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

7350

mp size raw text

4937

mp inner links count

1

mp inner links status

10 (links queued, awaiting import)

Open Graph

title

description

image

site name

author

updated

2026-03-05 20:31:00

raw text

Counterfactual Simulation Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing Nataniel Ruiz Sarah Adel Bargal Cihang Xie Kate Saenko Stan Sclaroff Boston University - Georgetown University - UC Santa Cruz - MIT-IBM Watson AI Lab NeurIPS 2022 A way to compare vastly different network architectures using counterfactual reasoning [Paper]      [Dataset]      [Code (soon)]      Abstract Modern deep neural networks tend to be evaluated on static test sets making it hard to evaluate for robustness issues with respect to specific scene variations (e.g. object scale, object pose, scene lighting and 3D occlusions). Collecting real datasets with fine-grained naturalistic variations of sufficient scale can be extremely time-consuming and expensive. In our work, we present Counterfactual Simulation Testing , a framework that allows us to study the robustness of neural networks with respect to some of these naturalist...

Text analysis

redirect type

0 (-)

block type

0 (no issues)

detected language

0 (awaiting analysis)

category id

Zastosowania AI (149)

index version

1

spam phrases

0

Text statistics

text nonlatin

0

text cyrillic

0

text characters

3955

text words

710

text unique words

286

text lines

46

text sentences

22

text paragraphs

7

text words per sentence

32

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

2

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

-