StratML
31 October 2019
StratML
Labels:
big data
,
data science
,
deep learning
,
linked data
,
machine learning
,
natural language processing
,
semantic web
,
text analytics
30 October 2019
Office Search
office search
hubblehq
complete office search
prime office search
workspaces
flexioffices
free office finder
office genie
coworker
croissant
included.co
instant offices
pickspace
share desk
happy desk
desksnear.me
desktime
coworking
desk pass
copass
coworking wiki
desk surfing
near desk
liquid space
gocowo
find workspaces
sneed
ofixu
qdesq
share your office
spacelist
42 floors
office list
coffices
breather
peerspace
bizly
spacewhiz
beewake
awfis
all office centers
splacer
lexc
desk camping
worksnug
seats 2 meet
coworking.coffee
commercial cafe
preferred office network
heydesk
office freedom
flexas
kontor
labs
spacesworks
thebrew
cowork
hubblehq
complete office search
prime office search
workspaces
flexioffices
free office finder
office genie
coworker
croissant
included.co
instant offices
pickspace
share desk
happy desk
desksnear.me
desktime
coworking
desk pass
copass
coworking wiki
desk surfing
near desk
liquid space
gocowo
find workspaces
sneed
ofixu
qdesq
share your office
spacelist
42 floors
office list
coffices
breather
peerspace
bizly
spacewhiz
beewake
awfis
all office centers
splacer
lexc
desk camping
worksnug
seats 2 meet
coworking.coffee
commercial cafe
preferred office network
heydesk
office freedom
flexas
kontor
labs
spacesworks
thebrew
cowork
28 October 2019
Curse of Dimensionality
As you increase the number of input features, the combination of inputs can grow exponentially. As the combinations grows, each training sample covers a smaller percentage of possibilities. The result being, as you add features, you need to increase the size of your training set, which may be exponentially. As the number of dimensions goes up, the model must train on significantly more data in order to learn an accurate representation of the input space.
Labels:
big data
,
data science
,
deep learning
,
machine learning
,
natural language processing
,
text analytics
26 October 2019
Annotation Services
Figure Eight
Hypothes.is
Open Annotations
Annotatorjs
OpenAnnotate
Prodi.gy
Doccano
Brat
Tagtog
X-Lisa
LightTag
DataTurks
Supervise.ly
AnnotatedStar
Folia
Annotable
Diigo
Zap
Lionbridge
Gate
UIMA
RSTWeb
LabelIMG
VGG Image Annotator
LabelBox
LabelMe
ImageTagger
RectLabel
Diffgram
Fast Annotation Tool
Further Annotation Tools
Hypothes.is
Open Annotations
Annotatorjs
OpenAnnotate
Prodi.gy
Doccano
Brat
Tagtog
X-Lisa
LightTag
DataTurks
Supervise.ly
AnnotatedStar
Folia
Annotable
Diigo
Zap
Lionbridge
Gate
UIMA
RSTWeb
LabelIMG
VGG Image Annotator
LabelBox
LabelMe
ImageTagger
RectLabel
Diffgram
Fast Annotation Tool
Further Annotation Tools
Labels:
big data
,
data science
,
deep learning
,
machine learning
,
natural language processing
,
text analytics
25 October 2019
24 October 2019
23 October 2019
21 October 2019
Description Logics
description logic
description logic primer
description logics
complexity of reasoning
foundations of description logics
ontologies and the semantic web
description logics
list of reasoners
description logic primer
description logics
complexity of reasoning
foundations of description logics
ontologies and the semantic web
description logics
list of reasoners
Labels:
artificial intelligence
,
big data
,
data science
,
intelligent web
,
linked data
,
semantic web
Alternative Sequences
- Attention - memory added to other networks to guide focus
- Transformers - networks that use attention exclusively instead of recurrent and convolutional layers
- Temporal Convolutional Networks - CNN designed for sequences
Attention Is All You Need
17 October 2019
16 October 2019
ALCOMO
Labels:
big data
,
data science
,
intelligent web
,
linked data
,
natural language processing
,
semantic web
AnyBURL
Labels:
big data
,
data science
,
intelligent web
,
linked data
,
natural language processing
,
semantic web
PatyBred
Labels:
big data
,
data science
,
ecommerce
,
intelligent web
,
linked data
,
natural language processing
,
semantic web
WebisLOD
Labels:
big data
,
data science
,
ecommerce
,
intelligent web
,
linked data
,
natural language processing
,
semantic web
DBkWik
Labels:
big data
,
data science
,
ecommerce
,
intelligent web
,
linked data
,
natural language processing
,
semantic web
T2D
Labels:
big data
,
data science
,
ecommerce
,
intelligent web
,
linked data
,
natural language processing
,
semantic web
Winter
Labels:
big data
,
data science
,
ecommerce
,
intelligent web
,
linked data
,
natural language processing
,
semantic web
15 October 2019
Enthymemes and Argument Mining
Finding Enthymemes in Real-World Texts
Argument Mining Using Argumentation Scheme Structures
Argumentative Approaches to Reasoning with Maximal Consistency
Dave the Debater
Argument Mining Papers (Filtered)
Workshop Synthesis:
EMNLP 2019
EMNLP 2018
EMNLP 2017
EMNLP 2016
EMNLP 2015
EMNLP 2014
Tutorials:
Computational Argumentation
Argument Mining
Unsupervised Corpus Wide Claim Detection
Argument Mining
Argumentation Mining (Synthesis Series)
Argument Mining Using Argumentation Scheme Structures
Argumentative Approaches to Reasoning with Maximal Consistency
Dave the Debater
Argument Mining Papers (Filtered)
Workshop Synthesis:
EMNLP 2019
EMNLP 2018
EMNLP 2017
EMNLP 2016
EMNLP 2015
EMNLP 2014
Tutorials:
Computational Argumentation
Argument Mining
Unsupervised Corpus Wide Claim Detection
Argument Mining
Argumentation Mining (Synthesis Series)
Labels:
big data
,
data science
,
deep learning
,
linked data
,
machine learning
,
natural language processing
,
semantic web
,
text analytics
14 October 2019
13 October 2019
12 October 2019
Prime Spirals
Labels:
artificial intelligence
,
big data
,
data science
,
deep learning
,
machine learning
,
security
BibFrame
Labels:
big data
,
data science
,
deep learning
,
library
,
linked data
,
machine learning
,
natural language processing
,
semantic web
,
text analytics
Netron
Labels:
big data
,
data science
,
deep learning
,
JavaScript
,
machine learning
,
python
,
visualization
11 October 2019
9 October 2019
8 October 2019
6 October 2019
KBert
Labels:
big data
,
data science
,
deep learning
,
linked data
,
machine learning
,
natural language processing
,
semantic web
,
text analytics
5 October 2019
2 October 2019
1 October 2019
Marketing Mix
- Packaging
- Partnership
- Passion
- Penetration
- People
- Perception
- Personality
- Persuasion
- Phrases
- Physical
- Place
- Placement
- Planning
- Popularity
- Population
- Positioning
- Positiveness
- Power
- Pragmatism
- Preference
- Price
- Privacy
- Process
- Product
- Productivity
- Professionalism
- Profit
- Promotion
- Prospect
- Publicity
- Purchase
- Push-Pull
- Picture
- Part
- Pilot
- Persona
- Peers
- Pass-Along-Value
- Party
- Pandemic
- Pandemonium
- Pain
- Placebo
- Planting
- Playfulness
- Pleasure
- Plot
- Politics
- Praise
- Prediction
- Premeditation
- Press
- Pressure
- Preview
- Principle
- Prominence
- Promise
- Proof
- Properties
- Prosperous
- Protection
- Purple Cow
- Purpose
- Production
Medical Codes
ICD-10 - Diagnosis
CPT - Procedures
LOINIC - Laboratory
RxNorms - Medications
ICF - Disabilities
CDT - Dentistry Procedures
DSM-IV-TR - Psychiatric Illnesses
NDC - Drugs
DRG - Diagnosis Group
HCPC - Procedures
Survey of Embeddings Use Cases for Clinical Healthcare
CPT - Procedures
LOINIC - Laboratory
RxNorms - Medications
ICF - Disabilities
CDT - Dentistry Procedures
DSM-IV-TR - Psychiatric Illnesses
NDC - Drugs
DRG - Diagnosis Group
HCPC - Procedures
Survey of Embeddings Use Cases for Clinical Healthcare
Labels:
big data
,
data science
,
deep learning
,
machine learning
,
natural language processing
,
text analytics
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