Max Welling
E200669
Max Welling is a prominent machine learning researcher known for foundational contributions to probabilistic deep learning and Bayesian inference, including co-developing variational autoencoders.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Max Welling canonical | 5 |
How this entity was disambiguated
This entity first appeared as the object of triple T1807328 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Max Welling Context triple: [variational autoencoders, introducedBy, Max Welling]
-
A.
Jonathon Shlens
Jonathon Shlens is a computer scientist and researcher known for his contributions to deep learning and computer vision, including influential work at Google.
-
B.
Samy Bengio
Samy Bengio is a prominent machine learning researcher known for his contributions to deep learning and his leadership roles at major AI organizations including Google and Apple.
-
C.
Ian Goodfellow
Ian Goodfellow is a machine learning researcher best known for inventing Generative Adversarial Networks (GANs) and co-authoring the influential textbook "Deep Learning."
-
D.
Yoshua Bengio
Yoshua Bengio is a Canadian computer scientist and deep learning pioneer whose work on neural networks and representation learning has been foundational to modern artificial intelligence.
-
E.
Dario Amodei
Dario Amodei is an AI researcher and entrepreneur, co-founder and CEO of Anthropic and former OpenAI research leader known for his work on large language models and AI safety.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Max Welling Target entity description: Max Welling is a prominent machine learning researcher known for foundational contributions to probabilistic deep learning and Bayesian inference, including co-developing variational autoencoders.
-
A.
Jonathon Shlens
Jonathon Shlens is a computer scientist and researcher known for his contributions to deep learning and computer vision, including influential work at Google.
-
B.
Samy Bengio
Samy Bengio is a prominent machine learning researcher known for his contributions to deep learning and his leadership roles at major AI organizations including Google and Apple.
-
C.
Ian Goodfellow
Ian Goodfellow is a machine learning researcher best known for inventing Generative Adversarial Networks (GANs) and co-authoring the influential textbook "Deep Learning."
-
D.
Yoshua Bengio
Yoshua Bengio is a Canadian computer scientist and deep learning pioneer whose work on neural networks and representation learning has been foundational to modern artificial intelligence.
-
E.
Dario Amodei
Dario Amodei is an AI researcher and entrepreneur, co-founder and CEO of Anthropic and former OpenAI research leader known for his work on large language models and AI safety.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
academic
ⓘ
computer scientist ⓘ machine learning researcher ⓘ person ⓘ |
| coAuthorOf | Auto-Encoding Variational Bayes ⓘ |
| coAuthorWith |
Diederik P. Kingma
ⓘ
Geoffrey Hinton ⓘ Tom Heskes ⓘ Yee-Whye Teh ⓘ
surface form:
Yee Whye Teh
Yoshua Bengio ⓘ |
| countryOfCitizenship | Netherlands ⓘ |
| employer | University of Amsterdam ⓘ |
| fieldOfWork |
Bayesian inference
ⓘ
artificial intelligence ⓘ deep learning ⓘ graphical models ⓘ machine learning ⓘ probabilistic deep learning ⓘ probabilistic modeling ⓘ variational inference ⓘ |
| hasAcademicPositionAt | University of Amsterdam ⓘ |
| hasResearchInterest |
Bayesian deep learning
ⓘ
Monte Carlo method ⓘ
surface form:
Monte Carlo methods
probabilistic graphical models ⓘ representation learning ⓘ scalable inference ⓘ unsupervised learning ⓘ variational autoencoders ⓘ |
| hasRole |
professor
ⓘ
researcher ⓘ scientific advisor ⓘ |
| influenced |
development of variational autoencoders in deep learning
ⓘ
research in Bayesian deep learning ⓘ research in probabilistic deep learning ⓘ |
| knownFor |
co-developing variational autoencoders
ⓘ
foundational contributions to Bayesian inference in machine learning ⓘ foundational contributions to probabilistic deep learning ⓘ research on Markov chain Monte Carlo methods ⓘ research on approximate inference algorithms ⓘ research on scalable Bayesian learning ⓘ research on variational inference ⓘ |
| nationality | Dutch ⓘ |
| notableWork |
Auto-Encoding Variational Bayes
ⓘ
papers on Bayesian learning for neural networks ⓘ papers on variational inference for graphical models ⓘ |
| positionHeld | professor of machine learning ⓘ |
| workLocation | Amsterdam ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Max Welling Description of subject: Max Welling is a prominent machine learning researcher known for foundational contributions to probabilistic deep learning and Bayesian inference, including co-developing variational autoencoders.
Referenced by (5)
Full triples — surface form annotated when it differs from this entity's canonical label.