Jürgen Schmidhuber
E739007
Jürgen Schmidhuber is a German computer scientist and AI researcher best known for his pioneering work on neural networks and the co-invention of Long Short-Term Memory (LSTM) networks.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Jürgen Schmidhuber canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T8482881 — 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: Jürgen Schmidhuber Context triple: [Alex Graves, influencedBy, Jürgen Schmidhuber]
-
A.
Marcus Hutter
Marcus Hutter is a computer scientist known for his foundational work in universal artificial intelligence and the development of the AIXI model of optimal decision-making.
-
B.
Martin Riedmiller
Martin Riedmiller is a German computer scientist and pioneer in deep reinforcement learning, known for his influential work on neural-network-based control and contributions to landmark deep RL systems.
-
C.
Sepp Hochreiter
Sepp Hochreiter is an Austrian computer scientist best known as a co-inventor of Long Short-Term Memory (LSTM) networks and a pioneer in deep learning and recurrent neural networks.
-
D.
Geoffrey Hinton
Geoffrey Hinton is a pioneering computer scientist widely regarded as one of the founding figures of deep learning and modern artificial intelligence.
-
E.
Jakob Uszkoreit
Jakob Uszkoreit is a computer scientist and AI researcher best known as one of the co-authors of the seminal "Attention Is All You Need" paper that introduced the Transformer architecture.
- 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: Jürgen Schmidhuber Target entity description: Jürgen Schmidhuber is a German computer scientist and AI researcher best known for his pioneering work on neural networks and the co-invention of Long Short-Term Memory (LSTM) networks.
-
A.
Marcus Hutter
Marcus Hutter is a computer scientist known for his foundational work in universal artificial intelligence and the development of the AIXI model of optimal decision-making.
-
B.
Martin Riedmiller
Martin Riedmiller is a German computer scientist and pioneer in deep reinforcement learning, known for his influential work on neural-network-based control and contributions to landmark deep RL systems.
-
C.
Sepp Hochreiter
Sepp Hochreiter is an Austrian computer scientist best known as a co-inventor of Long Short-Term Memory (LSTM) networks and a pioneer in deep learning and recurrent neural networks.
-
D.
Geoffrey Hinton
Geoffrey Hinton is a pioneering computer scientist widely regarded as one of the founding figures of deep learning and modern artificial intelligence.
-
E.
Jakob Uszkoreit
Jakob Uszkoreit is a computer scientist and AI researcher best known as one of the co-authors of the seminal "Attention Is All You Need" paper that introduced the Transformer architecture.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
artificial intelligence researcher
ⓘ
machine learning researcher ⓘ person ⓘ |
| academicDegree | PhD in computer science ⓘ |
| awardReceived |
Helmholtz Award
NERFINISHED
ⓘ
IEEE Neural Networks Pioneer Award NERFINISHED ⓘ |
| coFounded | Nnaisense NERFINISHED ⓘ |
| coInvented |
LSTM networks
NERFINISHED
ⓘ
Long Short-Term Memory NERFINISHED ⓘ |
| coInventorWith | Sepp Hochreiter NERFINISHED ⓘ |
| countryOfCitizenship | Germany ⓘ |
| dateOfBirth | 1963-01-17 ⓘ |
| educatedAt |
Ludwig Maximilian University of Munich
NERFINISHED
ⓘ
Technische Universität München NERFINISHED ⓘ |
| employer |
IDSIA
NERFINISHED
ⓘ
Università della Svizzera italiana NERFINISHED ⓘ |
| familyName | Schmidhuber NERFINISHED ⓘ |
| fieldOfWork |
artificial intelligence
ⓘ
deep learning ⓘ machine learning ⓘ neural networks ⓘ |
| founded | Nnaisense NERFINISHED ⓘ |
| givenName | Jürgen NERFINISHED ⓘ |
| hasResearchInterest |
algorithmic information theory
ⓘ
deep learning architectures ⓘ recurrent neural networks ⓘ reinforcement learning ⓘ sequence learning ⓘ universal artificial intelligence ⓘ |
| knownFor |
LSTM networks
NERFINISHED
ⓘ
Long Short-Term Memory NERFINISHED ⓘ recurrent neural networks ⓘ work on deep learning ⓘ |
| languageSpoken |
English
ⓘ
German ⓘ |
| name | Jürgen Schmidhuber NERFINISHED ⓘ |
| nationality | German ⓘ |
| notableStudent | Sepp Hochreiter NERFINISHED ⓘ |
| placeOfBirth | Munich NERFINISHED ⓘ |
| positionHeld |
director of IDSIA
ⓘ
professor ⓘ |
| publishedIn |
Journal of Machine Learning Research
NERFINISHED
ⓘ
Nature NERFINISHED ⓘ Neural Computation NERFINISHED ⓘ Neural Networks NERFINISHED ⓘ Science NERFINISHED ⓘ |
| workLocation |
Lugano
NERFINISHED
ⓘ
Switzerland NERFINISHED ⓘ |
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: Jürgen Schmidhuber Description of subject: Jürgen Schmidhuber is a German computer scientist and AI researcher best known for his pioneering work on neural networks and the co-invention of Long Short-Term Memory (LSTM) networks.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.