Sepp Hochreiter
E736829
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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Sepp Hochreiter canonical | 6 |
How this entity was disambiguated
This entity first appeared as the object of triple T8482882 — 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: Sepp Hochreiter Context triple: [Alex Graves, influencedBy, Sepp Hochreiter]
-
A.
David E. Rumelhart
David E. Rumelhart was a pioneering cognitive psychologist and neural network researcher whose work on parallel distributed processing and backpropagation profoundly shaped modern cognitive science and machine learning.
-
B.
John Hopfield
John Hopfield is an American physicist and neuroscientist best known for introducing the Hopfield network, a pioneering model in neural networks and computational neuroscience.
-
C.
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.
-
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.
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.
- 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: Sepp Hochreiter Target entity description: 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.
-
A.
David E. Rumelhart
David E. Rumelhart was a pioneering cognitive psychologist and neural network researcher whose work on parallel distributed processing and backpropagation profoundly shaped modern cognitive science and machine learning.
-
B.
John Hopfield
John Hopfield is an American physicist and neuroscientist best known for introducing the Hopfield network, a pioneering model in neural networks and computational neuroscience.
-
C.
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.
-
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.
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.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
person
ⓘ
researcher ⓘ |
| academicDegree | PhD in computer science ⓘ |
| awardReceived |
Austrian Cross of Honour for Science and Art
ⓘ
J. K. Aggarwal Prize of the International Association for Pattern Recognition ⓘ |
| citizenship | Austria ⓘ |
| coInvented |
LSTM
ⓘ
linked to:
LSTM networks
Long Short-Term Memory ⓘ
linked to:
LSTM networks
|
| coInventedWith | Jürgen Schmidhuber ⓘ |
| contributedTo | analysis of vanishing gradient problem in recurrent neural networks ⓘ |
| doctoralAdvisor | Jürgen Schmidhuber ⓘ |
| educatedAt |
Technische Universität München
ⓘ
linked to:
Technical University of Munich
|
| employer | Johannes Kepler University Linz ⓘ |
| familyName | Hochreiter ⓘ |
| fieldOfResearch |
gradient-based learning
ⓘ
sequence learning ⓘ vanishing gradient problem ⓘ |
| fieldOfWork |
artificial intelligence
ⓘ
computer science ⓘ deep learning ⓘ machine learning ⓘ neural networks ⓘ recurrent neural networks ⓘ |
| givenName | Sepp ⓘ |
| hasAcademicAffiliation | Austrian Academy of Sciences ⓘ |
| hasHIndex | high citation impact in machine learning research ⓘ |
| hasRole |
pioneer of deep learning
ⓘ
pioneer of recurrent neural networks ⓘ |
| influenced |
applications of LSTM in handwriting recognition
ⓘ
applications of LSTM in natural language processing ⓘ applications of LSTM in speech recognition ⓘ development of modern sequence modeling ⓘ |
| knownFor |
LSTM
ⓘ
linked to:
LSTM networks
Long Short-Term Memory ⓘ
linked to:
LSTM networks
deep learning ⓘ recurrent neural networks ⓘ |
| languageSpoken | German ⓘ |
| name | Sepp Hochreiter ⓘ |
| nationality | Austrian ⓘ |
| notablePublication | Long Short-Term Memory (1997) ⓘ |
| positionHeld |
head of Institute for Machine Learning
ⓘ
professor ⓘ |
| researchInterest |
bioinformatics applications of machine learning
ⓘ
optimization in deep learning ⓘ probabilistic modeling ⓘ |
| workInstitution | Johannes Kepler University Linz ⓘ |
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: Sepp Hochreiter Description of subject: 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.
Referenced by (6)
Full triples — surface form annotated when it differs from this entity's canonical label.