Jonas Schneider
E444493
Jonas Schneider is a researcher in reinforcement learning best known for co-authoring the Hindsight Experience Replay technique for more efficient learning from sparse rewards.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Jonas Schneider canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4470525 — 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.
Target entity: Jonas Schneider Context triple: [Hindsight Experience Replay, proposedBy, Jonas Schneider]
-
A.
Jens Schlosser
Jens Schlosser is a cinematographer known for his work on the film "The Salvation."
-
B.
Tobias Fünke
Tobias Fünke is a socially awkward, aspiring actor and former analyst-therapist known for his oblivious behavior and unintentional double entendres in the television series "Arrested Development."
-
C.
Christopher Scholz
Christopher Scholz is a prominent geophysicist renowned for his influential work on the mechanics of earthquakes and faulting.
-
D.
Maximilian Scheffler
Maximilian Scheffler is a scientist known as a notable student and protégé of the German quantum chemist Joachim Sauer.
-
E.
Sebastian Knapp
Sebastian Knapp is an actor best known for his role in the 2013 television miniseries adaptation of the Bible.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Target entity: Jonas Schneider Target entity description: Jonas Schneider is a researcher in reinforcement learning best known for co-authoring the Hindsight Experience Replay technique for more efficient learning from sparse rewards.
-
A.
Jens Schlosser
Jens Schlosser is a cinematographer known for his work on the film "The Salvation."
-
B.
Tobias Fünke
Tobias Fünke is a socially awkward, aspiring actor and former analyst-therapist known for his oblivious behavior and unintentional double entendres in the television series "Arrested Development."
-
C.
Christopher Scholz
Christopher Scholz is a prominent geophysicist renowned for his influential work on the mechanics of earthquakes and faulting.
-
D.
Maximilian Scheffler
Maximilian Scheffler is a scientist known as a notable student and protégé of the German quantum chemist Joachim Sauer.
-
E.
Sebastian Knapp
Sebastian Knapp is an actor best known for his role in the 2013 television miniseries adaptation of the Bible.
- F. None of above. chosen
Statements (11)
| Predicate | Object |
|---|---|
| instanceOf |
reinforcement learning technique
ⓘ
researcher ⓘ |
| aimsTo | improve sample efficiency ⓘ |
| appliesTo | sparse reward problems ⓘ |
| coAuthorOf | Hindsight Experience Replay NERFINISHED ⓘ |
| fieldOfWork | reinforcement learning ⓘ |
| knownFor | Hindsight Experience Replay NERFINISHED ⓘ |
| notableWork | Hindsight Experience Replay NERFINISHED ⓘ |
| researchInterest |
sample-efficient learning algorithms
ⓘ
sparse reward reinforcement learning ⓘ |
| usedIn | goal-conditioned reinforcement learning ⓘ |
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.
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.
Subject: Jonas Schneider Description of subject: Jonas Schneider is a researcher in reinforcement learning best known for co-authoring the Hindsight Experience Replay technique for more efficient learning from sparse rewards.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.