Arvind Neelakantan
E457863
Arvind Neelakantan is a researcher in artificial intelligence and machine learning known for his work on large language models and neural network architectures.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Arvind Neelakantan canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T4651160 — 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: Arvind Neelakantan Context triple: [Tom B. Brown, hasCoAuthor, Arvind Neelakantan]
-
A.
Vijay Vasudevan
Vijay Vasudevan is a computer scientist known for his work in machine learning and systems research, including co-authoring influential papers with Christian Szegedy.
-
B.
Ravi Menon
Ravi Menon is a Singaporean economist and central banker who has served as the long-time managing director of the Monetary Authority of Singapore, playing a key role in shaping the country’s financial and monetary policy.
-
C.
Bharat Ramaswami
Bharat Ramaswami is an Indian economist known for his research in agricultural economics, development policy, and trade, and for his long association with leading academic institutions in India.
-
D.
Raj Subramaniam
Raj Subramaniam is the President and Chief Executive Officer of FedEx Corporation, a leading global logistics and delivery services company.
-
E.
Rajeev Misra
Rajeev Misra is an Indian-born financier and executive best known for leading SoftBank’s Vision Fund, one of the world’s largest technology investment funds.
- 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: Arvind Neelakantan Target entity description: Arvind Neelakantan is a researcher in artificial intelligence and machine learning known for his work on large language models and neural network architectures.
-
A.
Vijay Vasudevan
Vijay Vasudevan is a computer scientist known for his work in machine learning and systems research, including co-authoring influential papers with Christian Szegedy.
-
B.
Ravi Menon
Ravi Menon is a Singaporean economist and central banker who has served as the long-time managing director of the Monetary Authority of Singapore, playing a key role in shaping the country’s financial and monetary policy.
-
C.
Bharat Ramaswami
Bharat Ramaswami is an Indian economist known for his research in agricultural economics, development policy, and trade, and for his long association with leading academic institutions in India.
-
D.
Raj Subramaniam
Raj Subramaniam is the President and Chief Executive Officer of FedEx Corporation, a leading global logistics and delivery services company.
-
E.
Rajeev Misra
Rajeev Misra is an Indian-born financier and executive best known for leading SoftBank’s Vision Fund, one of the world’s largest technology investment funds.
- F. None of above. chosen
Statements (43)
| Predicate | Object |
|---|---|
| instanceOf |
artificial intelligence researcher
ⓘ
computer scientist ⓘ researcher ⓘ |
| academicDegree |
Bachelor of Technology
ⓘ
PhD in Computer Science ⓘ |
| coAuthorOf |
Adding Gradient Noise Improves Learning for Very Deep Networks
NERFINISHED
ⓘ
Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space NERFINISHED ⓘ Learning a Natural Language Interface with Neural Programmer NERFINISHED ⓘ Neural Programmer: Inducing Latent Programs with Gradient Descent NERFINISHED ⓘ Simple and Effective Semi-Supervised Question Answering NERFINISHED ⓘ |
| educatedAt |
Indian Institute of Technology Madras
NERFINISHED
ⓘ
University of Massachusetts Amherst NERFINISHED ⓘ |
| employer |
Google
ⓘ
Google Brain NERFINISHED ⓘ OpenAI NERFINISHED ⓘ |
| fieldOfWork |
artificial intelligence
ⓘ
deep learning ⓘ large language models ⓘ machine learning ⓘ natural language processing ⓘ neural network architectures ⓘ |
| hasCitizenship | India NERFINISHED ⓘ |
| hasPublicationVenue |
ACL
NERFINISHED
ⓘ
EMNLP NERFINISHED ⓘ ICLR NERFINISHED ⓘ ICML NERFINISHED ⓘ NeurIPS NERFINISHED ⓘ |
| knownFor |
research in artificial intelligence
ⓘ
research in machine learning ⓘ work on large language models ⓘ work on neural network architectures ⓘ |
| language | English ⓘ |
| notableWorkArea |
neural program induction for question answering
ⓘ
semi-supervised learning for QA ⓘ training techniques for deep neural networks ⓘ |
| origin | India NERFINISHED ⓘ |
| positionHeld |
applied scientist
ⓘ
research scientist ⓘ |
| researchInterest |
language modeling
ⓘ
neural program induction ⓘ question answering ⓘ reinforcement learning ⓘ sequence-to-sequence models ⓘ |
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: Arvind Neelakantan Description of subject: Arvind Neelakantan is a researcher in artificial intelligence and machine learning known for his work on large language models and neural network architectures.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject surface form:
Tom B. Brown
subject surface form:
Language Models are Few-Shot Learners