Neuro-Symbolic Concept Learner
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Massachusetts Institute of Technology (MIT),Tsinghua University,MIT-IBM Watson AI Lab,DeepMind
- Organisation type
- Academia,Academia,Academia,Industry,Industry
- Country
- United States of America, China, United Kingdom of Great Britain and Northern Ireland
- Published
- 26 April 2019
- Authors
- Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum, Jiajun Wu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision, Language
- Task
- Visual question answering, Semantic segmentation
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- 100,000 tokens
CLEVR, ImageNet, VQS 5000 in CLEVR 64509 in VQS and whole ImageNet for pretraining "We train NS-CL on 5K images (<10% of CLEVR’s 70K training images). We generate 20 questions for each image for the entire curriculum learning process" section 4.3 "All models use a pre-trained semantic parser on the full CLEVR dataset" "The only extra supervision of the visual perception module comes from the pre-training of the perception modules on ImageNet (Deng et al., 2009). To quantify the influence of th…
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open source
MIT code: https://github.com/vacancy/NSCL-PyTorch-Release
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Unknown
- Citations
- 837
"NS-CL’s modularized design enables interpretable, robust, and accurate visual reasoning: it achieves state-of-the-art performance on the CLEVR datase"
Sources
Where this record came from and when it was last checked.
- Reference
- The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
- Last updated
- 25 May 2026
What the numbers mean
About this model
Neuro-Symbolic Concept Learner was published by Massachusetts Institute of Technology (MIT),Tsinghua University,MIT-IBM Watson AI Lab,DeepMind, in United States of America, in April 2019. academia,Academia,Academia,Industry,Industry is the category the publisher falls under.
It works in Vision, Language, and is recorded as doing visual question answering, Semantic segmentation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
The training set ran to roughly 100,000 tokens.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
Neuro-Symbolic Concept Learner — common questions
Is Neuro-Symbolic Concept Learner open source?
No. Neuro-Symbolic Concept Learner has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Neuro-Symbolic Concept Learner have?
No parameter count has been published for Neuro-Symbolic Concept Learner, which is why no memory or speed figure appears on this page.
Who created Neuro-Symbolic Concept Learner?
Neuro-Symbolic Concept Learner was published by Massachusetts Institute of Technology (MIT),Tsinghua University,MIT-IBM Watson AI Lab,DeepMind, based in United States of America, categorised as academia,Academia,Academia,Industry,Industry.
When was Neuro-Symbolic Concept Learner released?
Neuro-Symbolic Concept Learner was published in April 2019. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Neuro-Symbolic Concept Learner used for?
Neuro-Symbolic Concept Learner works in Vision, Language, and is recorded as handling visual question answering, Semantic segmentation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Neuro-Symbolic Concept Learner?
None. Neuro-Symbolic Concept Learner is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.