jax.experimental
E438360
jax.experimental is a submodule of the JAX library that provides access to experimental, unstable, or cutting-edge numerical and machine learning features not yet part of the stable API.
All labels observed (1)
| Label | Occurrences |
|---|---|
| jax.experimental canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4425390 — 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: jax.experimental Context triple: [JAX, hasComponent, jax.experimental]
-
A.
XLA
XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra that optimizes and accelerates machine learning computations on hardware such as TPUs and GPUs.
-
B.
Chainer
Chainer is an open-source deep learning framework for Python that pioneered a flexible "define-by-run" computation graph approach to building neural networks.
-
C.
MXNet
MXNet is an open-source deep learning framework designed for efficient, scalable training and inference across multiple GPUs and distributed systems.
-
D.
tensor programs framework
The tensor programs framework is a theoretical approach developed by Greg Yang that rigorously analyzes and characterizes the behavior and scaling limits of large neural networks using tools from probability and random matrix theory.
-
E.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
- 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: jax.experimental Target entity description: jax.experimental is a submodule of the JAX library that provides access to experimental, unstable, or cutting-edge numerical and machine learning features not yet part of the stable API.
-
A.
XLA
XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra that optimizes and accelerates machine learning computations on hardware such as TPUs and GPUs.
-
B.
Chainer
Chainer is an open-source deep learning framework for Python that pioneered a flexible "define-by-run" computation graph approach to building neural networks.
-
C.
MXNet
MXNet is an open-source deep learning framework designed for efficient, scalable training and inference across multiple GPUs and distributed systems.
-
D.
tensor programs framework
The tensor programs framework is a theoretical approach developed by Greg Yang that rigorously analyzes and characterizes the behavior and scaling limits of large neural networks using tools from probability and random matrix theory.
-
E.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
- F. None of above. chosen
Statements (35)
| Predicate | Object |
|---|---|
| instanceOf |
Python module
ⓘ
software component ⓘ |
| apiStatus | experimental ⓘ |
| documentationWarning | APIs may be removed or changed ⓘ |
| domain |
machine learning
ⓘ
numerical computing ⓘ |
| ecosystem | Python scientific computing ⓘ |
| hasPurpose | to expose features not yet in the stable JAX API ⓘ |
| isNamespaceFor |
jax.experimental.callback
ⓘ
jax.experimental.checkify NERFINISHED ⓘ jax.experimental.compilation_cache NERFINISHED ⓘ jax.experimental.global_device_array ⓘ jax.experimental.host_callback ⓘ jax.experimental.jax2tf ⓘ jax.experimental.loops NERFINISHED ⓘ jax.experimental.maps ⓘ jax.experimental.multihost_utils ⓘ jax.experimental.optimizers NERFINISHED ⓘ jax.experimental.pjit ⓘ jax.experimental.shard_map ⓘ jax.experimental.sparse ⓘ jax.experimental.stax ⓘ |
| library | JAX NERFINISHED ⓘ |
| mayChange | without backward compatibility guarantees ⓘ |
| partOf | jax NERFINISHED ⓘ |
| programmingLanguage | Python ⓘ |
| provides |
cutting-edge machine learning functionality
ⓘ
cutting-edge numerical functionality ⓘ experimental features ⓘ unstable APIs ⓘ |
| requires | jax NERFINISHED ⓘ |
| stability | unstable ⓘ |
| targetUsers |
advanced JAX users
ⓘ
early adopters of new JAX features ⓘ researchers ⓘ |
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: jax.experimental Description of subject: jax.experimental is a submodule of the JAX library that provides access to experimental, unstable, or cutting-edge numerical and machine learning features not yet part of the stable API.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.