TensorFlow Probability (JAX backend)
E438358
Python software library
open-source software
probabilistic programming library
statistical modeling library
TensorFlow Probability (JAX backend) is a probabilistic programming and statistical modeling library that runs on JAX, providing tools for Bayesian inference, probabilistic layers, and advanced distributions with XLA-accelerated computation.
All labels observed (2)
| Label | Occurrences |
|---|---|
| TensorFlow Probability | 1 |
| TensorFlow Probability (JAX backend) canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4425385 — 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: TensorFlow Probability (JAX backend) Context triple: [JAX, compatibleWith, TensorFlow Probability (JAX backend)]
-
A.
TensorFlow Extended
TensorFlow Extended (TFX) is an end-to-end platform for deploying, managing, and scaling production machine learning pipelines built on TensorFlow.
-
B.
TensorFlow
TensorFlow is an open-source, end-to-end machine learning and deep learning framework widely used for building, training, and deploying neural network models at scale.
-
C.
TensorFlow.js
TensorFlow.js is a JavaScript library that enables training and running machine learning models directly in the browser and in Node.js using TensorFlow.
-
D.
Swift for TensorFlow
Swift for TensorFlow is an experimental machine learning platform that integrates TensorFlow directly into the Swift programming language to enable differentiable programming and high-performance model development.
-
E.
TPUs (via XLA integrations)
TPUs (via XLA integrations) are Google's specialized tensor processing units that can be used as accelerators for PyTorch models through the XLA compilation framework.
- 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: TensorFlow Probability (JAX backend) Target entity description: TensorFlow Probability (JAX backend) is a probabilistic programming and statistical modeling library that runs on JAX, providing tools for Bayesian inference, probabilistic layers, and advanced distributions with XLA-accelerated computation.
-
A.
TensorFlow Extended
TensorFlow Extended (TFX) is an end-to-end platform for deploying, managing, and scaling production machine learning pipelines built on TensorFlow.
-
B.
TensorFlow
TensorFlow is an open-source, end-to-end machine learning and deep learning framework widely used for building, training, and deploying neural network models at scale.
-
C.
TensorFlow.js
TensorFlow.js is a JavaScript library that enables training and running machine learning models directly in the browser and in Node.js using TensorFlow.
-
D.
Swift for TensorFlow
Swift for TensorFlow is an experimental machine learning platform that integrates TensorFlow directly into the Swift programming language to enable differentiable programming and high-performance model development.
-
E.
TPUs (via XLA integrations)
TPUs (via XLA integrations) are Google's specialized tensor processing units that can be used as accelerators for PyTorch models through the XLA compilation framework.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
Python software library
ⓘ
open-source software ⓘ probabilistic programming library ⓘ statistical modeling library ⓘ |
| basedOn | TensorFlow Probability NERFINISHED ⓘ |
| compatibleWith |
JAX transformations
ⓘ
NumPyro-style JAX workflows ⓘ jax.grad ⓘ jax.jit ⓘ jax.pmap ⓘ jax.vmap ⓘ |
| designedFor |
Bayesian modeling
ⓘ
probabilistic deep learning ⓘ scientific computing ⓘ uncertainty quantification ⓘ |
| developedBy | Google NERFINISHED ⓘ |
| documentationURL | https://www.tensorflow.org/probability ⓘ |
| implements |
log-probability evaluation
ⓘ
reparameterization gradients ⓘ sampling from distributions ⓘ transformed distributions via bijectors ⓘ |
| language | Python NERFINISHED ⓘ |
| license | Apache License 2.0 ⓘ |
| partOf | TensorFlow ecosystem ⓘ |
| provides |
Monte Carlo estimators
ⓘ
bijectors ⓘ inference utilities ⓘ probabilistic layers for neural networks ⓘ probability distributions ⓘ random variable abstractions ⓘ tools for Bayesian neural networks ⓘ |
| supports |
Bayesian inference
ⓘ
GPU acceleration ⓘ Hamiltonian Monte Carlo NERFINISHED ⓘ Markov chain Monte Carlo ⓘ No-U-Turn Sampler NERFINISHED ⓘ TPU acceleration ⓘ XLA-accelerated computation ⓘ advanced probability distributions ⓘ automatic differentiation ⓘ functional programming style with JAX ⓘ gradient-based optimization ⓘ just-in-time compilation via XLA ⓘ probabilistic layers ⓘ probabilistic programming ⓘ stochastic variational inference ⓘ variational inference ⓘ vectorized probabilistic computation ⓘ |
| usesBackend | JAX NERFINISHED ⓘ |
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: TensorFlow Probability (JAX backend) Description of subject: TensorFlow Probability (JAX backend) is a probabilistic programming and statistical modeling library that runs on JAX, providing tools for Bayesian inference, probabilistic layers, and advanced distributions with XLA-accelerated computation.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
TensorFlow Probability