US 2025/0028995 A1Published application

Training and application of bottleneck models and embeddings

AssigneeX Development LLC

InventorsRishabh Singh, David Andre, Garrett Raymond Honke, Falak Shah, Nisarg Vyas, Jayendra Parmar, Brian M. Rosen, Shaili Trivedi

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Abstract

Disclosed implementations relate to adding “bottleneck” models to machine learning pipelines that already apply domain models to translate and/or transfer representations of high-level semantic concepts between domains. In various implementations, an initial representation in a first domain of a transition from an initial state of an environment to a goal state of the environment may be processed based on a pre-trained first domain encoder to generate a first embedding that semantically represents the transition. The first embedding may be processed based on one or more bottleneck models to generate a second embedding with fewer dimensions than the first embedding. In various implementations, the second embedding may be processed in various ways to train one or more of the bottleneck model(s) based on various different auxiliary loss functions.

Claims20

Dependent claims

A method implemented using one or more processors and comprising:

providing an initial representation in a first domain of a transition from an initial state of an environment to a goal state of the environment;
processing the initial representation in the first domain based on a pre-trained first domain encoder to generate a first embedding that semantically represents the transition;
processing the first embedding based on one or more bottleneck models to generate a second embedding with fewer dimensions than the first embedding, wherein the second embedding also semantically represents the transition;
processing the second embedding using a pre-trained second domain decoder to generate a first predicted representation of the transition from the initial state of the environment to the goal state of the environment, wherein the first predicted representation is in the second domain;
processing the first predicted representation using a pre-trained second domain encoder to generate a third embedding that semantically represents the transition;
based on the third embedding, generating a second predicted representation of the transition from the initial state of the environment to the goal state, wherein the second predicted representation is in the first domain;
comparing one or more features of the second predicted representation of the transition to one or more features of the initial representation of the transition;
and
training one or more of the bottleneck models based on the comparing.

6 more elements

A method implemented using one or more processors and comprising:

providing an initial representation in a first domain of a transition from an initial state of an environment to a goal state of the environment;
processing the initial representation in the first domain based on a pre-trained first domain encoder to generate a first embedding that semantically represents the transition;
processing the first embedding based on one or more bottleneck models to generate a second embedding with fewer dimensions than the first embedding;
processing the second embedding using a pre-trained decoder for a second domain to generate a first predicted representation of the transition from the initial state of the environment to the goal state of the environment, wherein the first predicted representation is in the second domain;
processing the first predicted representation using a pre-trained second domain encoder to generate a third embedding that semantically represents the transition;
processing the third embedding based on one or more of the bottleneck models to generate a fourth embedding with fewer dimensions than the third embedding;
translating the second and fourth embeddings into a third domain that is different from the first and second domains to generate, respectively, first and second representations in the third domain of the transition from the initial state of the environment to the goal state of the environment;
and
based on a comparison of the first and second representations in the third domain, training one or more of the bottleneck models.

6 more elements

A method implemented using one or more processors, comprising:

providing an initial representation in a first domain of a transition from an initial state of an environment to a goal state of the environment;
processing the initial representation in the first domain based on a pre-trained first domain encoder to generate a first transferable representation that semantically represents the transition;
processing the first transferable representation based on a bottleneck model to generate a second transferable representation with fewer dimensions than the first transferable representation, wherein the second transferable representation also semantically represents the transition;
processing the second transferable representation using a pre-trained second domain decoder to generate a first predicted representation of the transition from the initial state of the environment to the goal state of the environment, wherein the first predicted representation is in the second domain;
processing the first predicted representation using a pre-trained second domain encoder to generate a third transferable representation that semantically represents the transition;
based on the third transferable representation, generating a second predicted representation of the transition from the initial state of the environment to the goal state, wherein the second predicted representation is in the first domain;
comparing one or more features of the second predicted representation of the transition to one or more features of the initial representation of the transition;
and
training the bottleneck model based on the comparing.

6 more elements

Dependencies are inferred from the claim text.