Robert Boschgmbh

Carnegie Mellon University

Yiding Jiang

Filipe J Cabrita Condessa

Wan-Yi Lin

Jeremy Zico Kolter

Devin T Willmott

Jeremy Zieg Kolter

João D Semedo

Chen Qiu

Christina Baek

Aditi Raghunathan

Hariharan Manikandan

Dylan Jiang Sam

Victor Abayomi Akinwande

A computer-implemented method that includes receiving a plurality of input images, generating a visual matrix utilizing the plurality of images and an image encoder, wherein the visual matrix includes a list of encoded images, receiving a plurality of text prompts, selecting a text prompt from the plurality of text prompts, send the first one of the text prompts to a language model to generate a candidate list of tokens, selecting tokens, converting the text prompts into updated text prompts via appending the tokens, generating a text matrix utilizing the text prompt and text encoder, and utilizing numerical values assigned at an image-text similarity matrix, determining a score associated with the image-text similarity matrix; and evaluating a criteria and outputting a final token to the updated text prompt in response to identifying a highest score associated with the final token after evaluating each of the plurality of text prompts.

System and method for prompt searching

A computer-implemented method includes converting tabular data to a text representation, generating metadata associated with the text representation of the tabular data, outputting one or more natural language data descriptions indicative of the tabular data in response to utilizing a large language model (LLM) and zero-shot prompting of the metadata and text representation of the tabular data, outputting one or more summaries utilizing the LLM and appending a prompt on the one or more natural language data descriptions, selecting a single summary of the one or more summaries in response to the single summary having a smallest validation rate, receiving a query associated with the tabular data, outputting one or more predictions associated with the query, and in response to meeting a convergence threshold with the one or more predictions generated from the one or more iterations, output a final prediction associated with the query.

System and method for utilizing weak learners on large language models

Methods and systems of estimating an accuracy of a neural network on out-of-distribution data. In-distribution accuracies of a plurality of machine learning models trained with in-distribution data are determined. The plurality of machine learning models includes a first model, and a remainder of models. In-distribution agreement is determined between (i) an output of the first machine learning model executed with an in-distribution dataset and (ii) outputs of a remainder of the plurality of machine learning models executed with the in-distribution dataset. The machine learning models are also executed with an unlabeled out-of-distribution dataset, and an out-of-distribution agreement is determined. The in-distribution agreement is compared with the out-of-distribution agreement. Based on a result of the comparison being within a threshold, an accuracy of the first machine learning model on the unlabeled out-of-distribution dataset is estimated based on (i) the in-distribution accuracies, (ii) the in-distribution agreement, and (iii) the out-of-distribution agreement.

Performance of neural networks under distribution shift

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Pittsburgh, PA, United States of America

Yiding Jiang