公开(公告)号：US20210089870A1

公开(公告)日：2021-03-25

申请号：US17026145

申请日：2020-09-18

Applicant: Google LLC

Inventor： Sercan Omer Arik ,  Jinsung Yoon ,  Tomas Jon Pfister

IPC: G06N3/04 ,  G06N3/08 ,  G06F17/16

Abstract: A method includes obtaining a set of training samples. During each of a plurality of training iterations, the method also includes sampling a batch of training samples from the set of training samples. The method includes, for each training sample in the batch of training samples, determining, using a data value estimator, a selection probability. The selection probability for the training sample is based on estimator parameter values of the data value estimator. The method also includes selecting, based on the selection probabilities of each training sample, a subset of training samples from the batch of training samples, and determining, using a predictor model with the subset of training samples, performance measurements. The method also includes adjusting model parameter values of the predictor model based on the performance measurements, and updating the estimator parameter values of the data value estimator based on the performance measurements.

公开(公告)号：US20240378224A1

公开(公告)日：2024-11-14

申请号：US18654696

申请日：2024-05-03

Applicant: Google LLC

Inventor： Jinsung Yoon ,  Sercan Omer Arik

IPC: G06F16/33 ,  G06F16/332

Abstract: Aspects of the disclosed technology include techniques and mechanisms for customizing large language models (LLMs) for information retrieval (IR). For a plurality of (query, corpus) pairs, an IR adapter may generate embeddings and adapted embeddings associated with each of the query and the corpus. The IR adapter may analyze the adapted embeddings using a similarity function to determine the similarity between the adapted embeddings. The output of the similarity function may be used to determine a correlation between the query and the corpus, wherein the correlation may be fed back into the IR adapter to train an LLM.

公开(公告)号：US20240249204A1

公开(公告)日：2024-07-25

申请号：US18419476

申请日：2024-01-22

Applicant: Google LLC

Inventor： Jinsung Yoon ,  Jiefeng Chen ,  Sayna Ebrahimi ,  Sercan Omer Arik

IPC: G06N20/20

CPC classification number: G06N20/20

Abstract: A method includes obtaining a set of unlabeled test data samples and, for each respective initial training step, determining a first average output for each unlabeled test data sample using a deep ensemble. For each round of a plurality of rounds, the method includes selecting a subset of unlabeled test data samples based on the determined first average outputs, labeling each respective unlabeled in the subset of unlabeled test data samples, fine-tuning the deep ensemble model using the subset of labeled test data samples, and determining a second average output for each unlabeled test data sample using the fine-tuned deep ensemble model. The method also includes generating, using the set of unlabeled test data samples and the determined second average outputs, a pseudo-labeled set of training data samples. The method also includes training the deep ensemble model using the pseudo-labeled set of training data samples.

公开(公告)号：US20230325675A1

公开(公告)日：2023-10-12

申请号：US18333301

申请日：2023-06-12

Applicant: Google LLC

Inventor： Sercan Omer Arik ,  Jinsung Yoon ,  Tomas Pfister

IPC: G06N3/084 ,  G06F17/16 ,  G06N3/08 ,  G06N3/047

CPC classification number: G06N3/084 ,  G06F17/16 ,  G06N3/08 ,  G06N3/047

Abstract: A method includes obtaining a batch of training samples. For each particular training sample in the batch of training samples, the method includes generating, using a data value estimator model and the particular training sample, a corresponding predicted value of the particular training sample when used to train a machine learning model. The method includes selecting, based on the corresponding predicted values, a subset of the batch of training samples. For each particular training sample in the subset of the batch of training samples, the method includes determining, using the machine learning model and the particular training sample, a corresponding prediction performance measurement. The method includes adjusting one or more estimator parameter values of the data value estimator model based on the corresponding prediction performance measurements.

公开(公告)号：US20230274154A1

公开(公告)日：2023-08-31

申请号：US18113267

申请日：2023-02-23

Applicant: Google LLC

Inventor： Jinsung Yoon ,  Sercan Omer Arik ,  Madeleine Richards Udell ,  Chun-Hao Chang

IPC: G06N3/09 ,  G06N3/088 ,  G06N3/0895

CPC classification number: G06N3/09 ,  G06N3/088 ,  G06N3/0895

Abstract: Aspects of the disclosure provide for interpretable anomaly detection using a generalized additive model (GAM) trained using unsupervised and supervised learning techniques. A GAM is adapted to detect anomalies using an anomaly detection partial identification (AD PID) loss function for handling noisy or heterogeneous features in model input. A semi-supervised data interpretable anomaly detection (DIAD) system can generate more accurate results over models trained for anomaly detection using strictly unsupervised techniques. In addition, output from the DIAD system includes explanations, for example as graphs or plots, of relatively important input features that contribute to the model output by different factors, providing interpretable results from which the DIAD system can be improved upon.

公开(公告)号：US20220391724A1

公开(公告)日：2022-12-08

申请号：US17825788

申请日：2022-05-26

Applicant: Google LLC

Inventor： Jinsung Yoon ,  Kihyuk Sohn ,  Chun-Liang Li ,  Sercan Omer Arik

IPC: G06N5/04 ,  G06F11/34

Abstract: Aspects of the disclosure provide for methods, systems, and apparatus, including computer-readable storage media, for anomaly detection using a machine learning framework trained entirely on unlabeled training data including both anomalous and non-anomalous training examples. A self-supervised one-class classifier (STOC) refines the training data to exclude anomalous training examples, using an ensemble of machine learning models. The ensemble of models are retrained on the refined training data. The STOC can also use the refined training data to train a representation learning model to generate one or more feature values for each training example, which can be processed by the trained ensemble of models and eventually used for training an output classifier model to predict whether input data is indicative of anomalous or non-anomalous data.

公开(公告)号：US20210089828A1

公开(公告)日：2021-03-25

申请号：US17030316

申请日：2020-09-23

Applicant: Google LLC

Inventor： Sercan Omer Arik ,  Jinsung Yoon ,  Tomas Jon Pfister

IPC: G06K9/62 ,  G06N3/02

Abstract: A method for training a locally interpretable model includes obtaining a set of training samples and training a black-box model using the set of training samples. The method also includes generating, using the trained black-box model and the set of training samples, a set of auxiliary training samples and training a baseline interpretable model using the set of auxiliary training samples. The method also includes training, using the set of auxiliary training samples and baseline interpretable model, an instance-wise weight estimator model. For each auxiliary training sample in the set of auxiliary training samples, the method also includes determining, using the trained instance-wise weight estimator model, a selection probability for the auxiliary training sample. The method also includes selecting, based on the selection probabilities, a subset of auxiliary training samples and training the locally interpretable model using the subset of auxiliary training samples.