Abstract: An aspect of the disclosed technology comprises a test-time adaptation (“TTA”) technique for visual document understanding (“VDU”) tasks that uses self-supervised learning on different modalities (e.g., text and layout) by applying masked visual language modeling (“MVLM”) along with pseudo-labeling. In accordance with an aspect of the disclosed technology, the TTA technique enables a document model to adapt to domain or distribution shifts that are detected.

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.

Abstract: A method for rehearsal-free continual learning includes obtaining a set of training samples where training sample in the set of training samples is associated with a respective task of a plurality of different tasks. The method includes obtaining a task-invariant prompt representative of learned knowledge common to each respective task of the plurality of different tasks. The method includes, for each respective task of the plurality of different tasks, obtaining a respective task-specific prompt representative of learned knowledge specific to the respective task. The method includes, during each of one or more training iterations, for each respective training sample in the set of training samples, selecting the respective task-specific prompt representative of the respective task of the respective training sample and training a model using the task-invariant prompt and the selected respective task-specific prompt.

Abstract: Aspects of the disclosure provide for self-adapting forecasting (SAF) during the training and execution of machine learning models trained for multi-horizon forecasting on time-series data. The distribution of time-series data can shift over different periods of time. A deep neural network and other types of machine learning models are trained assuming that training data is independent and identically distributed (i.i.d.). With a computer system configured to execute SAF, the system can, at inference time, update a trained encoder to generate an encoded representation of time-series data capturing features characterizing the current distribution of the input time-series data. The updated encoded representation can be fed into a decoder trained to generate a multi-horizon forecast based on the updated encoded representation of the time-series data. At each instance of inference, the base weights of a trained model can be reused and updated to generate an updated encoded representation for that instance.

Abstract: Methods, systems, and apparatus, including computer storage media, for performing multi-horizon forecasting on time-series data. A method includes determining short-term temporal characteristics for respective forecasting horizons of one or more time-steps. The determining can include generating, using RNN encoders, encoder vectors based on static covariates, and time-varying input data; and predicting using one or more RNN decoders, a short-term pattern for a respective future time period. The method can also include capturing long-term temporal characteristics for the respective forecasting horizons based on the static covariates, the time-varying input data captured during the respective past time-periods, and the time-varying known future input data.

Abstract: A method includes receiving image data including a series of image patches of an image. The method includes generating, using a first set of transformers of a vision transformer (V-T) model, a first set of higher order feature representations based on the series of image patches and aggregating the first set of higher order feature representations into a second set of higher order feature representations that is smaller than the first set. The method includes generating, using a second set of transformers of the V-T model, a third set of higher order feature representations based on the second set of higher order feature representations and aggregating the third set of higher order feature representations into a fourth set of higher order feature representations that is smaller than the third set. The method includes generating, using the V-T model, an image classification of the image based on the fourth set.

Abstract: The present disclosure provides a method to integrate prior knowledge (referred to as rules) into deep learning in a way that can be controllable at inference without retraining or tuning the model. Deep Neural Networks with Controllable Rule Representations (DNN-CRR) incorporate a rule encoder into the model architecture, which is coupled with a corresponding rule-based objective for enabling a shared representation to be used in decision making by learning both the original task and the rule. DNN-CRR is agnostic to data type and encoder architecture and can be applied to any kind of rule defined for inputs and/or outputs. In real-world domains where incorporating rules is critical, such as prediction tasks in Physics, Retail, and Healthcare.

Abstract: In embodiments of the present invention improved capabilities are described for a content aggregation ranking facility adapted to rank a plurality of web-based content aggregations based on a search term, where each web-based content aggregation is comprised of a plurality of visual web-linked content comprising an image that is linked to a uniform resource locator (URL), and where the ranking may be determined based, at least in part, via determining a correlation between the search term and a characteristic of the plurality of web-based content aggregations, and ranking the plurality of web-based content aggregations based the strength of the that correlation.

Abstract: This document discloses a solution for detecting human facial micro-expressions automatically by a video analysis system. Facial micro-expressions are involuntary expressions having a very short duration.

Abstract: This document discloses a solution for detecting human facial micro-expressions automatically by a video analysis system. Facial micro-expressions are involuntary expressions having a very short duration.