Abstract: Systems and methods for performing one or more prediction task. The methods comprise: receiving a hierarchical set of encoder machine learning models configured to convert one or more tabular datasets into a token that represents the one or more tabular datasets; pre-training the hierarchical set of encoder machine learning models using self-supervised learning to generate a pre-trained hierarchical set of encoder machine learning models; fine-tuning a predictive model to perform one or more prediction task using supervised learning, the predictive model comprising a prediction head for each prediction task, each prediction head configured to generate a prediction for the corresponding prediction task based on the token generated by the pre-trained hierarchical set of encoder machine learning models; and processing one or more new tabular datasets using the fine-tuned predictive model to generate a prediction for each prediction task.

Abstract: Systems and methods for performing one or more prediction task. The methods comprise: receiving a plurality of tabular datasets comprising data related to a same historical time period; processing the tabular datasets using a hierarchical set of encoder machine learning models to generate a token that represents the plurality of tabular datasets; and processing the token using a predictor machine learning model to generate a predicted target for each of the one or more prediction task. The hierarchical set of encoder machine learning may comprise: a plurality of first layer encoder machine learning models configured to generate, for each tabular dataset in the plurality of tabular datasets, one or more intermediate token that represents that tabular dataset; and a second layer encoder machine learning model that is configured to generate the token from the intermediate tokens generated by the first layer encoder machine learning models.

Abstract: Systems and methods for performing one or more prediction task. The methods comprise: receiving a plurality of tabular datasets comprising data related to a same historical time period; processing the tabular datasets using a hierarchical set of encoder machine learning models to generate a token that represents the plurality of tabular datasets; and processing the token using a predictor machine learning model to generate a predicted target for each of the one or more prediction task. The hierarchical set of encoder machine learning may comprise: a plurality of first layer encoder machine learning models configured to generate, for each tabular dataset in the plurality of tabular datasets, one or more intermediate token that represents that tabular dataset; and a second layer encoder machine learning model that is configured to generate the token from the intermediate tokens generated by the first layer encoder machine learning models.

Abstract: Systems and methods for performing one or more prediction task. The methods comprise: receiving a plurality of tabular datasets, each tabular dataset of the plurality of tabular datasets comprising data related to a same historical time period; processing the plurality of tabular datasets using a plurality of encoder machine learning models to generate, for each tabular dataset in the plurality of tabular datasets, one or more intermediate token that represents that tabular dataset; processing the one or more intermediate token generated by the plurality of encoder machine learning models using a transformer model to generate a token that represents the plurality of tabular datasets; and using the token to perform the one or more prediction task.

Abstract: A data processing system comprising: inputting a tiled image of a vehicle including four different angle views of the vehicle combined into a single image to a first machine learning model (e.g. CNN), the model trained based on historical image data to predict a first likelihood of total loss vehicle; inputting a multi-fusion of images each into a second set of machine learning models; the multi-fusion of images including a set of separate and distinct images for each of the views input separately into the second set of machine learning models, and extracting features to predict a second likelihood of total loss vehicle; inputting tabular data relating to the vehicle into a third machine learning model to predict a third likelihood of total loss vehicle for the vehicle; and aggregating the first, second and third likelihood of total loss vehicle to determine the overall likelihood of total loss.

Abstract: An example operation may include at least one of receiving, from a source dataset, a plurality of labeled images, receiving, from a target dataset, a plurality of images associated with a different visual domain, extracting, from each of the plurality of labeled images and each of the plurality of images from the target dataset, one or more feature representations indicative of visual characteristics, grouping the plurality of labeled images into a plurality of image clusters based on similarity among the one or more feature representations, comparing the one or more feature representations of each of the plurality of image clusters to the one or more feature representations of the plurality of images from the target dataset to determine a similarity ranking for each image cluster, selecting, from the plurality of image clusters, a subset of labeled images based on the similarity ranking and a selection limit, and storing the subset of labeled images in a memory.

Abstract: An example operation may include one or more of retrieving an annotated source dataset from a storage via a software application, retrieving a non-annotated target dataset from the storage via the software application, identifying a subset of data from the annotated source dataset, wherein the subset is configured to include source dataset data that is similar to the non-annotated target dataset, reducing the subset of data from the annotated source dataset by using a classifier to remove redundant data from the subset of data from the annotated source dataset, and classifying data from the non-annotated target dataset by a trained artificial intelligence (AI) model.

Abstract: An example operation may include at least one of determining, by a transformer encoder trained on annotated image-text data, first latents for a first dataset stored in a memory, and second latents for a second dataset stored in the memory, generating a similarity matrix based on comparisons between the first latents and the second latents, constructing a graph comprising nodes corresponding to the first latents and edges based on pairwise similarity exceeding a threshold, identifying connected components in the graph and selecting, from each component, at least one latent having a highest score from a classifier trained to approximate divergence between the first dataset and the second dataset, forming a reduced dataset comprising the at least one latent, providing the reduced dataset to a model training module, and training an image classifier using the reduced dataset and the second dataset.

Abstract: An example operation may include at least one of converting an annotated dataset loaded from a storage into a first set of latents, converting a non-annotated dataset loaded from the storage into a second set of latents creating an aligned subset of data from the annotated dataset comprising: clustering the first set of latents into a plurality of clusters, determining a discrepancy score for each cluster in the plurality of clusters and the second set of latents, creating a refined subset of data from the annotated dataset by including at least one data from each cluster of the plurality of clusters, wherein adding the at least one data lowers the discrepancy score of the refined subset of data and the second set of latents, determining a similarity score between latents in the first set of latents and the second set of latents, wherein the aligned subset of data is created from the annotated dataset by parsing the refined subset into pairs of latents and for each of the pairs of latents, including a latent

Abstract: A data processing system comprising: inputting a tiled image of a vehicle including four different angle views of the vehicle combined into a single image to a first machine learning model (e.g. CNN), the model trained based on historical image data to predict a first likelihood of total loss vehicle; inputting a multi-fusion of images each into a second set of machine learning models; the multi-fusion of images including a set of separate and distinct images for each of the views input separately into the second set of machine learning models, and extracting features to predict a second likelihood of total loss vehicle; inputting tabular data relating to the vehicle into a third machine learning model to predict a third likelihood of total loss vehicle for the vehicle; and aggregating the first, second and third likelihood of total loss vehicle to determine the overall likelihood of total loss.

Abstract: A data processing system comprising: inputting a tiled image of a vehicle including four different angle views of the vehicle combined into a single image to a first machine learning model (e.g. CNN), the model trained based on historical image data to predict a first likelihood of total loss vehicle; inputting a multi-fusion of images each into a second set of machine learning models; the multi-fusion of images including a set of separate and distinct images for each of the views input separately into the second set of machine learning models, and extracting features to predict a second likelihood of total loss vehicle; inputting tabular data relating to the vehicle into a third machine learning model to predict a third likelihood of total loss vehicle for the vehicle; and aggregating the first, second and third likelihood of total loss vehicle to determine the overall likelihood of total loss.

Abstract: A data processing system comprising: inputting a tiled image of a vehicle including four different angle views of the vehicle combined into a single image to a first machine learning model (e.g. CNN), the model trained based on historical image data to predict a first likelihood of total loss vehicle; inputting a multi-fusion of images each into a second set of machine learning models; the multi-fusion of images including a set of separate and distinct images for each of the views input separately into the second set of machine learning models, and extracting features to predict a second likelihood of total loss vehicle; inputting tabular data relating to the vehicle into a third machine learning model to predict a third likelihood of total loss vehicle for the vehicle; and aggregating the first, second and third likelihood of total loss vehicle to determine the overall likelihood of total loss.