Abstract: Methods and server systems for generating task-agnostic representations for nodes in bipartite graph are described herein. Method performed by server system includes accessing bipartite graph including first set of nodes and second set of nodes. Herein, set of edges exist between first and second set of nodes. Method includes performing for each node of first and second set of nodes: identifying a natural neighbor node, the natural neighbor node being a two-hop neighbor node from the each node, Then, generating temporary representation for one-hop neighbor node based on set of features corresponding to the one-hop neighbor node Then, generating temporary neighbor node based on temporary representation for the one-hop neighbor node. Then, generating augmented neighborhood based on the natural node and the temporary neighbor node, and then determining via machine learning model, task-agnostic representation for the each node based on augmented neighborhood.

Abstract: Methods and server systems for mitigating negative transfer in Multi-Task Learning (MTL) are described herein. Method performed by server system includes accessing training dataset and training multi-task machine learning (MTML) model based on performing operations. The operations includes initializing MTML model based on model parameters. Then, computing task affinity metric for each task of a set of tasks based on determining an affinity between each task and one or more tasks. Then, computing task-specific activation probability for each task based on task affinity metric corresponding to each task. Then, activating subset of tasks when task-specific activation probability corresponding to each individual task from the subset of tasks is lower than predefined threshold. Further, processing, via MTML model, training dataset by performing subset of tasks to compute outputs. Furthermore, generating task-specific losses for subset of tasks based on outputs and training dataset.

Abstract: Methods and systems for training artificial intelligence (AI)-based models using limited labeled data are disclosed. The method performed by a server system includes accessing a tabular dataset including tabular data that further labeled data and unlabeled data. Method includes generating labeled features including labeled numerical features and labeled categorical features based on the labeled data and generating unlabeled features including unlabeled numerical features and unlabeled categorical features based on the unlabeled data. Method includes determining, via a first transformer model, a contextual numerical embeddings based on the labeled numerical features and the unlabeled numerical features. Method includes determining, via a second transformer model, a contextual categorical embeddings based on the labeled categorical features and the unlabeled categorical features.

Abstract: Analytics system onboarding of web content is described. In one example, an analytics onboarding system is configured to process web content to generate recommendations, automatically and without user intervention. The recommendations are configured to assist in mapping of web content variables in web content to data elements supported by an analytics system to generate metrics that describe occurrence of events as part of user interaction with web content.

Abstract: One embodiment provides a method, including: receiving a training dataset to be utilized for training a deep-learning model; identifying a plurality of aspects of the training dataset, wherein each of the plurality of aspects corresponds to one of a plurality of categories of operations that can be performed on the training dataset; measuring, for each of the plurality of aspects, an amount of variance of the aspect within the training dataset; creating additional data to be incorporated into the training dataset, wherein the additional data comprise data generated for each of the aspects having a variance less than a predetermined amount, wherein the data generated for an aspect results in the corresponding aspect having an amount of variance at least equal to the predetermined amount; and incorporating the additional data into the training dataset.

Abstract: Analytics system onboarding of web content is described. In one example, an analytics onboarding system is configured to process web content to generate recommendations, automatically and without user intervention. The recommendations are configured to assist in mapping of web content variables in web content to data elements supported by an analytics system to generate metrics that describe occurrence of events as part of user interaction with web content.