Abstract: Systems, computer-implemented methods, and computer program products to facilitate automated model pipeline generation with entity monitoring, interaction, and/or intervention are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interaction backend handler component that provides a recommended input action corresponding to a model pipeline candidate being evaluated in an automated model pipeline generation process. The computer executable components can further comprise a visualization render component that renders an input visualization corresponding to the model pipeline candidate based on the recommended input action.

Abstract: A computer automatically selects a machine learning model pipeline using a meta-learning machine learning model. The computer receives ground truth data and pipeline preference metadata. The computer determines a group of pipelines appropriate for the ground truth data, and each of the pipelines includes an algorithm. The pipelines may include data preprocessing routines. The computer generates hyperparameter sets for the pipelines. The computer applies preprocessing routines to ground truth data to generate a group of preprocessed sets of said ground truth data and ranks hyperparameter set performance for each pipeline to establish a preferred set of hyperparameters for each of pipeline. The computer selects favored data features and applies each of the pipelines, with associated sets of preferred hyperparameters, to score the favored data features of the preprocessed ground truth data. The computer ranks pipeline performance and selects a candidate pipeline according to the ranking.

Abstract: Dual-modality relation networks for audio-visual event localization can be provided. A video feed for audio-visual event localization can be received. Based on a combination of extracted audio features and video features of the video feed, informative features and regions in the video feed can be determined by running a first neural network. Based on the informative features and regions in the video feed determined by the first neural network, relation-aware video features can be determined by running a second neural network. Based on the informative features and regions in the video feed, relation-aware audio features can be determined by running a third neural network. A dual-modality representation can be obtained based on the relation-aware video features and the relation-aware audio features by running a fourth neural network. The dual-modality representation can be input to a classifier to identity an audio-visual event in the video feed.

Abstract: A method comprises receiving a new data set; identifying at least one prior data set of a plurality of prior data sets that matches the new data set; generating a natural language data science problem statement for the new data set based on information associated with the at least prior one data set that matches the new data set; outputting the generated natural language data science problem statement for user verification; and in response to receiving user input verifying the natural language generated data science problem statement, generating one or more AutoAI configuration settings for the new data set based on one or more AutoAI configuration settings associated with the at least one prior data set that matches the new data set.

Abstract: Embodiments relate to a system, program product, and method for leveraging cognitive systems to facilitate the automated data table discovery for automated machine learning, and, more specifically, to leveraging a trained cognitive system to automatically search for additional data in an external data source that may be merged with an initial user-selected data table to generate a more robust machine learning model. Manual efforts to find and validate data appropriate for building and training a particular model for a particular task are significantly reduced. Specifically, a learning-based approach to leverage with machine learning models to automatically discover related datasets and join the datasets for a given initial dataset is disclosed herein. Operations that include dataset selection facilitate continued reinforcement learning of the systems.

Abstract: Techniques regarding one or more automated machine learning processes that analyze time series data are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a time series analysis component that selects a machine learning pipeline for meta transfer learning on time series data by sequentially allocating subsets of training data from the time series data amongst a plurality of machine learning pipeline candidates.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate visualization of a model selection process are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interaction backend handler component that obtains one or more assessment metrics of a model pipeline candidate. The computer executable components can further comprise a visualization render component that renders a progress visualization of the model pipeline candidate based on the one or more assessment metrics.

Abstract: Systems and methods provide for automated messaging summarization and ranking. The systems and methods may use an integrated machine learning model to perform thread detection, thread summarization, and summarization ranking. The messages may be received from a team chat application, organized, summarized and ranked by the machine learning model, and the results may be returned to the team chat application. In some cases, the ranking may be different for different users of the team chat application.

Abstract: Mechanisms are provided for generating an adversarial perturbation attack sensitivity (APAS) visualization. The mechanisms receive a natural input dataset and a corresponding adversarial attack input dataset, where the adversarial attack input dataset comprises perturbations intended to cause a misclassification by a computer model. The mechanisms determine a sensitivity measure of the computer model to the perturbations in the adversarial attack input dataset based on a processing of the natural input dataset and corresponding adversarial attack input dataset by the computer model. The mechanisms generate a classification activation map (CAM) for the computer model based on results of the processing and a sensitivity overlay based on the sensitivity measure. The sensitivity overlay graphically represents different classifications of perturbation sensitivities.

Abstract: Systems and techniques that facilitate few-shot temporal action localization based on graph convolutional networks are provided. In one or more embodiments, a graph component can generate a graph that models a support set of temporal action classifications. Nodes of the graph can correspond to respective temporal action classifications in the support set. Edges of the graph can correspond to similarities between the respective temporal action classifications. In various embodiments, a convolution component can perform a convolution on the graph, such that the nodes of the graph output respective matching scores indicating levels of match between the respective temporal action classifications and an action to be classified. In various embodiments, an instantiation component can input into the nodes respective input vectors based on a proposed feature vector representing the action to be classified.

Abstract: An embodiment of the invention may include a method, computer program product, and system for creating a data analysis tool. The method may include a computing device that generates an AI pipeline based on an input dataset, wherein the AI pipeline is generated using an Automated Machine Learning program. The method may include converting the AI pipeline to a non-native format of the Automated Machine Learning program. This may enable the AI pipeline to be used outside of the Automated Machine Learning program, thereby increasing the usefulness of the created program by not tying it to the Automated Machine Learning program. Additionally, this may increase the efficiency of running the AI pipeline by eliminating unnecessary computations performed by the Automated Machine Learning program.

Abstract: A sequence of frames of a video can be received. For a given frame in the sequence of frames, a gradient-embedded frame is generated corresponding to the given frame. The gradient-embedded frame incorporates motion information. The motion information can be represented as disturbance in the gradient-embedded frame. A plurality of such gradient-embedded frames can be generated corresponding to a plurality of the sequence of frames. Based on the plurality of gradient-embedded frames, a neural network such as a generative adversarial network is trained to learn to suppress the disturbance in the gradient-embedded frame and to generate a substitute frame. In inference stage, anomaly in a target video frame can be detected by comparing it to a corresponding substitute frame generated by the neural network.

Abstract: Systems and techniques that facilitate few-shot temporal action localization based on graph convolutional networks are provided. In one or more embodiments, a graph component can generate a graph that models a support set of temporal action classifications. Nodes of the graph can correspond to respective temporal action classifications in the support set. Edges of the graph can correspond to similarities between the respective temporal action classifications. In various embodiments, a convolution component can perform a convolution on the graph, such that the nodes of the graph output respective matching scores indicating levels of match between the respective temporal action classifications and an action to be classified. In various embodiments, an instantiation component can input into the nodes respective input vectors based on a proposed feature vector representing the action to be classified.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate conditional parallel coordinates in automated artificial intelligence with constraints are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a visualization component that renders a pipeline constraint as a constraint axis having constraint scores of machine learning pipelines in a conditional parallel coordinates visualization. The computer executable components can further comprise a model generation component that generates a machine learning model based on the constraint scores of the machine learning pipelines.

Abstract: In accordance with an embodiment of the invention, a method is provided for personalizing machine learning models for users of an automated machine learning system, the machine learning models being generated by an automated machine learning system. The method includes obtaining a first set of datasets for training first, second, and third neural networks, inputting the training datasets to the neural networks, tuning hyperparameters for the first, second, and third neural networks for testing and training the neural networks, inputting a second set of datasets to the trained neural networks and the third neural network generating a third output data including a relevance score for each of the users for each of the machine learning models, and displaying a list of machine learning models associated with each of the users, with each of the machine learning models showing the relevance score.

Abstract: Techniques regarding transferring learning outcomes across machine learning tasks in automated machine learning systems are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a transfer learning component that can executes a machine learning task using an existing artificial intelligence model on a sample dataset based on a similarity between the sample dataset and a historical dataset. The existing artificial intelligence model can be generated by automated machine learning and trained on the historical dataset.

Abstract: A deep learning module classifies messages received from a plurality of entities into one or more conversation threads. In response to receiving a subsequent message, the deep learning module determines which of the one or more conversation threads and a new conversation thread is contextually a best fit for the subsequent message. The subsequent message is added to the determined conversation thread.

Abstract: Method and apparatus that includes receiving a query describing an aspect in a video, the video including a plurality of frames, identifying multiple proposals that potentially correspond to the query where each of the proposals includes a subset of the plurality of frames, ranking the proposals using a graph convolution network that identifies relationships between the proposals, and selecting, based on the ranking, one of the proposals as a video segment that correlates to the query.

Abstract: A deep learning module classifies messages received from a plurality of entities into one or more conversation threads. In response to receiving a subsequent message, the deep learning module determines which of the one or more conversation threads and a new conversation thread is contextually a best fit for the subsequent message. The subsequent message is added to the determined conversation thread.

Abstract: Embodiments herein describe an audio forwarding regularizer and an information bottleneck that are used when training a machine learning (ML) system. The audio forwarding regularizer receives audio training data and identifies visually irrelevant and relevant sounds in the training data. By controlling the information bottleneck, the audio forwarding regularizer forwards data to a generator that is primarily related to the visually irrelevant sounds, while filtering out the visually relevant sounds. The generator also receives data regarding visual objects from a visual encoder derived from visual training data. Thus, when being trained, the generator receives data regarding the visual objects and data regarding the visually irrelevant sounds (but little to no data regarding the visually relevant sounds). Thus, during an execution stage, the generator can generate sounds that are relevant to the visual objects while not adding visually irrelevant sounds to the videos.