Abstract: Embodiments are disclosed for a method. The method includes receiving an annotation set for a machine learning model. The annotation set includes multiple data points relevant to a task for the machine learning model. The method also includes determining total weights corresponding to the data points. The total weights are determined based on multiple ordering constraints indicating multiple data classes and corresponding weights. The corresponding weights represent a relative priority of the data classes with respect to each other. The method further includes generating an ordered annotation set from the annotation set. The ordered annotation set includes the data points in a sequence based on the determined total weights.

Abstract: Methods, systems, and computer program products for multi-modal construction of deep learning networks are provided herein. A computer-implemented method includes extracting, from user-provided multi-modal inputs, one or more items related to generating a deep learning network; generating a deep learning network model, wherein the generating includes inferring multiple details attributed to the deep learning network model based on the one or more extracted items; creating an intermediate representation based on the deep learning network model, wherein the intermediate representation includes (i) one or more items of data pertaining to the deep learning network model and (ii) one or more design details attributed to the deep learning network model; automatically converting the intermediate representation into source code; and outputting the source code to at least one user.

Abstract: A method, computer system, and a computer program product for debugging a deep neural network is provided. The present invention may include identifying, automatically, one or more debug layers associated with a deep learning (DL) model design/code, wherein the identified one or more debug layers include one or more errors, wherein a reverse operation is introduced for the identified one or more debug layers. The present invention may then include presenting, to a user, a debug output based on at least one break condition, wherein in response to determining the at least one break condition is satisfied, triggering the debug output to be presented to the user, wherein the presented debug output includes a fix for the identified one or more debug layers in the DL model design/code and at least one actionable insight.

Abstract: A system, method and computer readable medium are provided for implementing data free neural network pruning. The illustrative method include determining mutual information between outputs of two or more of the plurality neurons and a respective two or more inputs used to generate the outputs, the two or more neurons being activated as a result of synthetically created inputs for measuring entropy. The method includes determining a sparser neural network by pruning the plurality of neurons based on the determined mutual information.

Abstract: One embodiment provides a method, including: mining a plurality of deep-learning models from a plurality of input sources; extracting information from each of the deep-learning models, by parsing at least one of (i) code corresponding to the deep-learning model and (ii) text corresponding to the deep-learning model; identifying, for each of the deep-learning models, operators that perform operations within the deep-learning model; producing, for each of the deep-learning models and from (i) the extracted information and (ii) the identified operators, an ontology comprising terms and features of the deep-learning model, wherein the producing comprises populating a pre-defined ontology format with features of each deep-learning model; and generating a deep-learning model catalog comprising the plurality of deep-learning models, wherein the catalog comprises, for each of the deep-learning models, the ontology corresponding to the deep-learning model.

Abstract: Methods, systems, and computer program products for combined data pre-process and architecture search for deep learning models are provided herein. A computer-implemented method includes obtaining data corresponding to a deep learning task; performing, based on the deep learning task and the data, a multi-objective learning process to select an optimal combination of (i) a deep learning architecture for the deep learning task and (ii) a data pre-processing strategy to be applied to the data, the data pre-processing strategy comprising one or more pre-processing steps; pre-processing the data for the selected deep learning architecture based on the data pre-processing strategy; and providing the pre-processed data as input to the selected deep learning architecture to perform the deep learning task.

Abstract: Techniques for the suggestion and completion of deep learning models are disclosed including receiving a set of data and determining at least one property of the data. A plurality of characteristics of a computing device and a plurality of deep learning models are received and a score for each of the plurality of deep learning models is determined based on the received computing device characteristics and the determined at least one property of the data. The plurality of deep learning models are ranked for presentation to a user based on the determined scores. One or more of the deep learning models are presented on a display based on the ranking. A selection of one of the deep learning models is received and the selected deep learning model is trained using the set of data.

Abstract: Embodiments are disclosed for a method. The method includes receiving an annotation set for a machine learning model. The annotation set includes multiple data points relevant to a task for the machine learning model. The method also includes determining total weights corresponding to the data points. The total weights are determined based on multiple ordering constraints indicating multiple data classes and corresponding weights. The corresponding weights represent a relative priority of the data classes with respect to each other. The method further includes generating an ordered annotation set from the annotation set. The ordered annotation set includes the data points in a sequence based on the determined total weights.

Abstract: Methods, systems, and computer program products related to a cognitive assistant for co-generating creative content are provided herein. A computer-implemented method includes obtaining semantic-level inputs from at least one user, wherein the semantic-level inputs pertain to multiple aspects of a desired content narrative; generating textual content based at least in part on the semantic-level inputs, wherein said generating the textual content comprises applying one or more deep learning algorithms to the semantic-level inputs; generating image content based at least in part on the generated textual content; creating the desired content narrative by integrating (i) the generated textual content and (ii) the generated image content; and outputting the desired content narrative to the at least one user.

Abstract: One embodiment provides a method, including: obtaining, for each step in a food supply chain, information corresponding to extraneous factors, wherein the extraneous factors comprise factors that may affect quality of a food product within the food supply chain; generating a rating, for each step in the food supply chain, indicating a level of pollution to which the food product was exposed at the corresponding step, wherein the rating comprises (i) scoring each of the extraneous factors based upon a level of pollution identified from the extraneous factors and (ii) aggregating the scores for the extraneous factors to determining a rating; generating, using the rating for each of the steps, an aggregate food supply chain score for the food product, wherein the aggregate food supply chain indicates an impact of pollution across the food supply chain on the food product; and producing a quality rating for the food product.

Abstract: Systems and methods are provided to implement intelligent recommendations to users by modeling user profiles through deep learning of multimodal user data. For example, a recommendation computing platform collects multimodal user data from a computing device of a registered user, wherein the multimodal user data include time-series data, unstructured textual data, and multimedia data. A first deep learning classification engine is utilized to extract features from the multimodal user data. A second deep learning classification engine is utilized to generate a profile of the registered user based on the extracted features. A deep recommendation classification engine is utilized to determine a recommendation for the registered user based on the profile of the registered user, wherein the recommendation identifies at least one additional registered user. The recommendation is presented to the registered user on the computing device of the registered user.

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: Methods, systems, and computer program products for combined data pre-process and architecture search for deep learning models are provided herein. A computer-implemented method includes obtaining data corresponding to a deep learning task; performing, based on the deep learning task and the data, a multi-objective learning process to select an optimal combination of (i) a deep learning architecture for the deep learning task and (ii) a data pre-processing strategy to be applied to the data, the data pre-processing strategy comprising one or more pre-processing steps; pre-processing the data for the selected deep learning architecture based on the data pre-processing strategy; and providing the pre-processed data as input to the selected deep learning architecture to perform the deep learning task.

Abstract: One embodiment provides a method, comprising: identifying, using a processor, an individual in a video segment; ascertaining, from at least the video segment, a viewpoint expressed by the individual on a topic, wherein the viewpoint comprises at least one of: a stance of the individual and a sentiment of the individual toward the topic; identifying, using a processor, a superset video comprising the video segment, wherein the superset video is an originally published video; ascertaining, based at least in part on the superset video, an overarching viewpoint of the individual on the topic; determining whether an inconsistency exists between the viewpoint expressed by the individual in the video segment and the overarching viewpoint of the individual ascertained in the superset video; and alerting, responsive to determining that an inconsistency exists, a user that the video segment contains the inconsistency.

Abstract: One embodiment provides a method, including: mining a plurality of deep-learning models from a plurality of input sources; extracting information from each of the deep-learning models, by parsing at least one of (i) code corresponding to the deep-learning model and (ii) text corresponding to the deep-learning model; identifying, for each of the deep-learning models, operators that perform operations within the deep-learning model; producing, for each of the deep-learning models and from (i) the extracted information and (ii) the identified operators, an ontology comprising terms and features of the deep-learning model, wherein the producing comprises populating a pre-defined ontology format with features of each deep-learning model; and generating a deep-learning model catalog comprising the plurality of deep-learning models, wherein the catalog comprises, for each of the deep-learning models, the ontology corresponding to the deep-learning model.

Abstract: A method, computer system, and a computer program product for debugging a deep neural network is provided. The present invention may include identifying, automatically, one or more debug layers associated with a deep learning (DL) model design/code, wherein the identified one or more debug layers include one or more errors, wherein a reverse operation is introduced for the identified one or more debug layers. The present invention may then include presenting, to a user, a debug output based on at least one break condition, wherein in response to determining the at least one break condition is satisfied, triggering the debug output to be presented to the user, wherein the presented debug output includes a fix for the identified one or more debug layers in the DL model design/code and at least one actionable insight.

Abstract: One embodiment provides a method, including: obtaining, for each step in a food supply chain, information corresponding to extraneous factors, wherein the extraneous factors comprise factors that may affect quality of a food product within the food supply chain; generating a rating, for each step in the food supply chain, indicating a level of pollution to which the food product was exposed at the corresponding step, wherein the rating comprises (i) scoring each of the extraneous factors based upon a level of pollution identified from the extraneous factors and (ii) aggregating the scores for the extraneous factors to determining a rating; generating, using the rating for each of the steps, an aggregate food supply chain score for the food product, wherein the aggregate food supply chain indicates an impact of pollution across the food supply chain on the food product; and producing a quality rating for the food product.

Abstract: One embodiment provides a method, including: providing, at a collaborative deep learning model authoring tool, a dialog window that (i) receives user inputs discussing deep learning model aspects and (ii) provides recommendations from the collaborative deep learning model authoring tool; providing, at the collaborative deep learning model authoring tool, a consensus view indicating (i) a conflicting aspect identified as an aspect where more than one user selected a different aspect and (ii) the aspect selected for implementation within the deep learning model based upon that aspect having the most user selections; providing, at the collaborative deep learning model authoring tool, a model view displaying layers of the deep learning model based upon (i) aspects selected by the users in the dialog window and (ii) the aspect selected for implementation in the consensus view; and providing, at the collaborative deep learning model authoring tool, a deployment view that displays an execution of the deep learning m

Abstract: Methods, systems, and computer program products related to a cognitive assistant for co-generating creative content are provided herein. A computer-implemented method includes obtaining semantic-level inputs from at least one user, wherein the semantic-level inputs pertain to multiple aspects of a desired content narrative; generating textual content based at least in part on the semantic-level inputs, wherein said generating the textual content comprises applying one or more deep learning algorithms to the semantic-level inputs; generating image content based at least in part on the generated textual content; creating the desired content narrative by integrating (i) the generated textual content and (ii) the generated image content; and outputting the desired content narrative to the at least one user.

Abstract: Techniques for the suggestion and completion of deep learning models are disclosed including receiving a set of data and determining at least one property of the data. A plurality of characteristics of a computing device and a plurality of deep learning models are received and a score for each of the plurality of deep learning models is determined based on the received computing device characteristics and the determined at least one property of the data. The plurality of deep learning models are ranked for presentation to a user based on the determined scores. One or more of the deep learning models are presented on a display based on the ranking. A selection of one of the deep learning models is received and the selected deep learning model is trained using the set of data.