International Business Machines Corporation

Ambrish Rawat

Mathieu Sinn

Ngoc Minh Tran

Martin Wistuba

Beat Buesser

Maria-Irina Nicolae

Killian Levacher

Sean A McKenna

Stefano Braghin

Fearghal O'Donncha

Joern Ploennigs

Yufang Hou

Amadou Ba

Jonathan P Epperlein

Debasis Ganguly

Marco Simioni

Martin Gleize

Mark Purcell

Fearghal O'donncha

Sharon Qian

Suwen Lin

Giulio Zizzo

A method, computer program, and computer system are provided for predicting and assessing risks on websites. Data corresponding to historical interactions of a user with one or more websites is accessed. A simulation of actions of the user is generated based on the accessed data, and actions of the user are simulated on a pre-defined target website based on the generated simulation of the actions of the user. Risks on the target website are identified based on simulating the actions of the user. The website is updated to mitigate the identified risks.

Federated generative models for website assessment

A method for additive manufacturing includes identifying a discrepancy between a three-dimensional model and an object model. The three-dimensional model is a model of a three-dimensional object that is being constructed by an additive manufacturing process, and the three-dimensional object is being constructed based on the object model. The method further includes determining a reconfiguration recommendation based on the identified discrepancy. The method further includes reconfiguring the additive manufacturing process based on the reconfiguration recommendation.

Real-time reconfiguration of additive manufacturing

One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to facilitating a process to compensate a service being provided over a network connection. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a determination component that determines a network connection between a server and a client node, and a predictive component that predicts, employing machine learning, a graphical representation update to a service provided by the server over the network connection. The predictive component can generate the prediction in response to a decrease in bandwidth and/or an increase in latency of a network connection. A training component can train a machine learning model employed by the predictive component based on historical data of the service provided by the server.

AI-based compensation of resource constrained communication

A system, computer program product, and method are presented for administering examinations with adversarial hardening of queries against automated responses. The method include receiving an original query electronically. A response to the original query is to be submitted electronically by a human. The method also includes modifying the original query, thereby generating a modified query. The modified query is configured to be comprehensible by the human, and not properly responded to through electronic means without human support.

Adversarial hardening of queries against automated responses

Adversarial attack detection operations may be applied on one or more deep generative models for defending deep generative models from adversarial attacks. The adversarial attack may be detected on the one or more deep generative models based on the one or more of a plurality of adversarial attack detection operations. The one or more deep generative models may be sanitized based on the adversarial attack.

Defending deep generative models against adversarial attacks

Techniques of facilitating deep learning model rescaling by computing devices. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise: a rescaling component; and a forecasting component. The rescaling component can determine a scaling ratio that maps low mesh resolution predictive data output by a partial differential equation (PDE)-based model for a sub-domain to high-resolution observational or ground-truth data for a domain comprising the sub-domain. The forecasting component can generate high mesh resolution predictive data for the domain with a machine-learning model using input data of the PDE-based model and the scaling ratio.

Superresolution and consistency constraints to scale up deep learning models

Methods, computer program products and/or systems are provided that perform the following operations: obtaining a collection of objects; constructing a weighted graph based on the collection of objections, wherein the weighted graph preserves neighborhood semantics of objects of the collection of objects; generating partitions of nodes in the weighted graph of a fixed maximum size utilizing combinatorial partitioning; generating a vector for each node based on the partitions of nodes in the weighted graph; determining vector representations for objects in this collection and eventually applying this vector representation of the objects to gain efficiency (e.g., in terms of computation time and memory requirements) for use in downstream tasks such as recommendation.

Discrete representation learning

A computing device configured for automatic selection of model parameters includes a processor and a memory coupled to the processor. The memory stores instructions to cause the processor to perform acts including providing an initial set of model parameters and initial condition information to a model based on historical data. A model generates data based on the model parameters and the initial condition information. After determining whether the model-generated data is similar to an observed data, updated model parameters are selected for input to the model based on the determined similarity.

System for continuous update of advection-diffusion models with adversarial networks

Various embodiments are provided for securing machine learning models by one or more processors in a computing system. One or more hardened machine learning models that are secured against adversarial attacks are provided by applying one or more of a plurality of combinations of selected preprocessing operations from one or more machine learning models, a data set used for hardening the one or more machine learning models, a list of preprocessors, and a selected number of learners.

Learning input preprocessing to harden machine learning models

Various embodiments are provided for automating decision making for a neural architecture search by one or more processors in a computing system. One or more specifications may be automatically selected for a dataset, tasks, and one or more constraints for a neural architecture search. The neural architecture search may be performed based on the one or more specifications. A deep learning model may be suggested, predicted, and/or configured for the dataset, the tasks, and the one or more constraints based on the neural architecture search.

Automated decision making for neural architecture search

One or more hardened machine learning models are secured against adversarial attacks by adding adversarial protection to one or more previously trained machine learning models. To generate the hardened machine learning models, the previously trained machine learning models are retrained and extended using preprocessing layers or using additional network layers which test model performance on benign or adversarial samples. A rollback strategy is additionally implemented to retain intermediate model states during the retraining to provide recovery if a training collapse is detected.

Adding adversarial robustness to trained machine learning models

Embodiments for providing adversarial protection to computing display devices by a processor. Security defenses may be provided on one or more image display devices against automated media analysis by using adversarial noise, an adversarial patch, or a combination thereof.

Providing adversarial protection for electronic screen displays

A method for protecting a machine learning model includes: generating a first adversarial example by modifying an original input using an attack tactic, wherein the model accurately classifies the original input but does not accurately classify at least the first adversarial example; training a defender to protect the model from the first adversarial example by updating a strategy of the defender based on predictive results from classifying the first adversarial example; updating the attack tactic based on the predictive results from classifying the first adversarial example; generating a second adversarial example by modifying the original input using the updated attack tactic, wherein the trained defender does not protect the model from the second adversarial example; and training the defender to protect the model from the second adversarial example by updating the at least one strategy of the defender based on results obtained from classifying the second adversarial example.

Protecting a machine learning model

Embodiments for providing adversarial protection of speech in audio signals by a processor. Security defenses on one or more audio devices may be provide against automated audio analysis of audio signals by using adversarial noise.

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Dublin, Ireland

Ambrish Rawat