Robert Boschgmbh

Carnegie Mellon University

C3.ai, Inc.

Jeremy Zico Kolter

Filipe J Cabrita Condessa

Devin T Willmott

Henrik Ohlsson

Anit Kumar Sahu

Wan-Yi Lin

Mehdi Maasoumy Haghighi

Po-Wei Wang

Nikhil Krishnan

Kuenley Chiu

Frank R Schmidt

Fatemeh Sheikholeslami

Eric Wong

Vladlen Koltun

Jan Hendrik Metzen

Umashankar Sandilya

Shaojie Bai

Csaba Domokos

Rizal Fathony

Filipe Cabrita Condessa

Giuseppe Barbaro

Huan Zhang

Jonathan Mailoa

Mordechai C Kornbluth

Kenneth Wojciechowski

Thomas M Siebel

Edward Y Abbo

Houman Behzadi

Julia Vinogradska

João D Semedo

Christian Daniel

Joseph Christopher Szurley

Sayyed Mahdi Kashmiri

Frank Schmidt

Mohammad Sadegh Norouzzadeh

Daria Stepanova

Efthymios Papageorgiou

Louis Poirier

Avid Boustani

Yiding Jiang

Karren Yang

Ezra Maurice Winston

Chirag Pabbaraju

Priya L Donti

Mario Berges

Mehdi Maasoumy

Manash Pratim

Gaurav Manek

Rajshekhar Das

Jinghao Shi

Xiao Zhang

Jan Hendrick Metzen

Billy Li

Frank Rüdiger Schmidt

Brandon David Amos

Christina Baek

Filipe De Avila Belbute-Peres

Aditi Raghunathan

Thomas D Economon

Asher Trockman

Pratyush Maini

Leslie Rice

Satya Narayan Shukla

Leonid Boytsov

Mingjie Sun

Henning Lange

Ali Lotfi Rezaabad

A method of identifying an attack comprising receiving an input of one or more images, wherein the one or more images includes a patch size and size, divide the image into a first sub-image and a second sub-image, classify the first sub-image and the second sub-image, wherein classifying is accomplished via introducing a variable in a pixel location associated with the first and second sub-image, and in response to classifying the first and second sub-image and identifying an adversarial patch, output a notification indicating that the input is not certified.

Method and system of crown based for adversarial attacks

The disclosed technology can acquire a first set of data from a first group of data sources including a plurality of network components within an energy delivery network. A first metric indicating a likelihood that a particular network component, from the plurality of network components, is affected by cyber vulnerabilities can be generated based on the first set of data. A second set of data can be acquired from a second group of data sources including a collection of services associated with the energy delivery network. A second metric indicating a calculated impact on at least a portion of the energy delivery network when the cyber vulnerabilities affect the particular network component can be generated based on the second set of data. A third metric indicating an overall level of cybersecurity risk associated with the particular network component can be generated based on the first metric and the second metric.

Systems and methods for providing cybersecurity analysis based on operational techniques and information technologies

A computer-implemented method for training a machine learning network includes receiving an input data from one or more sensors, selecting one or more batch samples from the input data, wherein the batch samples include one or more perturbed samples from a source class configured to be misclassified into a target class, identifying the one or more perturbed samples from the one or more batch samples, determining a trigger event in response to identification of a trigger pattern of the one or more batch samples, wherein the trigger pattern induces a pre-determined response on a classifier, outputting a classification in response to identification of the trigger pattern via the classifier, and outputting a set of trigger patterns extracted from the machine-learning network.

Method and system for breaking backdoored classifiers through adversarial examples

A computer-implemented method for training a machine-learning network includes receiving an input data from a sensor, wherein the input data includes a perturbation, wherein the input data is indicative of image, radar, sonar, or sound information, obtain a worst-case bound on a classification error and loss for perturbed versions of the input data, utilizing at least bounding of one or more hidden layer values, in response to the input data, train a classifier, wherein the classifier includes a plurality of classes, including an additional abstain class, wherein the abstain class is determined in response to at least bounding the input data, outputting a classification in response to the input data, and output a trained classifier configured to detect the additional abstain class in response to the input data classifier with a plurality of classes, including an additional abstain class.

Method and system for probably robust classification with detection of adversarial examples

A system includes a machine-learning network. The network includes an input interface configured to receive input data from a sensor. The processor is programmed to receive the input data, generate a perturbed input data set utilize the input data, wherein the perturbed input data set includes perturbations of the input data, denoise the perturbed input data set utilizing a denoiser, wherein the denoiser is configured to generate a denoised data set, send the denoised data set to both a pre-trained classifier and a rejector, wherein the pre-trained classifier is configured to classify the denoised data set and the rejector is configured to reject a classification of the denoised data set, train, utilizing the denoised input data set, the a rejector to achieve a trained rejector, and in response to obtaining the trained rejector, output an abstain classification associated with the input data, wherein the abstain classification is ignored for classification.

System and method for improving robustness of pretrained systems in deep neural networks utilizing randomization and sample rejection

Methods and systems of estimating an accuracy of a neural network on out-of-distribution data. In-distribution accuracies of a plurality of machine learning models trained with in-distribution data are determined. The plurality of machine learning models includes a first model, and a remainder of models. In-distribution agreement is determined between (i) an output of the first machine learning model executed with an in-distribution dataset and (ii) outputs of a remainder of the plurality of machine learning models executed with the in-distribution dataset. The machine learning models are also executed with an unlabeled out-of-distribution dataset, and an out-of-distribution agreement is determined. The in-distribution agreement is compared with the out-of-distribution agreement. Based on a result of the comparison being within a threshold, an accuracy of the first machine learning model on the unlabeled out-of-distribution dataset is estimated based on (i) the in-distribution accuracies, (ii) the in-distribution agreement, and (iii) the out-of-distribution agreement.

Performance of neural networks under distribution shift

Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to identify a set of features associated with at least one of a collection of residences or an energy billing period. Measured energy consumption information and a plurality of feature values can be acquired for each residence in the collection of residences. Each feature value in the plurality of feature values can correspond to a respective feature in the set of features. A regression model can be trained based on the measured energy consumption information and the plurality of features values for each residence in the collection of residences. At least one expected consumption value and at least one efficient consumption value can be determined based on the regression model.

Systems and methods for regression-based determination of expected energy consumption and efficient energy consumption

A linguistic system includes a controller. The controller may be configured to receive a query and document, tokenize the query into a sequence of query tokens and tokenize the document into a sequence of document tokens, generate a matrix of token pairs for each of the query and the document tokens, retrieve for each entry in the matrix of token pairs, a precomputed similarity score produced by a neural conditional translation probability network, wherein the neural network has been trained in a ranking task using a corpus of paired queries and respective relevant documents, produce a ranking score for each document with respect to each query via a product-of-sum aggregation of each of the similarity scores for the respective query; and output the document and associated ranking score of the document.

Interaction layer neural network for search, retrieval, and ranking

A computer-implemented method for training a machine-learning network, wherein the network includes receiving an input data from a sensor, wherein the input data includes data indicative of an image, wherein the sensor includes a video, radar, LiDAR, sound, sonar, ultrasonic, motion, or thermal imaging sensor, generating an adversarial version of the input data utilizing an optimizer, wherein the adversarial version of the input data utilizes a subset of the input data, parameters associated with the optimizer, and one or more perturbation tiles, determining loss function value in response to the adversarial version of the input data and a classification of the adversarial version of the input data, determining a perturbation tile in response the loss function value associated with one or more subsets of the adversarial version of the input data, and output a perturbation that includes at least the perturbation tile.

Method and system for low-query black-box universal attacks

A computational method for training a meta-learned, evolution strategy black box optimization classifier. The method includes receiving one or more training functions and one or more initial meta-learning parameters of the optimization classifier. The method further includes sampling a sampled objective function from the one or more training functions and an initial mean of the sampled function. The method also includes computing a set of T number of means by running the meta learned, evolution strategy classifier on the sampled objective function using the initial mean for T number of steps in t=1, . . . , T. The method also includes computing a loss function from the set of T number of means. The method further includes updating the one or more initial meta-learning parameters of the optimization classifier in response to a characteristic of the loss function.

Meta-learned, evolution strategy black box optimization classifiers

A computer-implemented method for training a classifier, particularly a binary classifier, for classifying input signals to optimize performance according to a non-decomposable metric that measures an alignment between classifications corresponding to input signals of a set of training data and corresponding predicted classifications of the input signals obtained from the classifier. The method includes providing weighting factors that characterize how the non-decomposable metric depends on a plurality of terms from a confusion matrix of the classifications and the predicted classifications, and training the classifier depending on the provided weighting factors.

Device and method for training a classifier

A convolutional neural network system includes a sensor and a controller, wherein the controller is configured to receive an image from the sensor, divide the image into patches, each patch of size p, extract, via a first convolutional layer, a feature map having a number of channels based on a feature detector of size p, wherein the feature detector has a stride equal to size p, refine the feature map by alternatingly applying depth-wise convolutional layers and point-wise convolutional layers to obtain a refined feature map, wherein the number of channels in the feature map and the size of the feature map remains constant throughout all operations in the refinement; and output the refined feature map.

Image processing via isotonic convolutional neural networks

A multimodal perception system for an autonomous vehicle includes a first sensor that is one of a video, RADAR, LIDAR, or ultrasound sensor, and a controller. The controller may be configured to, receive a first signal from a first sensor, a second signal from a second sensor, and a third signal from a third sensor, extract a first feature vector from the first signal, extract a second feature vector from the second signal, extract a third feature vector from the third signal, determine an odd-one-out vector from the first, second, and third feature vectors via an odd-one-out network of a machine learning network, based on inconsistent modality prediction, fuse the first, second, and third feature vectors and odd-one-out vector into a fused feature vector, output the fused feature vector, and control the autonomous vehicle based on the fused feature vector.

Defending multimodal fusion models against single-source adversaries

A system and computer-implemented method are provided for training a dynamics model to learn the dynamics of a physical system. The dynamics model may be learned to be able to infer a future state of the physical system and/or its environment based on a current state of the physical system and/or its environment. The learned dynamics model is inherently globally stable. Instead of learning a dynamics model and attempting to separately verify its stability, the learnable dynamics model comprises a learnable Lyapunov function which is jointly learned together with the nominal dynamics of the physical system. The learned dynamics model is highly suitable for real-life applications in which a physical system may assume a state which was unseen during training as the learned dynamics model is inherently globally stable.

Dynamics model for globally stable modeling of system dynamics

Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating toa plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.

Systems and methods for utilizing machine learning to identify non-technical loss

Growing community of inventors

Pittsburgh, PA, United States of America

Jeremy Zico Kolter