Robert Boschgmbh

Carnegie Mellon University

Devin T Willmott

Jeremy Zico Kolter

Filipe J Cabrita Condessa

João D Semedo

Anit Kumar Sahu

Shaojie Bai

Wan-Yi Lin

Jeremy Zieg Kolter

Fatemeh Sheikholeslami

Jonathan Mailoa

Mordechai C Kornbluth

Vladlen Koltun

Christian Daniel

Po-Wei Wang

Robin Hutmacher

Filipe Condessa

Laura Beggel

Yiding Jiang

Filipe Cabrita Condessa

Rizal Fathony

Chirag Pabbaraju

Satya Narayan Shukla

Ivan Batalov

Jeffrey Thompson

Thomas D Economon

Yash Savani

Asher Trockman

Filipe De Avila Belbute-Peres

Dylan Jiang Sam

Victor Abayomi Akinwande

Nhung Thi Phuong Ngo

Jianghong Shi

Methods and systems for training a machine learning model with measurement data captured during a manufacturing process. Measurement data regarding a physical characteristic of a plurality of manufactured parts is received as measured by a plurality of sensors at various manufacturing stations. A time-series dynamics machine learning model encodes the measurement data into a latent space having a plurality of nodes. Each node is associated with the measurement data of one of the manufactured parts and at one of the manufacturing stations. A batch of the measurement data can be built, the batch include a first node and a first plurality of nodes immediately connected to the first node via first edges, and measured in time earlier than the first node. A prediction machine learning model can predict measurements of a first of the manufactured parts based on the latent space of the batch of nodes.

Methods and systems for training a machine learning model with measurement data captured during manufacturing process

A system includes a controller configured to generate an original patch utilizing Bayesian optimization, output the original patch at a display at a scene and determine if the original patch does not meet a success criteria of the machine-learning model, in response to the original patch not meeting the success criteria, upscaling the patch, decompose the upscaled patch into o components, for each of the components, utilize Bayesian optimization to update one of the components of the upscaled patch and freezing the other components to generate an updated patch, in response to the updated patch meeting the success criteria, output the updated upscaled patch, and in response to the updated upscaled patch not meeting the success criteria, iteratively update the unfrozen components and determine if the success criteria is met and if not met, unfreeze the frozen components and iteratively update the unfrozen components until the success criteria is met.

System and method for high-resolution blackbox patch attack with Bayesian optimization

A computer-implemented method that includes receiving a plurality of input images, generating a visual matrix utilizing the plurality of images and an image encoder, wherein the visual matrix includes a list of encoded images, receiving a plurality of text prompts, selecting a text prompt from the plurality of text prompts, send the first one of the text prompts to a language model to generate a candidate list of tokens, selecting tokens, converting the text prompts into updated text prompts via appending the tokens, generating a text matrix utilizing the text prompt and text encoder, and utilizing numerical values assigned at an image-text similarity matrix, determining a score associated with the image-text similarity matrix; and evaluating a criteria and outputting a final token to the updated text prompt in response to identifying a highest score associated with the final token after evaluating each of the plurality of text prompts.

System and method for prompt searching

A computer-implemented method for a machine learning (ML) system includes receiving a first image frame and a second frame from a sensor, wherein the first and second image frames are time series data, determining a first flow state and a first latent state of the first image frame, determining a Deep Equilibrium Model (DEQ) based fix point solution via a root finding method based on the first flow state, the first latent state, and a layer function to obtain an estimated flow and latent state, receiving a third image frame, wherein the second and third image frames are time series data, determining the fix point solution via the root finding method based on the estimated flow state, the estimated latent state, and layer function to obtain an updated flow state and updated latent state, and outputting the updated flow state.

Deep equilibrium flow estimation

Methods and systems are disclosed for generating training data for a machine learning model for better performance of the model. A source image is selected from an image database, along with a target image. An image segmenter is utilized with the source image to generate a source image segmentation mask having a foreground region and a background region. The same is performed with the target image to generate a target image segmentation mask having a foreground region and a background region. Foregrounds and backgrounds of the source image and target image are determined based on the masks. The target image foreground is removed from the target image, and the source image foreground is inserted into the target image to create an augmented image having the source image foreground and the target image background. The training data for the machine learning model is updated to include this augmented image.

Data augmentation for domain generalization

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 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 computer-implemented method for a classification and training a neural network includes receiving input at the neural network, wherein the input includes a plurality of resolution inputs of varying resolutions, outputting a plurality of feature tensors for each corresponding resolution of the plurality of resolution inputs, fusing the plurality of feature tensors utilizing upsampling or down sampling for the vary resolutions, utilizing an equilibrium solver to identify one or more prediction vectors from the plurality of feature tensors, and outputting a loss in response to the one or more prediction vectors.

System and method for multiscale deep equilibrium models

A system for controlling a physical system via segmentation of an image includes a controller. The controller may be configured to receive an image of n pixels from a first sensor, and an annotation of the image from a second sensor, form a coupling matrix, k class vectors each of length n, and a bias coefficient based on the image and the annotation, generate n pixel vectors each of length n based on the coupling matrix, class vectors, and bias coefficient create a single segmentation vector of length n from the pixel vectors wherein each entry in the segmentation vector identifies one of the k class vectors, output the single segmentation vector; and operate the physical system based on the single segmentation vector.

Image segmention via efficient semidefinate-programming based inference for binary and multi-class Markov Random Fields

A computer-implemented method includes applying a filter to input data based on an initial set of parameters to generate an initial feature map. The filter is configured to activate a filter function that involves a periodic function. The method includes performing a first linear transform on the initial feature map based on a subset of a first set of parameters to generate a first linear transform. The method includes applying the filter to the input data based on another subset of the first set of parameters to generate a first feature map. The method includes performing a multiplicative operation on the first linear transform and the first feature map to generate a first product. The method includes performing a second linear transform on the first product based on a subset of a second set of parameters to generate a second linear transform. The method includes generating output data that takes into account at least the second linear transform. The output data is a quantity of interest that is generated with high fidelity. The output data is specified by the input data over a space that defines the output data.

Multiplicative filter network

Performing an adversarial attack on a neural network classifier is described. A dataset of input-output pairs is constructed, each input element of the input-output pairs randomly chosen from a search space, each output element of the input-output pairs indicating a prediction output of the neural network classifier for the corresponding input element. A Gaussian process is utilized on the dataset of input-output pairs to optimize an acquisition function to find a best perturbation input element from the dataset. The best perturbation input element is upsampled to generate an upsampled best input element. The upsampled best input element is added to an original input to generate a candidate input. The neural network classifier is queried to determine a classifier prediction for the candidate input. A score for the classifier prediction is computed. The candidate input is accepted as a successful adversarial attack responsive to the classifier prediction being incorrect.

Bayesian-optimization-based query-efficient black-box adversarial attacks

A computer-implemented method includes receiving a coarse mesh input that includes a first set of nodes, wherein the coarse mesh is input to a computational fluid dynamics solver with physical parameters to obtain a coarse mesh solution, receiving a fine mesh input that is of a second set of nodes, wherein the second set of nodes includes more nodes than the first set of nodes, concatenating the fine mesh input with the physical parameters and run the concatenation through a graph convolution layer to obtain a fine mesh hidden layer, upsampling the coarse mesh solution to obtain a coarse mesh upsample including a same number of nodes as the second set of nodes, and outputting a prediction in response to at least the coarse mesh upsample.

System and method for combining differentiable partial differential equation solvers and graph neural networks for fluid flow prediction

A simulation includes converting a molecular dynamics snapshot of elements within a multi-element system into a graph with atoms as nodes of the graph; defining a matrix such that each column of the matrix represents a node in the graph; defining a distance matrix according to a set of relative positions of each of the atoms; iterating through the GTFF using an attention mechanism, operating on the matrix and augmented by incorporating the distance matrix, to pass hidden state from a current layer of the GTFF to a next layer of the GTFF; performing a combination over the columns of the matrix to produce a scalar molecular energy; making a backward pass through the GTFF, iteratively calculating derivatives at each of the layers of the GTFF to compute a prediction of force acting on each atom; and returning the prediction of the force acting on each atom.

Growing community of inventors

Pittsburgh, PA, United States of America

Devin T Willmott