Patents by Inventor Devin T. WILLMOTT

Devin T. WILLMOTT has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20220172061
    Abstract: 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.
    Type: Application
    Filed: November 30, 2020
    Publication date: June 2, 2022
    Inventors: Devin T. WILLMOTT, Anit Kumar SAHU, Fatemeh SHEIKHOLESLAMI, Filipe J. CABRITA CONDESSA, Jeremy KOLTER
  • Publication number: 20220172372
    Abstract: 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.
    Type: Application
    Filed: November 30, 2020
    Publication date: June 2, 2022
    Inventors: Devin T. WILLMOTT, Chirag PABBARAJU, Po-Wei WANG, Jeremy KOLTER
  • Publication number: 20210383234
    Abstract: 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.
    Type: Application
    Filed: June 8, 2020
    Publication date: December 9, 2021
    Inventors: Shaojie BAI, Jeremy KOLTER, Vladlen KOLTUN, Devin T. WILLMOTT
  • Publication number: 20210374549
    Abstract: 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 meta-learned, evolution strategy black box 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 black box optimization 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 meta-learned, evolution strategy black box optimization classifier in response to a characteristic of the loss function.
    Type: Application
    Filed: May 29, 2020
    Publication date: December 2, 2021
    Inventors: Devin T. WILLMOTT, Christian DANIEL, Jeremy KOLTER