Patents by Inventor Filipe J. CABRITA CONDESSA

Filipe J. CABRITA CONDESSA 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: 20220100850
    Abstract: 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.
    Type: Application
    Filed: September 28, 2020
    Publication date: March 31, 2022
    Inventors: Mingjie SUN, Jeremy KOLTER, Filipe J. CABRITA CONDESSA
  • Publication number: 20220092466
    Abstract: A computer-implemented method for training a machine learning network. The method may include receiving an input data, selecting one or more batch samples from the input data, applying a perturbation object onto the one or more batch samples to create a perturbed sample, running the perturbed sample through the machine learning network, updating the perturbation object in response to the function in response to running the perturbed sample, and outputting the perturbation object in response to exceeding a convergence threshold.
    Type: Application
    Filed: September 20, 2020
    Publication date: March 24, 2022
    Inventors: Filipe J. CABRITA CONDESSA, Wan-Yi LIN, Karren YANG, Manash PRATIM
  • Patent number: 11231914
    Abstract: Community detection for a network using low-cardinality semidefinite programming is described. Data descriptive of a set of nodes of a network is received. The data includes a set of nodes representing entities of a system and edges representing connections between the entities of the system. A modularity maximization is performed to assign each node of the set of nodes to one or more communities to generate multiple-cardinality embeddings. The multiple-cardinality embeddings are rounded to unit cardinality to recover a community assignment with maximum modularity. The community assignment is refined and aggregated to create an aggregate network defining a set of connected communities of the network. The network is operated on in accordance with the community assignment.
    Type: Grant
    Filed: September 22, 2020
    Date of Patent: January 25, 2022
    Assignee: Robert Bosch GmbH
    Inventors: Po-Wei Wang, Jeremy Kolter, Filipe J. Cabrita Condessa
  • Publication number: 20210192335
    Abstract: A system and method is disclosed for labeling an unlabeled dataset, with a labeling budget constraint and noisy oracles (i.e. noisy labels provided by annotator), using a noisy labeled dataset from another domain or application. The system and method combine active learning with noisy labels and active learning with domain adaptation to enhance classification performance.
    Type: Application
    Filed: December 20, 2019
    Publication date: June 24, 2021
    Inventors: Rajshekhar DAS, Filipe J. CABRITA CONDESSA, Jeremy Zieg KOLTER
  • Publication number: 20210192386
    Abstract: A computational method for training a classifier. The method includes receiving a training data set comprised of pairs of training input and output signals, the classifier parameterized by parameters, a class-dependent allowed perturbation for each of at least two different classes and including a first class-dependent allowed perturbation for a first class and a second class-dependent allowed perturbation for a second class, and a loss function. The method further includes partitioning the training data set into a first subset labeled with a first label and a second subset labeled with a second label. The method also includes calculating a first loss in response to the first subset and the first class-dependent allowed perturbation and a second loss calculated in response to the second subset and the second class-dependent allowed perturbation. The method also includes updating the parameters in response to the first and second losses to obtain updated parameters.
    Type: Application
    Filed: December 20, 2019
    Publication date: June 24, 2021
    Inventors: Filipe J. CABRITA CONDESSA, Jeremy KOLTER
  • Publication number: 20210182731
    Abstract: For each generative model of a set of K generative models that classifies sensor data into K classes, in-distribution samples are sampled from training data as being classified as belonging to the class of the generative model and out-of-distribution samples are sampled from the training data as being classified as not belonging to the class of the generative model. Out-of-distribution samples are also generated from each remaining reciprocal generative model in the set of reciprocating generative models excluding the generative model to provide additional samples classified as not belonging to the class of the generative model. Parameters of the generative model are updated to minimize a loss function to maximize likelihood of the samples belonging to the class, and to maximize the loss function on both the sampled out-of-distribution samples and the generated out-of-distribution samples to minimize likelihood of the samples not belonging to the class.
    Type: Application
    Filed: December 13, 2019
    Publication date: June 17, 2021
    Inventors: Filipe J. CABRITA CONDESSA, Jeremy Z. KOLTER