Abstract: 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.

Abstract: A computer-implemented system and method relate to object discovery. The system and method include receiving a source image and generating input data by associating each pixel of the source image with predetermined phase values. An encoder encodes the input data to generate latent representation data in spherical coordinates. A decoder decodes the latent representation data to generate spherical reconstruction data of the source image. The spherical reconstruction data includes a radial component and a plurality of phase components. A reconstructed image is generated based at least on the radial component. The reconstructed image is a reconstruction of the source image.

Abstract: Disclosed embodiments include methods for evaluating augmented training elements. The augmented training elements may be generated using different augmentation techniques. Disclosed embodiments may generate training set useful for training a plurality of different machine-learning models.

Abstract: Disclosed embodiments use diffusion-based generative models for radar point cloud super-resolution. Disclosed embodiments use the mathematics of diffusion modeling to generate higher-resolution radar point cloud data from lower-resolution radar point cloud data.

Abstract: A computer-implemented system and method relate to certified robust defenses against adversarial patches. A set of one-mask images are generated using a source image and a first mask at a set of predetermined image regions. The set of predetermined image regions collectively cover at least every pixel of the source image. A particular one-mask image with a highest prediction loss is selected from among the set of one-mask images. A set of two-mask images is generated using the selected one-mask image and a second mask at the set of predetermined image regions. A particular two-mask image with a highest prediction loss is selected from among the set of two-mask images. The machine learning system is trained using a training dataset, which includes the selected two-mask image.

Abstract: A computer-implemented system and method relate to certified robust defenses against adversarial patches. A two-mask image is generated using a first mask and a second mask with respect to a source image. The two-mask image is associated with a highest prediction loss. A set of two-submask images are generated using a first submask and a second submask with respect to the source image. The first submask refers to a portion of the first mask. The second submask refers a portion of the second mask. A machine learning system generates a set of predictions upon receiving the set of two-submask images. A particular two-submask image with a highest prediction loss is selected from among the set of two-submask images. The machine learning system is trained via a training dataset, which includes the source image, the two-mask image, and the selected two-submask image.

Abstract: Generative equilibrium transformers are disclosed. Disclosed embodiments provide a simple and effective technique that can distill a multi-step diffusion process into a single-step generative model using solely noise/image pairs.

Abstract: A computer-implemented system and method includes establishing a station sequence that a given part traverses. Each station includes a machine that performs at least one operation with respect to the given part. Measurement data, which relates to attributes of a plurality of parts that traversed the plurality of machines, is received. The measurement data is obtained by sensors and corresponds to a current process period. A first machine learning model is pretrained to generate (i) latent representations based on the measurement data and (ii) machine states based on the latent representations. Machine observation data, which relates to the current process period, is received. Aggregated data is generated based on the measurement data and the machine observation data. A second machine learning model generates a maintenance prediction based on the aggregated data. The maintenance prediction corresponds to a next process period.

Abstract: A computer-implemented system and method include establishing a station sequence that a given part traverses. A history embedding sequence is generated and comprises (a) history measurement embeddings based on history measurement data, the history measurement data relating to attributes of at least one other part that traversed the plurality of stations before the given part, (b) history part identifier embeddings based at least one history part identifiers of at least one other part, and (c) history station identifier embeddings based on the at least one history station identifier corresponding to the history measurement data.

Abstract: A computer-implemented system and method includes establishing a station sequence that a given part traverses. A first neural network generates a set of parameter data based on observed measurement data of the given part at each station of a station subsequence. The set of parameter data is associated with a latent variable subsequence corresponding to the station subsequence. A second neural network generates next parameter data based on history measurement data and the set of parameter data. The history measurement data relates to another part processed before the given part and is associated with each station of the station sequence. The next parameter data is associated with a next latent variable that follows the latent variable subsequence. The next latent variable corresponds to a next station that follows the station subsequence in the station sequence.

Abstract: A computer-implemented system and method relate to object discovery. The system and method include receiving a source image and generating input data by associating each pixel of the source image with predetermined phase values. An encoder encodes the input data to generate latent representation data in spherical coordinates. A decoder decodes the latent representation data to generate spherical reconstruction data of the source image. The spherical reconstruction data includes a radial component and a plurality of phase components. A reconstructed image is generated based at least on the radial component. The reconstructed image is a reconstruction of the source image.

Abstract: A system and method include dividing a source image into a plurality of source regions, which are portions of the source image that correspond to a plurality of grid regions. A mask is used to create a first masked region that masks a first source region and a first unmasked region that comprises a second source region. A first inpainted region is generated by inpainting the first masked region based on the second source region. Similarity data is generated based on a similarity assessment. A protected image is generated that includes at least (i) the first masked region at a first grid region when the similarity data indicates that the first source region is not similar to the first inpainted region and (ii) the first inpainted region at the first grid region when the similarity data indicates that the first source region is similar to the first inpainted region.

Abstract: 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.