Abstract: A system for time series analysis using attention models is disclosed. The system may capture dependencies across different variables through input embedding and may map the order of a sample appearance to a randomized lookup table via positional encoding. The system may capture capturing dependencies within a single sequence through a self-attention mechanism and determine a range of dependency to consider for each position being analyzed. The system may obtain an attention weighting to other positions in the sequence through computation of an inner product and utilize the attention weighting to acquire a vector representation for a position and mask the sequence to enable causality. The system may employ a dense interpolation technique for encoding partial temporal ordering to obtain a single vector representation and a linear layer to obtain logits from the single vector representation. The system may use a type dependent final prediction layer.

Abstract: A system for time series analysis using attention models is disclosed. The system may capture dependencies across different variables through input embedding and may map the order of a sample appearance to a randomized lookup table via positional encoding. The system may capture capturing dependencies within a single sequence through a self-attention mechanism and determine a range of dependency to consider for each position being analyzed. The system may obtain an attention weighting to other positions in the sequence through computation of an inner product and utilize the attention weighting to acquire a vector representation for a position and mask the sequence to enable causality. The system may employ a dense interpolation technique for encoding partial temporal ordering to obtain a single vector representation and a linear layer to obtain logits from the single vector representation. The system may use a type dependent final prediction layer.

Abstract: A system for time series analysis using attention models is disclosed. The system may capture dependencies across different variables through input embedding and may map the order of a sample appearance to a randomized lookup table via positional encoding. The system may capture capturing dependencies within a single sequence through a self-attention mechanism and determine a range of dependency to consider for each position being analyzed. The system may obtain an attention weighting to other positions in the sequence through computation of an inner product and utilize the attention weighting to acquire a vector representation for a position and mask the sequence to enable causality. The system may employ a dense interpolation technique for encoding partial temporal ordering to obtain a single vector representation and a linear layer to obtain logits from the single vector representation. The system may use a type dependent final prediction layer.

Abstract: A system for identifying in an image an object that is commonly found in a collection of images and for identifying a portion of an image that represents an object based on a consensus analysis of segmentations of the image. The system collects images of containers that contain objects for generating a collection of common objects within the containers. To process the images, the system generates a segmentation of each image. The image analysis system may also generate multiple segmentations for each image by introducing variations in the selection of voxels to be merged into a segment. The system then generates clusters of the segments based on similarity among the segments. Each cluster represents a common object found in the containers. Once the clustering is complete, the system may be used to identify common objects in images of new containers based on similarity between segments of images and the clusters.

Abstract: A system for identifying objects in an image is provided. The system identifies segments of an image that may contain objects. For each segment, the system generates a segment score by inputting to a multi-scale neural network windows of multiple scales that include the segment that have been resampled to a fixed window size. A multi-scale neural network includes a feature extracting convolutional neural network (“feCNN”) for each scale and a classifier that inputs each feature of each feCNN. The segment score indicates whether the segment contains an object. The system generates a pixel score for pixels of the image. The pixel score for a pixel indicates that that pixel is within an object based on the segment scores of segments that contain that pixel. The system then identifies the object based on the pixel scores of neighboring pixels.

Abstract: A system for identifying objects in an image is provided. The system identifies segments of an image that may contain objects. For each segment, the system generates a segment score by inputting to a multi-scale neural network windows of multiple scales that include the segment that have been resampled to a fixed window size. A multi-scale neural network includes a feature extracting convolutional neural network (“feCNN”) for each scale and a classifier that inputs each feature of each feCNN. The segment score indicates whether the segment contains an object. The system generates a pixel score for pixels of the image. The pixel score for a pixel indicates that that pixel is within an object based on the segment scores of segments that contain that pixel. The system then identifies the object based on the pixel scores of neighboring pixels.

Abstract: A system for identifying in an image an object that is commonly found in a collection of images and for identifying a portion of an image that represents an object based on a consensus analysis of segmentations of the image. The system collects images of containers that contain objects for generating a collection of common objects within the containers. To process the images, the system generates a segmentation of each image. The image analysis system may also generate multiple segmentations for each image by introducing variations in the selection of voxels to be merged into a segment. The system then generates clusters of the segments based on similarity among the segments. Each cluster represents a common object found in the containers. Once the clustering is complete, the system may be used to identify common objects in images of new containers based on similarity between segments of images and the clusters.

Abstract: A system for identifying in an image an object that is commonly found in a collection of images and for identifying a portion of an image that represents an object based on a consensus analysis of segmentations of the image. The system collects images of containers that contain objects for generating a collection of common objects within the containers. To process the images, the system generates a segmentation of each image. The image analysis system may also generate multiple segmentations for each image by introducing variations in the selection of voxels to be merged into a segment. The system then generates clusters of the segments based on similarity among the segments. Each cluster represents a common object found in the containers. Once the clustering is complete, the system may be used to identify common objects in images of new containers based on similarity between segments of images and the clusters.

Abstract: A system for identifying in an image an object that is commonly found in a collection of images and for identifying a portion of an image that represents an object based on a consensus analysis of segmentations of the image. The system collects images of containers that contain objects for generating a collection of common objects within the containers. To process the images, the system generates a segmentation of each image. The image analysis system may also generate multiple segmentations for each image by introducing variations in the selection of voxels to be merged into a segment. The system then generates clusters of the segments based on similarity among the segments. Each cluster represents a common object found in the containers. Once the clustering is complete, the system may be used to identify common objects in images of new containers based on similarity between segments of images and the clusters.