Abstract: A cleaning apparatus includes a combing unit including a series of spaced protrusions or teeth extending into a cleaning roller for preventing build up and removing debris (such as hair, string, and the like). The protrusions extend along a substantial portion of the cleaning roller and extend partially into the cleaning roller to intercept the debris as it passes around the roller. The protrusions have angled leading edges that are not aligned with a rotation center of the cleaning roller and are directed into or against a direction of rotation of the cleaning roller. The combing unit and protrusions have a shape and configuration designed to facilitate debris removal from the cleaning roller with minimal impact on the operation of the cleaning apparatus. The cleaning apparatus may include a surface cleaning head of an upright vacuum cleaner or sweeper or a robotic vacuum cleaner.

Abstract: A cleaning apparatus includes a combing unit including a series of spaced protrusions or teeth extending into a cleaning roller for preventing build up and removing debris (such as hair, string, and the like). The protrusions extend along a substantial portion of the cleaning roller and extend partially into the cleaning roller to intercept the debris as it passes around the roller. The protrusions have angled leading edges that are not aligned with a rotation center of the cleaning roller and are directed into or against a direction of rotation of the cleaning roller. The combing unit and protrusions have a shape and configuration designed to facilitate debris removal from the cleaning roller with minimal impact on the operation of the cleaning apparatus. The cleaning apparatus may include a surface cleaning head of an upright vacuum cleaner or sweeper or a robotic vacuum cleaner.

Abstract: A cleaning apparatus includes a combing unit including a series of spaced protrusions or teeth extending into a cleaning roller for preventing build up and removing debris (such as hair, string, and the like). The protrusions extend along a substantial portion of the cleaning roller and extend partially into the cleaning roller to intercept the debris as it passes around the roller. The protrusions have angled leading edges that are not aligned with a rotation center of the cleaning roller and are directed into or against a direction of rotation of the cleaning roller. The combing unit and protrusions have a shape and configuration designed to facilitate debris removal from the cleaning roller with minimal impact on the operation of the cleaning apparatus. The cleaning apparatus may include a surface cleaning head of an upright vacuum cleaner or sweeper or a robotic vacuum cleaner.

Abstract: A cleaning apparatus includes a combing unit including a series of spaced protrusions or teeth extending into a cleaning roller for preventing build up and removing debris (such as hair, string, and the like). The protrusions extend along a substantial portion of the cleaning roller and extend partially into the cleaning roller to intercept the debris as it passes around the roller. The protrusions have angled leading edges that are not aligned with a rotation center of the cleaning roller and are directed into or against a direction of rotation of the cleaning roller. The combing unit and protrusions have a shape and configuration designed to facilitate debris removal from the cleaning roller with minimal impact on the operation of the cleaning apparatus. The cleaning apparatus may include a surface cleaning head of an upright vacuum cleaner or sweeper or a robotic vacuum cleaner.

Abstract: A novel interpretable and steerable deep sequence modeling technique is disclosed. The technique combines prototype learning and RNNs to achieve both interpretability and high accuracy. Experiments and case studies on different real-world sequence prediction/classification tasks demonstrate that the model is not only as accurate as other state-of-the-art machine learning techniques but also much more interpretable. In addition, a large-scale user study on Amazon Mechanical Turk demonstrates that for familiar domains like sentiment analysis on texts, the model is able to select high quality prototypes that are well aligned with human knowledge for prediction and interpretation. Furthermore, the model obtains better interpretability without a loss of performance by incorporating the feedback from a user study to update the prototypes, demonstrating the benefits of involving human-in-the-loop for interpretable machine learning.

Abstract: A deep sequence model with prototypes may be steered. A prototype overview is displayed, the prototype overview including a plurality of prototype sequences learned by a model through backpropagation, each of the prototype sequences including a series of events, where for each of the prototype sequences, statistical information is presented with respect to use of the prototype sequence by the model. Input is received adjusting one or more of the prototype sequences to fine-tune the model. The model is updated using the plurality of prototype sequences, as adjusted, to create an updated model. The model, as updated, is displayed in the prototype overview.

Abstract: A deep sequence model with prototypes may be steered. A prototype overview is displayed, the prototype overview including a plurality of prototype sequences learned by a model through backpropagation, each of the prototype sequences including a series of events, where for each of the prototype sequences, statistical information is presented with respect to use of the prototype sequence by the model. Input is received adjusting one or more of the prototype sequences to fine-tune the model. The model is updated using the plurality of prototype sequences, as adjusted, to create an updated model. The model, as updated, is displayed in the prototype overview.

Abstract: A novel interpretable and steerable deep sequence modeling technique is disclosed. The technique combines prototype learning and RNNs to achieve both interpretability and high accuracy. Experiments and case studies on different real-world sequence prediction/classification tasks demonstrate that the model is not only as accurate as other state-of-the-art machine learning techniques but also much more interpretable. In addition, a large-scale user study on Amazon Mechanical Turk demonstrates that for familiar domains like sentiment analysis on texts, the model is able to select high quality prototypes that are well aligned with human knowledge for prediction and interpretation. Furthermore, the model obtains better interpretability without a loss of performance by incorporating the feedback from a user study to update the prototypes, demonstrating the benefits of involving human-in-the-loop for interpretable machine learning.

Abstract: A cleaning apparatus includes a combing unit including a series of spaced protrusions or teeth extending into a cleaning roller for preventing build up and removing debris (such as hair, string, and the like). The protrusions extend along a substantial portion of the cleaning roller and extend partially into the cleaning roller to intercept the debris as it passes around the roller. The protrusions have angled leading edges that are not aligned with a rotation center of the cleaning roller and are directed into or against a direction of rotation of the cleaning roller. The combing unit and protrusions have a shape and configuration designed to facilitate debris removal from the cleaning roller with minimal impact on the operation of the cleaning apparatus. The cleaning apparatus may include a surface cleaning head of an upright vacuum cleaner or sweeper or a robotic vacuum cleaner.