Abstract: A disclosed example includes providing vibration information to a model, the vibration information corresponding to a first vibration measured at a first mobile device when the first mobile device is in a state of non-use by a user, the model based on a plurality of vibration patterns that correspond to second vibrations measured by second mobile devices in different environments; identifying, using the model, one of the vibration patterns that corresponds to the vibration information; determining an environment of the first mobile device based on the one of the vibration patterns; and instructing the first mobile device to modify a functionality of the first mobile device based on the environment.

Abstract: Methods and apparatuses for implementing a neural network using symmetric tensors. In embodiments, a system may include a higher order neural network with a plurality of layers that includes an input layer, one or more hidden layers, and an output layer. Each of the input layer, the one or more hidden layers, and the output layer includes a plurality of neurons, where the plurality of neurons includes at least first order neurons and second order neurons, and where inputs at a second order neuron are combined using a symmetric tensor.

Abstract: Methods and apparatus to facilitate autonomous navigation of robotic devices. An example autonomous robot includes a region model analyzer to: analyze a first image of an environment based on a first neural network model, the first image captured by an image sensor of the robot when the robot is in a first region of the environment; and analyze a second image of the environment based on a second neural network model, the second image captured by the image sensor when the robot is in a second region of the environment, the second neural network associated with the second region. The example robot further includes a movement controller to: autonomously control movement of the robot within the first region toward the second region based on the analysis of the first image; and autonomously control movement of the robot within the second region based on the analysis of the second image.

Abstract: Methods and apparatuses for implementing a neural network using symmetric tensors. In embodiments, a system may include a higher order neural network with a plurality of layers that includes an input layer, one or more hidden layers, and an output layer. Each of the input layer, the one or more hidden layers, and the output layer includes a plurality of neurons, where the plurality of neurons includes at least first order neurons and second order neurons, and where inputs at a second order neuron are combined using a symmetric tensor.

Abstract: The present disclosure provides a machine learning model where each activation node within the model has an adaptive activation function defined in terms of an input and a hyperparameter of the model. Accordingly, each activation node can have a separate of distinct activation function, based on the adaptive activation function where the hyperparameter for each activation node is trained during overall training of the model. Furthermore, the present disclosure provides that a set of adaptive activation functions can be provided for each activation node such that a spike train of activations can be generated.

Abstract: A disclosed example includes providing vibration information to a model, the vibration information corresponding to a first vibration measured at a first mobile device when the first mobile device is in a state of non-use by a user, the model based on a plurality of vibration patterns that correspond to second vibrations measured by second mobile devices in different environments; identifying, using the model, one of the vibration patterns that corresponds to the vibration information; determining an environment of the first mobile device based on the one of the vibration patterns; and instructing the first mobile device to modify a functionality of the first mobile device based on the environment.

Abstract: In some embodiments, a method calculates a first color metric value from a detection window in a first image that is detected by a visual system. A second image of pixels is generated where the pixels include one or more second color metric values that meet the first color metric value within a threshold. The method compares the second image to an object. When the comparison meets a criterion, the method transitions from a first mode to a second mode. When the comparison does not meet the criterion, the method continues to operate in the first mode.

Abstract: An apparatus to facilitate fractional convolutional kernels is disclosed. The apparatus includes one or more processors comprising a convolution circuit of a neural network, the convolution circuit to initialize a set of parameters of a fractional convolutional kernel, the set of parameters comprising at least a fractional derivative parameter that is initialized with a fractional value, and apply the fractional convolutional kernel to input data to convolve the input data to obtain output data.

Abstract: Technology for a mobile device is described. The mobile device can include a vibration motor operable to generate a vibration, and a sensor operable to capture a plurality of vibration signals that result from the vibration. The mobile device can provide the plurality of vibration signals to a model running on the mobile device. The model can include a plurality of predefined patterns that correspond to vibration signals produced by mobile devices residing on different surfaces. The mobile device can identify, using the model, a predefined pattern in the plurality of predefined patterns that substantially corresponds to the plurality of vibration signals based on a confidence level that exceeds a threshold. The mobile device can determine a type of surface on which the mobile device resides based on the predefined pattern identified using the model.

Abstract: Technology for a mobile device is described. The mobile device can include a vibration motor operable to generate a vibration, and a sensor operable to capture a plurality of vibration signals that result from the vibration. The mobile device can provide the plurality of vibration signals to a model running on the mobile device. The model can include a plurality of predefined patterns that correspond to vibration signals produced by mobile devices residing on different surfaces. The mobile device can identify, using the model, a predefined pattern in the plurality of predefined patterns that substantially corresponds to the plurality of vibration signals based on a confidence level that exceeds a threshold. The mobile device can determine a type of surface on which the mobile device resides based on the predefined pattern identified using the model.

Abstract: Technology for a mobile device is described. The mobile device can include a vibration motor operable to generate a vibration, and a sensor operable to capture a plurality of vibration signals that result from the vibration. The mobile device can provide the plurality of vibration signals to a model running on the mobile device. The model can include a plurality of predefined patterns that correspond to vibration signals produced by mobile devices residing on different surfaces. The mobile device can identify, using the model, a predefined pattern in the plurality of predefined patterns that substantially corresponds to the plurality of vibration signals based on a confidence level that exceeds a threshold. The mobile device can determine a type of surface on which the mobile device resides based on the predefined pattern identified using the model.

Abstract: Technology for a mobile device is described. The mobile device can include a vibration motor operable to generate a vibration, and a sensor operable to capture a plurality of vibration signals that result from the vibration. The mobile device can provide the plurality of vibration signals to a model running on the mobile device. The model can include a plurality of predefined patterns that correspond to vibration signals produced by mobile devices residing on different surfaces. The mobile device can identify, using the model, a predefined pattern in the plurality of predefined patterns that substantially corresponds to the plurality of vibration signals based on a confidence level that exceeds a threshold. The mobile device can determine a type of surface on which the mobile device resides based on the predefined pattern identified using the model.

Abstract: In some embodiments, a method calculates a first color metric value from a detection window in a first image that is detected by a visual system. A second image of pixels is generated where the pixels include one or more second color metric values that meet the first color metric value within a threshold. The method compares the second image to an object. When the comparison meets a criterion, the method transitions from a first mode to a second mode. When the comparison does not meet the criterion, the method continues to operate in the first mode.

Abstract: Methods and apparatus to facilitate autonomous navigation of robotic devices. An example autonomous robot includes a region model analyzer to: analyze a first image of an environment based on a first neural network model, the first image captured by an image sensor of the robot when the robot is in a first region of the environment; and analyze a second image of the environment based on a second neural network model, the second image captured by the image sensor when the robot is in a second region of the environment, the second neural network associated with the second region. The example robot further includes a movement controller to: autonomously control movement of the robot within the first region toward the second region based on the analysis of the first image; and autonomously control movement of the robot within the second region based on the analysis of the second image.

Abstract: Methods and apparatus for training a neural network are disclosed. An example apparatus includes a neural network trainer to determine an amount of training error experienced in a prior training epoch of a neural network, and determine a gradient descent value based on the amount of training error. A learning rate determiner is to calculate a learning rate based on the gradient descent value and a selected number of epochs such that a training process of the neural network is completed within the selected number of epochs, the neural network trainer to update weighting parameters of the neural network based on the learning rate.

Abstract: Methods and apparatuses for implementing a neural network using symmetric tensors. In embodiments, a system may include a higher order neural network with a plurality of layers that includes an input layer, one or more hidden layers, and an output layer. Each of the input layer, the one or more hidden layers, and the output layer includes a plurality of neurons, where the plurality of neurons includes at least first order neurons and second order neurons, and where inputs at a second order neuron are combined using a symmetric tensor.

Abstract: Embodiments of the present disclosure provide techniques and configurations for an apparatus for audio signal emulation, based on a vibration signal generated in response to a user's voice. In some embodiments, the apparatus may include at least one sensor disposed on the apparatus to generate a sensor signal indicative of vibration induced by a user's voice in a portion of a user's head. The apparatus may further include a controller coupled with the sensor, to transform the sensor signal into an emulated audio signal, with distortions associated with the vibration in the user's head portion that are manifested in the generated sensor signal, to improve speech recognition based on the generated sensor signal at least partially mitigated. Other embodiments may be described and/or claimed.