Abstract: Implement one or both of processing of computer queries using machine learning models and/or generation of machine learning models in a computer system. A computer processor generates a plurality of stored machine learning models. A computer processor extracts a plurality of updated parameters sets from the plurality of stored machine learning models. A computer processor creates a new machine learning model based on the respective distribution of each parameter included in the plurality of updated parameters sets. A computer processor processes at least one new query using the new machine learning model.

Abstract: Motion parameters estimation for localization of differential drive vehicles is an important part of robotics and autonomous navigation. Conventional methods require introceptive as well extroceptive sensors for localization. The present disclosure provides a control command based adaptive system and method for estimating motion parameters of differential drive vehicles. The method utilizes information from one or more time synchronized command signals and generate an experimental model for estimating one or more motion parameters of the differential drive vehicle by computing a mapping function. The experimental model is validated to determine change in the one or more motion parameters with change in one or more factors and adaptively updated to estimate updated value of the one or more motion parameters based on the validation. The system and method of present disclosure provide accurate results for localization with minimum use of extroceptive sensors.

Abstract: Disclosed is an in-memory device for operation of a multi-bit weight. A multi-bit memory cell array according to an exemplary embodiment of the present invention includes at least one multi-bit unit which stores input data based on an input signal and outputs a per-group sum value summed for every group by applying a multi-bit weight to the stored input data; and a final summation unit which is connected to at least one multi-bit unit, adjusts a ratio for every group to receive the peer-group sum value, and outputs a final output value by summing the input per-group sum value.

Abstract: An approach for selecting a transformed prediction model for estimating the uncertainty of prediction associated with machine learning is disclosed. The approach determines a function corresponding to a prediction interval based on one or more datasets and calculates one or more sets of prediction interval parameters associated with the function based on training a set of regression models with the one or more datasets. The approach creates one or more transformed predictions based on the one or more sets of parameters and based on a logical distance and selects a transformed prediction model based on a mean absolute correlation from the one or more transformed predictions. Furthermore, the approach outputs the selected transformed prediction model.

Abstract: A classification-based diagnosis for detecting and predicting faults in physical system (e.g. an electronic circuit or rail switch) is disclosed. Some embodiments make use of partial system model information (e.g., system topology, components behavior) to simplify the classifier complexity (e.g., reduce the number of parameters). Some embodiments of the method use a Bayesian approach to derive a classifier structure.

Abstract: Technical solutions are described for storing weight in a crosspoint device of a resistive processing unit (RPU) array. An example system includes a crosspoint array, wherein each array node represents a connection between neurons of the neural network, and wherein each node stores a weight assigned to the node. The crosspoint array includes a crosspoint device at each node. The crosspoint device includes a counter that has multiple single bit counters, and states of the counters represent the weight to be stored at the crosspoint device. Further, the crosspoint device includes a resistor device that has multiple resistive circuits, and each resistive circuit is associated with a respective counter from the counters. The resistive circuits are activated or deactivated according to a state of the associated counter, and an electrical conductance of the resistor device is adjusted based at least in part on the resistive circuits that are activated.

Abstract: When a signal glitches, logic receiving the signal may change in response, thereby charging and/or discharging nodes within the logic and dissipating power. Providing a glitch-free signal may reduce the number of times the nodes are charged and/or discharged, thereby reducing the power dissipation. A technique for eliminating glitches in a signal is to insert a storage element that samples the signal after it is done changing to produce a glitch-free output signal. The storage element is enabled by a “ready” signal having a delay that matches the delay of circuitry generating the signal. The technique prevents the output signal from changing until the final value of the signal is achieved. The output signal changes only once, typically reducing the number of times nodes in the logic receiving the signal are charged and/or discharged so that power dissipation is also reduced.

Abstract: Methods, systems, apparatus and computer program products for implementing machine learning within control systems are disclosed. An industrial facility setting slate can be received from a machine learning system and a determination can be made as to whether to adopt the settings in the industrial facility setting slate. The machine learning model can be a neural network, e.g., a deep neural network, that has been trained, e.g., using reinforcement learning to predict a data setting slate that is predicted to optimize an efficiency of a data center.

Abstract: A load control device may be configured to control multiple characteristics of one or more electrical loads such as the intensity and color of a lighting load. The load control device may include concentric rotating portions for adjusting the multiple characteristics. A control circuit of the load control device may be configured to generate control data for controlling one or more of the characteristics of the electrical loads in response to rotations of the concentric rotating portions. The control circuit may be further configured to provide feedback regarding the control being applied on one or more visual indicators. The load control device may be a wall-mounted dimmer device or a battery-powered remote control device.

Abstract: A system and method of accelerating execution of a NN model, by at least one processor may include: receiving a first matrix A, representing elements of a kernel K of the NN model and a second matrix B, representing elements of an input I to kernel K; producing from matrix A, a group-sparse matrix A?, comprising G tensors of elements. The number of elements in each tensor is defined by, or equal to a number of entries in each index of an input tensor register used for a specific Single Instruction Multiple Data (SIMD) tensor operation, and all elements of A? outside said G tensors are null. The system and method may further include executing kernel K on input I, by performing at least one computation of the SIMD tensor operation, having as operands elements of a tensor of the G tensors and corresponding elements of the B matrix.

Abstract: Systems and computer-implemented methods facilitating automatic report generation, population and information prompting employing artificial intelligence technology are provided.

Abstract: A system for accelerating an RNN network including: a first cache, which is used for outputting Wx1 to WxN or Wh1 to WhN in parallel in N paths in a cyclic switching manner, and the degree of parallelism is k; a second cache, which is used for outputting xt or ht-1 in the cyclic switching manner; a vector multiplication circuit, which is used for, by using N groups of multiplication arrays, respectively calculating Wx1xt to WxNxt, or respectively calculating Wh1ht-1 to WhNht-1; an addition circuit, which is used for calculating Wx1xt+Wh1ht-1+b1 to WxNxt+WhNht-1+bN; an activation circuit, which is used for performing an activation operation according to an output of the addition circuit; a state updating circuit, which is used for acquiring ct-1, calculating ct and ht, updating ct-1, and sending ht to the second cache; a bias data cache; a vector cache; and a cell state cache.

Abstract: Methods and systems may be used for transfer learning of neural networks. According to one example, a method includes: grouping data objects of a first training set into a plurality of clusters; training a base model using a first cluster of the plurality of clusters, the base model being a neural network having a plurality of nodes; generalizing the base model to obtain a generalized base model, the generalizing the base model including setting a portion of the plurality of nodes to have random or predetermined weights; determining that the first cluster is, out of the plurality clusters, most similar to a second training set; and training the generalized base model using the second training set to obtain a trained model.

Abstract: The present disclosure discloses an artificial intelligence apparatus including an input interface configured to obtain input data, a sensing interface configured to obtain environment information, and one or more processors configured to classify an object by inputting the input data obtained from the input interface to an artificial intelligence model, in which the artificial intelligence model uses a first learning model and a second learning model which is connected with the first learning model and includes a plurality of output layers to respectively assign weights to the respective result values output by the plurality of output layers and combine the respective result values to which the weights are assigned to derive the final result.

Abstract: Exemplary embodiments are disclosed that include multi-voltage contactors, controls, and related methods.

Abstract: A first set of execution platforms is deployed for a set of operations of a training phase of a machine learning model. Prior to the completion of the training phase, a triggering condition for deployment of a different set of execution platforms is detected. The different set of execution platforms is deployed for a subsequent set of training phase operations.

Abstract: A technique includes modifying a first record based on a plurality of training records to provide a plurality of modified records. The plurality of training records are associated with a regression model, and a target outcome is associated with the first record. The technique includes applying the regression model to the plurality of modified records to provide outcomes for the modified records; and selecting a group of the modified records based at least in part on the outcomes for the modified records. The technique includes providing a recommendation for the first record to change an outcome predicted for the first record by the regression model based at least part on the target outcome and the outcomes for the modified records.

Abstract: Systems and methods for tuning hyperparameters of a model includes: receiving a multi-criteria tuning work request for tuning hyperparameters of the model of the subscriber to the remote tuning service, wherein the multi-criteria tuning work request includes: a first objective function of the model to be optimized by the remote tuning service; a second objective function to be optimized by the remote tuning service, the second objective function being distinct from the first objective function; computing a joint tuning function based on a combination of the first objective function and the second objective function; executing a tuning operation of the hyperparameters for the model based on a tuning of the joint function; and identifying one or more proposed hyperparameter values based on one or more hyperparameter-based points along a convex Pareto optimal curve.

Abstract: An operating method of a neuromorphic processor which processes data based on a neural network including a first layer including axons and a second layer including neurons includes receiving synaptic weights between the first layer and the second layer, decomposing the synaptic weights into presynaptic weights, a number of which is identical to a number of the axons, and postsynaptic weights, a number of which is identical to a number of the synaptic weights, and storing the presynaptic weights and the postsynaptic weights. A precision of each of the synaptic weights is a first number of bits, a precision of each of the presynaptic weights is a second number of bits, and a precision of each of the postsynaptic weights is a third number of bits. The third number of the bits is smaller than the first number of the bits.

Abstract: A foldable display apparatus, a method of manufacturing the same, and a controlling method of the same are disclosed. The foldable display apparatus includes a substrate including a metal thin film and an insulating layer provided on the metal thin film, an organic light-emitting unit formed on the substrate and emitting light in an direction away from the substrate, and a thin film encapsulating layer for encapsulating the organic light-emitting unit. The foldable display apparatus may be folded in a direction such that the metal thin film is exposed.