Abstract: Forecasting resource allocation is disclosed. An example method includes receiving operating data from a resource; applying periodicity tests to the received operating data using a plurality of sketches of time series of prior operating data, the periodicity tests generating periodic components; applying regression models to the received operating data, the regression models collectively generating a trend component, each regression model being applied over a different time scale of a plurality of time scales; computing a trend model using the periodic components and a trend component; determining a random process describing the historical evolution of the trend model; and calculating and providing a mean prediction, an upper bound, and a lower bound for resource utilization at a future time using the trend model and a predicted distribution.

Abstract: Methods and apparatus for quantum assisted optimization. In one aspect, a method includes obtaining a set of initial input states, applying one or more of (i) dynamical thermal fluctuations and (ii) cluster update algorithms to the set of input states and subsequent input states when the states evolve within the classical information processors, applying dynamical quantum fluctuations to the set of input states and subsequent states when the states evolve within the quantum systems and repeating the application steps until a desirable output state is obtained.

Abstract: Described are a system, method, and computer program product for dynamic node classification in temporal-based machine learning classification models. The method includes receiving graph data of a discrete time dynamic graph including graph snapshots, and node classifications associated with all nodes in the discrete time dynamic graph. The method includes converting the discrete time dynamic graph to a time-augmented spatio-temporal graph and generating an adjacency matrix based on a temporal walk of the time-augmented spatio-temporal graph. The method includes generating an adaptive information transition matrix based on the adjacency matrix and determining feature vectors based on the nodes and the node attribute matrix of each graph snapshot.

Abstract: A method and system for identifying entangled photons includes generating a plurality of sets of four entangled photons, wherein one pair of photons of each set are time correlated, thereby indicating that another pair of four entangled photons are entangled. Quantum metadata comprising a time window corresponding to the generated plurality of sets of four entangled photons is collected. A coincidence of one pair of photons of each of the plurality of the sets of four entangled photons is determined. A state value of at least one photon of the other pair of each of the number of the sets of four entangled photons is determined. Ordered lists of coincidences are compared to ordered lists of state values to determine entangled state information. Time window are compared to times corresponding to the ordered lists. Error conditions are generated if conditions are met.

Abstract: Embodiments are directed towards a system on chip (SoC) that implements a deep convolutional network heterogeneous architecture. The SoC includes a system bus, a plurality of addressable memory arrays coupled to the system bus, at least one applications processor core coupled to the system bus, and a configurable accelerator framework coupled to the system bus. The configurable accelerator framework is an image and deep convolutional neural network (DCNN) co-processing system. The SoC also includes a plurality of digital signal processors (DSPs) coupled to the system bus, wherein the plurality of DSPs coordinate functionality with the configurable accelerator framework to execute the DCNN.

Abstract: Techniques related to compressing a pre-trained dense deep neural network to a sparsely connected deep neural network for efficient implementation are discussed. Such techniques may include iteratively pruning and splicing available connections between adjacent layers of the deep neural network and updating weights corresponding to both currently disconnected and currently connected connections between the adjacent layers.

Abstract: A hardware accelerator can store, in multiple memory storage areas in one or more memories on the accelerator, input data for each processing time step of multiple processing time steps for processing sequential inputs to a machine learning model. For each processing time step, the following is performed. The accelerator can access a current value of a counter stored in a register within the accelerator to identify the processing time step. The accelerator can determine, based on the current value of the counter, one or more memory storage areas that store the input data for the processing time step. The accelerator can facilitate access of the input data for the processing time step from the one or more memory storage areas to at least one processor coupled to the one or more memory storage areas. The accelerator can increment the current value of the counter stored in the register.

Abstract: The present disclosure provides systems and methods that generate new architectures for artificial neural networks based on connectomics data that describes connections between biological neurons of a biological organism. In particular, in some implementations, a computing system can identify one or more new artificial neural network architectures by performing a neural architecture search over a search space that is constrained based at least in part on the connectomics data.

Abstract: Techniques for analyzing code are described. In some instances, a code analysis service is implemented by one or more electronic devices, the code analysis service including instructions that upon execution cause the code analysis service to: perform a program analysis to mine a code segment of the stored code to generate a descriptor of each input in the code segment that appears to be have insufficient input validation; assess that an input has insufficient validation and determining a classification of input validation to use by determining a category of input validation to apply to the input; acquire suggestion for the determined category; and provide the acquired suggestion for the determined category.

Abstract: A neural network compression method whereby forward inference is performed on target data by using a target parameter sharing network to obtain an output feature map of the last convolutional module, a channel related feature is extracted from the output feature map, the extracted channel related feature and a target constraint condition are input into a target meta-generative network, and an optimal network architecture under the target constraint condition is predicted by using the target meta-generative network to obtain a compressed neural network model.

Abstract: An event driven device has a network collecting data. A device is coupled to the network for determining changes in the data collected, wherein the device signals the network to process the data collected when the device determines desired changes in the data collected. In a second embodiment a level shift adjusts the band diagram of a spill and fill circuit to allow processing only if a change in input value occurs. This is extended to teach a means by which the subset of an image or incoming audio data might be used to trigger an event. It could also be used for always on operation at lower power than alternative solutions.

Abstract: A policy improvement method of improving a policy of reinforcement learning based on a state value function is performed by a computer. The method causes a computer to execute a process including: calculating an input to a control target based on the policy and a predetermined exploration method of exploring for an input to the control target in the reinforcement learning; and updating a parameter of the policy based on a result of applying the calculated input to the control target, using the input to the control target and a generalized inverse matrix regarding a state of the control target.

Abstract: Described herein are systems and methods for coupling Nitrogen Vacancy (NV)-defects in a quantum computing architecture. A diamond wafer comprises separated implantation sites, at least a portion of which comprise a single NV-defect. An optical cavity system comprises cavity sites aligned to the implantation sites. An integrated optics system includes a first chip module comprising optical waveguides and associated switchable elements, photon sources, photon detectors, and fiber optic connections. A first switchable element couples a first pair of NV-defects by splitting a beam emitted by a photon source, via a first optical waveguide, to the cavity sites aligned to the implantation sites of the first pair of NV-defects. A second switchable element couples a second pair of NV-defects by splitting a beam emitted by a photon source, via a second optical waveguide, to the cavity sites aligned to the implantation sites of the second pair of NV-defects.

Abstract: The present invention discloses a brain machine interface decoding method based on spiking neural network, comprising: (1) constructing a liquid state machine model based on a spiking neural network, the liquid state machine model consists of an input layer, an middle layer and an output layer, wherein, a connection weight from the input layer to the middle layer is Whh, a loop connection weight inside the middle layer is Whh, a readout weight from the middle layer to the output layer is Wyh; (2) Inputting a neuron spike train signal, and training each weight with the following strategy: (2-1) Using STDP without supervision to train the connection weight Whh from the input layer to the middle layer; (2-2) Setting the loop connection weight Whh inside the middle layer by means of distance model and random connection, and obtaining a middle layer liquid information R(t); (2-3) Using ridge regression with supervision to train the readout weight Wyh from the middle layer to the output layer, and establishing a ma

Abstract: A computer implemented method for training a neural network to capture a structural feature specific to a set of chemical compounds is disclosed. In the method, the computer system reads an expression describing a structure of the chemical compound for each chemical compound in the set and enumerates one or more combinations of a position and a type of a structural element appearing in the expression for each chemical compound in the set. The computer system also generates training data based on the one or more enumerated combinations for each chemical compound in the set. The training data includes one or more values with a length, each of which indicates whether or not a corresponding type of the structural element appears at a corresponding position for each combination. Furthermore, the computer system trains the neural network based on the training data for the set of the chemical compounds.

Abstract: A machine learning (ML) system includes a student ML system, a learning coach ML system, and a reference system that generates training data for the student ML system. The learning coach ML system learns to make an enhancement to the student ML system or to its learning process, such as updated hyperparameter or a network structural change, based on training of the student ML system with the training data generated by the reference system. The system may also comprise a learning experimentation system that communicates with the reference system to conduct experiments on the learning of the student learning system. Also, the learning experimentation system can determine a cost function for the learning coach ML system.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using recurrent neural networks. One of the systems includes a main recurrent neural network comprising one or more recurrent neural network layers and a respective hyper recurrent neural network corresponding to each of the one or more recurrent neural network layers, wherein each hyper recurrent neural network is configured to, at each of a plurality of time steps: process the layer input at the time step to the corresponding recurrent neural network layer, the current layer hidden state of the corresponding recurrent neural network layer, and a current hypernetwork hidden state of the hyper recurrent neural network to generate an updated hypernetwork hidden state.

Abstract: A neuron circuit, comprising first and second NDR devices biased each with opposite polarities, said first and second NDR devices being coupled to first and second grounded capacitors.

Abstract: Example implementations described herein are directed to a novel Automated Machine Learning (AutoML) framework that is generated on an AutoML library so as to facilitate functionality to incorporate multiple machine learning model libraries within the same framework through a solution configuration file. The example implementations further involve a solution generator that identifies solution candidates and parameters for machine learning models to be applied to a dataset specified by the solution configuration file.

Abstract: The structure of an untagged document can be derived using a predictive model that is trained in a supervised learning framework based on a corpus of tagged training documents. Analyzing the training documents results in a plurality of document part feature vectors, each of which correlates a category defining a document part (for example, “title” or “body paragraph”) with one or more feature-value pairs (for example, “font=Arial” or “alignment=centered”). Any suitable machine learning algorithm can be used to train the predictive model based on the document part feature vectors extracted from the training documents. Once the predictive model has been trained, it can receive feature-value pairs corresponding to a portion of an untagged document and make predictions with respect to the how that document part should be categorized. The predictive model can therefore generate tag metadata that defines a structure of the untagged document in an automated fashion.