Abstract: Techniques herein train a multilayer perceptron, sparsify edges of a graph such as the perceptron, and store edges and vertices of the graph. Each edge has weight. A computer sparsifies perceptron edges. The computer performs a forward-backward pass on the perceptron to calculate a sparse Hessian matrix. Based on that Hessian, the computer performs quasi-Newton perceptron optimization. The computer repeats this until convergence. The computer stores edges in an array and vertices in another array. Each edge has weight and input and output indices. Each vertex has input and output indices. The computer inserts each edge into an input linked list based on its weight. Each link of the input linked list has the next input index of an edge. The computer inserts each edge into an output linked list based on its weight. Each link of the output linked list comprises the next output index of an edge.

Abstract: In an artificial neural network, integrality refers to the degree to which a neuron generates, for a given set of inputs, outputs that are near the border of the output range of a neuron. From each neural network of a pool of trained neural networks, a group of neurons with a higher integrality is selected to form a neural network tunnel (“tunnel”). The tunnel must include all input neurons and output neurons from the neural network, and some of the hidden neurons. Tunnels generated from each neural network in a pool are merged to form another neural network. The new network may then be trained.

Abstract: The disclosed embodiments relate to a system that improves operation of a monitored system. During a training mode, the system uses a training data set comprising labeled data points received from the monitored system to train the SVM to detect one or more conditions-of-interest. While training the SVM model, the system makes approximations to reduce computing costs, wherein the approximations involve stochastically discarding points from the training data set based on an inverse distance to a separating hyperplane for the SVM model. Next, during a surveillance mode, the system uses the trained SVM model to detect the one or more conditions-of-interest based on monitored data points received from the monitored system. When one or more conditions-of-interest are detected, the system performs an action to improve operation of the monitored system.

Abstract: In an artificial neural network, integrality refers to the degree to which a neuron generates, for a given set of inputs, outputs that are near the border of the output range of a neuron. From each neural network of a pool of trained neural networks, a group of neurons with a higher integrality is selected to form a neural network tunnel (“tunnel”). The tunnel must include all input neurons and output neurons from the neural network, and some of the hidden neurons. Tunnels generated from each neural network in a pool are merged to form another neural network. The new network may then be trained.

Abstract: Techniques herein train a multilayer perceptron, sparsify edges of a graph such as the perceptron, and store edges and vertices of the graph. Each edge has weight. A computer sparsifies perceptron edges. The computer performs a forward-backward pass on the perceptron to calculate a sparse Hessian matrix. Based on that Hessian, the computer performs quasi-Newton perceptron optimization. The computer repeats this until convergence. The computer stores edges in an array and vertices in another array. Each edge has weight and input and output indices. Each vertex has input and output indices. The computer inserts each edge into an input linked list based on its weight. Each link of the input linked list has the next input index of an edge. The computer inserts each edge into an output linked list based on its weight. Each link of the output linked list comprises the next output index of an edge.

Abstract: Techniques herein train a multilayer perceptron, sparsify edges of a graph such as the perceptron, and store edges and vertices of the graph. Each edge has weight. A computer sparsifies perceptron edges. The computer performs a forward-backward pass on the perceptron to calculate a sparse Hessian matrix. Based on that Hessian, the computer performs quasi-Newton perceptron optimization. The computer repeats this until convergence. The computer stores edges in an array and vertices in another array. Each edge has weight and input and output indices. Each vertex has input and output indices. The computer inserts each edge into an input linked list based on its weight. Each link of the input linked list has the next input index of an edge. The computer inserts each edge into an output linked list based on its weight. Each link of the output linked list comprises the next output index of an edge.

Abstract: Techniques herein are for sharing data structures. In embodiments, a computer obtains a directed object graph (DOG) containing objects and pointers interconnecting the objects. Each object pointer (OP) resides in a source object and comprises a memory address (MA) of a target object (TO). An original address space (OAS) contains the MA of the TO. The objects are not contiguous within the OAS. The DOG resides in original memory segment(s). The computer obtains an additional memory segment (AMS) beginning at a base address. The computer records the base address within the AMS. For each object in the DOG, the computer copies the object into the AMS at a respective address. For each OP in the DOG having the object as the TO of the MA of the OP, the computer replaces the MA of the OP with the respective address. AMS contents are provided in another address space.

Abstract: According to one technique, a modeling computer computes a Hessian matrix by determining whether an input matrix contains more than a threshold number of dense columns. If so, the modeling computer computes a sparsified version of the input matrix and uses the sparsified matrix to compute the Hessian. Otherwise, the modeling computer identifies which columns are dense and which columns are sparse. The modeling computer then partitions the input matrix by column density and uses sparse matrix format to store the sparse columns and dense matrix format to store the dense columns. The modeling computer then computes component parts which combine to form the Hessian, wherein component parts that rely on dense columns are computed using dense matrix multiplication and component parts that rely on sparse columns are computed using sparse matrix multiplication.

Abstract: Techniques herein are for sharing data structures. In embodiments, a computer obtains a directed object graph (DOG) containing objects and pointers interconnecting the objects. Each object pointer (OP) resides in a source object and comprises a memory address (MA) of a target object (TO). An original address space (OAS) contains the MA of the TO. The objects are not contiguous within the OAS. The DOG resides in original memory segment(s). The computer obtains an additional memory segment (AMS) beginning at a base address. The computer records the base address within the AMS. For each object in the DOG, the computer copies the object into the AMS at a respective address. For each OP in the DOG having the object as the TO of the MA of the OP, the computer replaces the MA of the OP with the respective address. AMS contents are provided in another address space.

Abstract: According to one technique, a modeling computer computes a Hessian matrix by determining whether an input matrix contains more than a threshold number of dense columns. If so, the modeling computer computes a sparsified version of the input matrix and uses the sparsified matrix to compute the Hessian. Otherwise, the modeling computer identifies which columns are dense and which columns are sparse. The modeling computer then partitions the input matrix by column density and uses sparse matrix format to store the sparse columns and dense matrix format to store the dense columns. The modeling computer then computes component parts which combine to form the Hessian, wherein component parts that rely on dense columns are computed using dense matrix multiplication and component parts that rely on sparse columns are computed using sparse matrix multiplication.

Abstract: Techniques herein are for sharing data structures between processes. A method involves obtaining a current memory segment that begins at a current base address within a current address space. The current memory segment comprises a directed object graph and a base pointer. The graph comprises object pointers and objects. For each particular object, determine whether a different memory segment contains an equivalent object that is equivalent to the particular object. If the equivalent object exists, for each object pointer having the particular object as its target object, replace the memory address of the object pointer with a memory address of the equivalent object that does not reside in the current memory segment. Otherwise, for each object pointer having the particular object as its target object, increment the memory address of the object pointer by an amount that is a difference between the current base address and the original base address.

Abstract: According to one technique, a modeling computer computes a Hessian matrix by determining whether an input matrix contains more than a threshold number of dense columns. If so, the modeling computer computes a sparsified version of the input matrix and uses the sparsified matrix to compute the Hessian. Otherwise, the modeling computer identifies which columns are dense and which columns are sparse. The modeling computer then partitions the input matrix by column density and uses sparse matrix format to store the sparse columns and dense matrix format to store the dense columns. The modeling computer then computes component parts which combine to form the Hessian, wherein component parts that rely on dense columns are computed using dense matrix multiplication and component parts that rely on sparse columns are computed using sparse matrix multiplication.

Abstract: Techniques herein are for sharing data structures between processes. A method involves obtaining a current memory segment that begins at a current base address within a current address space. The current memory segment comprises a directed object graph and a base pointer. The graph comprises object pointers and objects. For each particular object, determine whether a different memory segment contains an equivalent object that is equivalent to the particular object. If the equivalent object exists, for each object pointer having the particular object as its target object, replace the memory address of the object pointer with a memory address of the equivalent object that does not reside in the current memory segment. Otherwise, for each object pointer having the particular object as its target object, increment the memory address of the object pointer by an amount that is a difference between the current base address and the original base address.

Abstract: Techniques herein are for sharing data structures between processes. A method involves obtaining a current memory segment that begins at a current base address within a current address space. The current memory segment comprises a directed object graph and a base pointer. The graph comprises object pointers and objects. For each particular object, determine whether a different memory segment contains an equivalent object that is equivalent to the particular object. If the equivalent object exists, for each object pointer having the particular object as its target object, replace the memory address of the object pointer with a memory address of the equivalent object that does not reside in the current memory segment. Otherwise, for each object pointer having the particular object as its target object, increment the memory address of the object pointer by an amount that is a difference between the current base address and the original base address.

Abstract: Techniques herein train a multilayer perceptron, sparsify edges of a graph such as the perceptron, and store edges and vertices of the graph. Each edge has weight. A computer sparsifies perceptron edges. The computer performs a forward-backward pass on the perceptron to calculate a sparse Hessian matrix. Based on that Hessian, the computer performs quasi-Newton perceptron optimization. The computer repeats this until convergence. The computer stores edges in an array and vertices in another array. Each edge has weight and input and output indices. Each vertex has input and output indices. The computer inserts each edge into an input linked list based on its weight. Each link of the input linked list has the next input index of an edge. The computer inserts each edge into an output linked list based on its weight. Each link of the output linked list comprises the next output index of an edge.

Abstract: According to one technique, a modeling computer computes a Hessian matrix by determining whether an input matrix contains more than a threshold number of dense columns. If so, the modeling computer computes a sparsified version of the input matrix and uses the sparsified matrix to compute the Hessian. Otherwise, the modeling computer identifies which columns are dense and which columns are sparse. The modeling computer then partitions the input matrix by column density and uses sparse matrix format to store the sparse columns and dense matrix format to store the dense columns. The modeling computer then computes component parts which combine to form the Hessian, wherein component parts that rely on dense columns are computed using dense matrix multiplication and component parts that rely on sparse columns are computed using sparse matrix multiplication.