A dataset comprising text-based messages can be accessed. Tokens for words and punctuation marks contained in the text-based messages can be generated. Each token corresponds to one word or one punctuation mark. A vector representation for each of a plurality of the tokens can be generated using natural language processing. A sequence of tokens corresponding to the text-based message can be generated for each of a plurality of the text-based messages in the dataset. Ones of the tokens that represent punctuation marks can be identified. An artificial neural network can be trained to predict use of the punctuation marks in sentence structures. The training uses the generated sequence of tokens and the vector representations for the tokens, in the sequence of tokens, that represent the punctuation marks.

Requests for computing resources and other resources can be predicted and managed. For example, a system can determine a baseline prediction indicating a number of requests for an object over a future time-period. The system can then execute a first model to generate a first set of values based on seasonality in the baseline prediction, a second model to generate a second set of values based on short-term trends in the baseline prediction, and a third model to generate a third set of values based on the baseline prediction. The system can select a most accurate model from among the three models and generate an output prediction by applying the set of values output by the most accurate model to the baseline prediction. Based on the output prediction, the system can cause an adjustment to be made to a provisioning process for the object.

Embodiments are directed to neural network processing for multi-object three-dimensional (3D) modeling. An embodiment of a computer-readable storage medium includes executable computer program instructions for obtaining data from multiple cameras, the data including multiple images, and generating a 3D model for 3D imaging based at least in part on the data from the cameras, wherein generating the 3D model includes one or more of performing processing with a first neural network to determine temporal direction based at least in part on motion of one or more objects identified in an image of the multiple images or performing processing with a second neural network to determine semantic content information for an image of the multiple images.

The present disclosure relates to neural network model processing methods. One example method includes obtaining an operation process of a neural network model, where the operation process is represented by at least one first-type operator and a plurality of second-type operators, and obtaining a first computation graph of the neural network model based on the operation process. In the operation process, the first-type operator includes a boundary identifier, and computational logic of the first-type operator is represented by a group of second-type operators. For any first-type operator, a range of second-type operators included in the any first-type operator is indicated by a boundary identifier in the any first-type operator.

Provided is a method for classifying information technology (IT) service request messages. The method may include receiving data associated with an IT service request message, determining a plurality of number values associated with a plurality of characters included in the IT service request message, generating a vector that includes index values, generating a first bitmap based on generating the vector, generating a second bitmap based on the first bitmap, where the second bitmap has a first dimension and a second dimension, and where the first dimension and the second dimension are equal, and determining a classification of the IT service request message using a neural network algorithm. A system and computer program product are also disclosed.

Systems and methods for parsing natural language sentences using an artificial neural network (ANN) are described. Embodiments of the described systems and methods may generate a plurality of word representation matrices for an input sentence, wherein each of the word representation matrices is based on an input matrix of word vectors, a query vector, a matrix of key vectors, and a matrix of value vectors, and wherein a number of the word representation matrices is based on a number of syntactic categories, compress each of the plurality of word representation matrices to produce a plurality of compressed word representation matrices, concatenate the plurality of compressed word representation matrices to produce an output matrix of word vectors, and identify at least one word from the input sentence corresponding to a syntactic category based on the output matrix of word vectors.

Systems and methods for training an artificial intelligence (AI) classifier of scanned items. The items may include a training set of sample raw scans. The set may include in-class objects and not-in-class raw scans. An AI classifier may be configured to sample raw scans in the training set, measure errors in the results, update classifier parameters based on the errors, and detect completion of training.

A facility for generating monotonic fully connected layer blocks for a machine learning model is described. The facility receives an indication of a convex constituent monotonically increasing activation function and a concave constituent monotonically increasing activation function for a monotonic layer. The facility generates a composite monotonic activation function made up of the convex and concave constituent activation functions. The facility receives an indication of a monotonicity indicator vector for the monotonic dense layer block. The facility determines one or more selector weights for the composite activation function. The facility initializes a sign for each weight of one or more kernel weights included in the monotonic layer and initializes a bias vector. The facility generates the monotonic dense layer block based on the composite activation function, the monotonicity indicator vector, the selector weights, the sign for each kernel weight, and the bias vector.

A computer system obtains a plurality of road images captured by one or more cameras attached to one or more vehicles. The one or more vehicles execute a model that facilitates driving of the one or more vehicles. For each road image of the plurality of road images, the computer system determines, in the road image, a fraction of pixels having an ambiguous lane marker classification. Based on the fraction of pixels, the computer system determines whether the road image is an ambiguous image for lane marker classification. In accordance with a determination that the road image is an ambiguous image for lane marker classification, the computer system enables labeling of the image and adds the labeled image into a corpus of training images for retraining the model.

Systems and methods take into account the criticality of workloads, the warranty needs of workloads, the warranty available time, and the lifetime of a workload to provide an optimal solution that ensures servers are used to highest extent. The warranty health of servers is computed and categorized as critical, warning, or healthy based on the number of days remaining in warranty. Workloads are tagged as short-term or long-term workloads. Workloads are also classified based on criticality. The quarantine mode for proactive high availability of servers is divided into multiple modes, including a long-time, critical-workload quarantine mode, a critical-workload quarantine mode, and a standard quarantine mode. Servers that are in quarantine mode are assigned new workloads based upon the warranty health, workload term, and workload criticality.