Abstract: Systems, computer-implemented methods, and computer program products that facilitate task-dependent analysis of various types of data graphs, based at least on generation of a random graph based on node embeddings corresponding to a data graph, and compution of a graph feature matrix corresponding to the data graph based on a distance between the random graph and the data graph.

Abstract: Aspects described herein include a method of conversational machine reading comprehension, as well as an associated system and computer program product. The method comprises receiving a plurality of questions relating to a context, and generating a sequence of context graphs. Each of the context graphs includes encoded representations of: (i) the context, (ii) a respective question of the plurality of questions, and (iii) a respective conversation history reflecting: (a) one or more previous questions relative to the respective question, and (b) one or more previous answers to the one or more previous questions. The method further comprises identifying, using at least one graph neural network, one or more temporal dependencies between adjacent context graphs of the sequence. The method further comprises predicting, based at least on the one or more temporal dependencies, an answer for a first question of the plurality of questions.

Abstract: Aspects described herein include a method of semantic parsing, and related system and computer program product. The method comprises receiving an input comprising a plurality of words, generating a structured representation of the plurality of words, encoding the structured representation into a latent embedding space, and decoding the encoded structured representation from the latent embedding space into a logical representation of the plurality of words.

Abstract: A semantic parsing method using a graph-to-sequence model, system, and computer program product include generating a syntactic graph for a sentence, generating node embeddings for each node based on other nodes the each node is connected to in the syntactic graph, generating a graph embedding over the node embeddings, performing attention-based recurrent neural network (RNN) decoding of the graph embedding and the node embeddings, and providing a logical translation of the sentence based on the decoding.

Abstract: A block coordinate descent method, system, and computer program product for partitioning a global feature matrix into blocks, each node of the nodes of the blocks having a block size of a number of the blocks over a number of the nodes, selecting, at each node, a subset of the blocks from the blocks, and in one of the nodes, launching a thread to simultaneously update a closed-form solution by minimizing a single coordinate in one of the blocks.

Abstract: A method (and structure and computer product) of machine translation for processing input questions includes receiving, in a processor on a computer, an input question presented in a natural language. The input question is preprocessed to find one or more condition values for possible Structured Query Language (SQL) queries. One or more possible SQL queries are enumerated based on the one or more found condition values and a paraphrasing model is used to rank the enumerated SQL queries. The highest ranked SQL query is executed against a relational database to search for a response to the input question.

Abstract: An adversarial robustness testing method, system, and computer program product include testing a robustness of a black-box system under different access settings via an accelerator.

Abstract: A method of machine translation includes receiving a query as input data. The input data is converted, using a processor on a computer, into a graph.

Abstract: Systems, computer-implemented methods, and computer program products that facilitate random graph embedding components are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a random graph component that can generate a random graph based on node embeddings corresponding to a data graph. The computer executable components can further comprise a graph embedding component that can compute a graph feature matrix corresponding to the data graph based on a distance between the random graph and the data graph.

Abstract: A semantic parsing method using a graph-to-sequence model, system, and computer program product include generating a syntactic graph for a sentence, generating node embeddings for each node based on other nodes the each node is connected to in the syntactic graph, generating a graph embedding over the node embeddings, performing attention-based recurrent neural network (RNN) decoding of the graph embedding and the node embeddings, and providing a logical translation of the sentence based on the decoding

Abstract: A method and system of analyzing a symbolic sequence is provided. Metadata of a symbolic sequence is received from a computing device of an owner. A set of R random sequences are generated based on the received metadata and sent to the computing device of the owner of the symbolic sequence for computation of a feature matrix based on the set of R random sequences and the symbolic sequence. The feature matrix is received from the computing device of the owner. Upon determining that an inner product of the feature matrix is below a threshold accuracy, the iterative process returns to generating R random sequences. Upon determining that the inner product of the feature matrix is at or above the threshold accuracy, the feature matrix is categorized based on machine learning. The categorized global feature matrix is sent to be displayed on a user interface of the computing device of the owner.

Abstract: Techniques that facilitate graph similarity analytics are provided. In one example, a system includes an information component and a similarity component. The information component generates a first information index indicative of a first entropy measure for a first graph-structured dataset associated with a machine learning system. The information component also generates a second information index indicative of a second entropy measure for a second graph-structured dataset associated with the machine learning system. The similarity component determines similarity between the first graph-structured dataset and the second graph-structured dataset based on a graph similarity computation associated with the first information index and the second information index.

Abstract: Technologies for interpretable general reasoning system using key value memory networks are described. Aspects include processing a complex question having at least two subject and relation pairs into keys in key memory locations, and importing entities of a knowledge base as values into value memory locations based on the keys and importing a STOP key. Other aspects include generating a query representation, a key representation of the keys in the key memory locations, and a value representation of the values in the value memory locations; and updating the query representation into an updated query representation over one or more iterations by combining the query representation with the value representation and the key representations until the STOP key is detected.

Abstract: A method and system of creating a model for large scale data analytics is provided. Training data is received in a form of a data matrix X and partitioned into a plurality of partitions. A random matrix T is generated. A feature matrix is determined based on multiplying the partitioned training data by the random matrix T. A predicted data {tilde over (y)} is determined for each partition via a stochastic average gradient (SAG) of each partition. A number of SAG values is reduced based on a number of rows n in the data matrix X. For each iteration, a sum of the reduced SAG values is determined, as well as a full gradient based on the sum of the reduced SAG values from all rows n, by distributed parallel processing. The model parameters w are updated based on the full gradient for each partition.

Abstract: Embodiments of the present invention provide a computer-implemented method for performing unsupervised feature representation learning for text data. The method generates reference text data having a set of random text sequences, in which each text sequence of set of random text sequences is of a random length and comprises a number of random words, and in which each random length is sampled from a minimum length to a maximum length. The random words of each text sequence in the set are drawn from a distribution. The method generates a feature matrix for raw text data based at least in part on a set of computed distances between the set of random text sequences and the raw text data. The method provides the feature matrix as an input to one or more machine learning models.

Abstract: Embodiments of the present invention provide a computer-implemented method for performing unsupervised time-series feature learning. The method generates a set of reference time-series of random lengths, in which each length is uniformly sampled from a predetermined minimum length to a predetermined maximum length, and in which values of each reference time-series in the set are drawn from a distribution. The method generates a feature matrix for raw time-series data based on a set of computed distances between the generated set of reference time-series and the raw time-series data. The method provides the feature matrix as an input to one or more machine learning models.

Abstract: A random binning featurization process method, system, and computer program product for a distributed random binning featurization process on one or more multicore systems with a hybrid two-level parallelism, the method including in a training phase, receiving a first data matrix dividing the random binning featurization process into two orthogonal levels, in a high-level generating a randomized number of high-dimension grids and evenly partitioning the grids into nodes in a parallel system, and in a low-level, evenly partitioning dimensions in each grid to construct look-up tables of index vectors and compute a local feature matrix for each node.

Abstract: A block coordinate descent method, system, and computer program product for partitioning a global feature matrix into blocks, each node of the nodes of the blocks having a block size of a number of the blocks over a number of the nodes, selecting, at each node, a subset of the blocks from the blocks, and in one of the nodes, launching a thread to simultaneously update a closed-form solution by minimizing a single coordinate in one of the blocks.