International Business Machines Corporation

Ismail Yunus Akhalwaya

Naweed Aghmad Khan

Francois Pierre Luus

Ndivhuwo Makondo

Lior Horesh

Mark Steven Squillante

Kenneth Lee Clarkson

Toby Kurien

Etienne Eben Vos

Ryan Nelson Riegel

Komminist S Weldemariam

Vasileios Kalantzis

Shashanka Ubaru

Alexander Gray

Richard Allen Young

Ashley D Gritzman

Melvin Mathew Varughese

Francois P S Luus

Tal Kachman

Maletsabisa Molapo

Juliet K Mutahi

Francisco Barahona

Lucile Ter-Minassian

Arun Aniyan

Monica Obrocka

Techniques and a system to facilitate estimation of a quantum phase, and more specifically, to facilitate estimation of an expectation value of a quantum state, by utilizing a hybrid of quantum and classical methods are provided. In one example, a system is provided. The 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 include an encoding component and a learning component. The encoding component can encode an expectation value associated with a quantum state. The learning component can utilize stochastic inference to determine the expectation value based on an uncollapsed eigenvalue pair.

Short-depth active learning quantum amplitude estimation without eigenstate collapse

A system for configuring and using a logical neural network including a graph syntax tree of formulae in a represented knowledgebase connected to each other via nodes representing each proposition. One neuron exists for each logical connective occurring in each formula and, additionally, one neuron for each unique proposition occurring in any formula. All neurons return pairs of values representing upper and lower bounds on truth values of their corresponding subformulae and propositions. Neurons corresponding to logical connectives accept as input the output of neurons corresponding to their operands and have activation functions configured to match the connectives' truth functions. Neurons corresponding to propositions accept as input the output of neurons established as proofs of bounds on the propositions' truth values and have activation functions configured to aggregate the tightest such bounds. Bidirectional inference permits every occurrence of each proposition in each formula to be used as a potential proof.

First-order logical neural networks with bidirectional inference

An ontology topic is selected and a pretrained predictive language model is primed to create a predictive primed model based on one or more ontological rules corresponding to the selected ontology topic. Using the predictive primed model, natural language text is generated based on the ontology topic and guidance of a prediction steering component. The predictive primed model is guided in selecting text that is predicted to be appropriate for the ontology topic and the generated natural language text. The generated natural language text is processed to generate extracted ontology rules and the extracted ontology rules are compared to one or more rules of an ontology rule database that correspond to the ontology topic. A check is performed to determine if a performance of the ontology extractor is acceptable.

Generative ontology learning and natural language processing with predictive language models

Methods and systems for generating representative data. A generator is configured to create, using a learning model, one or more generated records based on a plurality of training records obtained from a sensitive database. A discriminator is trained to identify the generated records as being generated based on the training records and a privacy adversary is trained to identify a training sample as being more similar to a distribution of the generated records than a distribution of the reference records.

Generation of representative data to preserve membership privacy

Systems, computer-implemented methods, and computer program products that can facilitate quantum topological classification are described. 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 topological component that employs one or more quantum computing operations to identify one or more persistent homology features of a topological simplicial structure. The computer executable components can further comprise a topological classifier component that employs one or more machine learning models to classify the topological simplicial structure based on the one or more persistent homology features.

Quantum topological classification

Word sense disambiguation using a glossary layer embedded in a deep neural network includes receiving, by one or more processors, input sentences including a plurality of words. At least two words in the plurality of words are homonyms. The one or more processors convert the plurality of words associated with each input sentence into a first vector including possible senses for the at least two words. The first vector is then combined with a second vector including a domain-specific contextual vector associated with the at least two words. The combination of the first vector with the second vector is fed into a recurrent deep logico-neural network model to generate a third vector that includes word senses for the at least two words. A threshold is set for the third vector to generate a fourth vector including a final word sense vector for the at least two words.

Word sense disambiguation using a deep logico-neural network

A media stream is delivered to a searcher by identifying a desirable emotional state for the searcher; identifying a current emotional state of the searcher; and estimating a target emotional trajectory that begins with the current emotional state and concludes with the desirable emotional state. Then the target emotional trajectory is matched to an aggregate emotional trajectory of the media stream; the media stream is recommended to the searcher in response to the matching; and a bit stream of the media stream is rendered to the searcher in response to the searcher's acceptance of the recommendation.

Media stream delivery

Maximum expressivity can be received representing a ratio between maximum and minimum input weights to a neuron of a neural network implementing a weighted real-valued logic gate. Operator arity can be received associated with the neuron. Logical constraints associated with the weighted real-valued logic gate can be determined in terms of weights associated with inputs to the neuron, a threshold-of-truth, and a neuron threshold for activation. The threshold-of-truth can be determined as a parameter used in an activation function of the neuron, based on solving an activation optimization formulated based on the logical constraints, the activation optimization maximizing a product of expressivity representing a distribution width of input weights to the neuron and gradient quality for the neuron given the operator arity and the maximum expressivity. The neural network of logical neurons can be trained using the activation function at the neuron, the activation function using the determined threshold-of-truth.

Optimizing capacity and learning of weighted real-valued logic

Methods and systems for operating a machine learning system are described. In an example, a device can receive an image and assign a set of pixels in the image as a digital representation of a livestock. The device can further train a machine learning model using the digital representation. The device can further run the machine learning model to generate prediction data relating to the livestock. The device can further generate output data relating to at least one activity among a livestock value chain. The at least one activity can correspond to a process to generate a commodity based on the livestock.

Machine learning models of livestock value chain

One or more application forms can be hosted, which application forms are received from a service provider, the service provider having been authenticated by at least one data custodian of a set of data custodians. One or more application forms include at least form fields to be populated with information. A user selection of an application form to be automatically populated can be received, the user having been authenticated by at least one data custodian of the set of data custodians. Data request is sent to at least one data custodian of the set of data custodians for automatically populating at least some of the form fields. Received data from said at least one data custodian can be collated and used to populate one or more form fields. The populated form fields can be returned to the service provider, for instance, provided a minimum requirement is met.

Automatic form completion from a set of federated data providers

Language models may be run with an input set of words in a given sentence. Each of the language models can predict a set of next candidate words to follow the input set or words. Based on the sets of next candidate words predicted by the language models and an actual next word, language guidance can be provided.

Automated feedback in online language exercises

A computer-implemented method and computer system for supervised machine learning and classification comprises applying, by a dimensional visualization module incorporating a global objective function, an unsupervised dimension reduction algorithm to a dataset including a plurality of data points capable of visual representation to produce a dimensionally reduced dataset and parsing, by the dimensional visualization module, the dimensionally reduced dataset to a user interface module for visual display of the dimensionally reduced dataset. The method comprises receiving a user input indicative of a categorization of at least one of the data points within the dataset, applying, by the dimensional visualization module, the global objective function weighted in accordance with the indicative categorization, to the dimensionally reduced dataset to produce a user-enhanced dimensionally reduced dataset, and parsing, by the dimensional visualization module, the user-enhanced dimensionally reduced dataset to the user interface module for visual display of the dimensionally reduced dataset.

Computer-implemented method and computer system for supervised machine learning

A computer-implemented method to identify RPI (Radio Frequency Interference) in a spectrogram includes dividing, by a matrix module of a computer processor, the spectrogram into a plurality of blocks, each block comprising a matrix of data points. The method includes applying, by a masking module of the computer processor, a plurality of matrix pooling functions to each block, the plurality of matrix pooling functions configured to produce a plurality of summarized pools. The method includes creating, by a masking module of the computer processor, a line mask for each of the pools and creating, by the masking module, an RPI mask based at least on the line masks, the RPI mask being indicative of RPI.

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Emmarentia, South Africa

Ismail Yunus Akhalwaya