International Business Machines Corporation

Aurelie C Lozano

Naoki Abe

Vikas Sindhwani

Georgios Kollias

Fei Liu

Bonnie Kathryn Ray

Yan Liu

Payel Das

Shubir Kapoor

Shilpa N Mahatma

Grzegorz Michal Swirszcz

Huijing Jiang

Saharon Rosset

Floyd S Kelly

Jing Fu

David M Loehr

Michael G Gemmell

Subhajit Chaudhury

Elliot Nelson

A computerized system includes a generative large language model (LLM). The LLM includes an encoder configured to provide a vector input to a content addressable memory system and a decoder configured to receive a vector output from the content addressable memory system and generate a data output. The content addressable memory system is configured to receive the vector input and generate a vector output based on data contents of the content addressable memory system. The content addressable memory system is configured to respond to receiving at least one memory update command by updating the data contents of the content addressable memory system.

Dynamic updating of content addressable associative memories for large language models

Techniques for causal modeling. Historical feature data is received, relating to a plurality of nodes in a system. A machine learning (ML) model is generated, for a node of the plurality of nodes. The ML model is trained to predict a plurality of future feature values for the node based on at least a portion of the historical feature data. A causal graph is generated, for the plurality of nodes, using a feature selection mechanism within the ML model. The feature selection mechanism includes a regularization term encouraging sparsity of nodes in selected features in the ML model.

Non-linear causal modeling from diverse data sources

A computing system initializes a first frontier to be a root of a multi-dimensional hierarchical data structure representing an entity. The system acquires first data corresponding to the first frontier. The system performs modeling on the first data to obtain a first model and a corresponding first statistic. The system expands a dimension of the first frontier. The system gathers second data corresponding to the expanded frontier. The system applies the data modeling on the second data to obtain a second model and a corresponding second statistic. The system compares the first statistic of the first model and the second statistic of the second model. The system sets the second model to be the first model in response to determining that the second model statistic is better than the first model statistic. The system outputs the first model.

Casual modeling of multi-dimensional hierarchical metric cubes

Structural changes in causal relationship over time may be detected, for example, by a Markov switching vector autoregressive model that detects and infers the structural changes in the causal graphs.

Change point detection in causal modeling

In response to issues of high dimensionality and sparsity in machine learning, it is proposed to use a multiple output regression modeling module that takes into account information on groups of related predictor features and groups of related regressions, both given as input, and outputs a regression model with selected feature groups. Optionally, the method can be employed as a component in methods of causal influence detection, which are applied on a time series training data set representing the time-evolving content generated by community members, output a model of causal relationships and a ranking of the members according to their influence.

Group variable selection in spatiotemporal modeling

A computer model finds an originating community member or members, for instance amongst bloggers, scientific researchers, or phenomena in phenomenological systems. In one embodiment, non-explicit causal relationships may be inferred from comparing blogs as a whole. Influence relationships are derived from a weighted, directed graph output and sources of influence are ranked. This implementation is useful for a variety of applications.

Inferring influence and authority

Multi-class cost-sensitive boosting based on gradient boosting with 'p-norm' cost functionals' uses iterative example weighting schemes derived with respect to cost functionals, and a binary classification algorithm. Weighted sampling is iteratively applied from an expanded data set obtained by enhancing each example in the original data set with as many data points as there are possible labels for any single instance, and where each non-optimally labeled example is given the weight equaling a half times the original misclassification cost for the labeled example times the p&#x2212;1 norm of the average prediction of the current hypotheses. Each optimally labeled example is given the weight equaling the sum of the weights for all the non-optimally labeled examples for the same instance. Component classification algorithm is executed on a modified binary classification problem. A classifier hypothesis is output, which is the average of all the hypotheses output in the respective iterations.

Methods and systems for cost-sensitive boosting

A 'variable group selection' system and method in which constructs are based upon a training data set, a regression modeling module that takes into account information on groups of related predictor variables given as input and outputs a regression model with selected variable groups. Optionally, the method can be employed as a component in methods of temporal causal modeling, which are applied on a time series training data set, and output a model of causal relationships between the multiple times series in the data.

Growing community of inventors

Scarsdale, NY, United States of America

Aurelie C Lozano