A method is provided. The method includes obtaining an enhanced state graph. The enhanced state graph represents a set of objects within an environment and a set of positions of the set of objects. The enhanced state graph includes a set of object nodes, a set of property nodes and a set of goal nodes to represent a set of objectives. The method also includes generating a set of instructions for a set of mechanical systems based on the enhanced state graph. The set of mechanical systems is configured to interact with one or more of the set of objects within the environment. The method further includes operating the set of mechanical systems to achieve the set of objectives based on the set of instructions.

A system and method for implementing a machine learning-based virtual dialogue agent includes computing an input embedding based on receiving a user input; computing, via a pre-trained machine learning language model, an embedding response inference based on the input embedding; searching, based on the embedding response inference, a response imprintation embedding space that includes a plurality of distinct embedding representations of potential text-based responses to the user input, wherein each of the plurality of distinct embedding representations is tethered to a distinct human-imprinted media response, and searching the response imprintation embedding space includes: searching the response imprintation embedding space based on an embedding search query, and returning a target embedding representation from the response imprintation embedding space based on the searching of the response imprintation embedding space; and executing, via a user interface of the machine learning-based virtual dialogue agent, a human-imprinted media response tethered to the target embedding representation.

An adaptive processing method for new scenes in autonomous driving, comprising: obtaining scene data corresponding to new scene of vehicle driving, wherein the scene data describes vehicles state and driving operations in the new scene; obtaining a test set of the new scene based on processing the scene data by a preset distribution; updating parameters of a pre-training model by inputting the test set, and obtaining a scene model adapted to the new scene based on gradient iteration of general model parameters of the pre-training model, wherein the scene model is configured to output an autonomous driving strategy for the vehicle in the new scene. Therefore, the autonomous driving vehicle transforms a new scene to a known scene, and no longer be troubled by unpredictable new scenes, and greatly enhance the reliability and stability of autonomous driving.

Example aspects of the present disclosure are directed to systems and methods for generation of improved language embeddings (e.g., entity embeddings for natural language tokens) which provide improved model performance. In addition, the proposed techniques require less computational consumption relative to previous approaches.

In a method for deriving existential rules from knowledge graph data, a processor represents a knowledge graph using a geometric embedding. A processor transforms the geometric embedding to a syllogism logic representation using a geometric relationship. And a processor derives existential rules using standard transformation rules present in the syllogism logic representation.

Approaches presented herein use ultrahyperbolic representations (e.g., non-Riemannian manifolds) in inferencing tasks—such as classification—performed by machine learning models (e.g., neural networks). For example, a machine learning model may receive, as input, a graph including data on which to perform an inferencing task. This input can be in the form of, for example, a set of nodes and an adjacency matrix, where the nodes can each correspond to a vector in the graph. The neural network can take this input and perform mapping in order to generate a representation of this graph using an ultrahyperbolic (e.g., non-parametric, pseudo- or semi-Riemannian) manifold. This manifold can be of constant non-zero curvature, generalizing to at least hyperbolic and elliptical geometries. Once such a manifold-based representation is obtained, the neural network can perform one or more inferencing tasks using this representation, such as for classification or animation.

A computer readable network analysis program of performing local modeling analysis of determining an estimated value of a current network quality corresponding to explanatory variable vector in current aggregated data based on a local model including local training data; determining an abnormality in the network based on whether or not a measured value of the current network quality is lower than a threshold; determining whether or not a distribution of the connections having the measured value of the network quality exceeding the threshold is present in a large size; extracting an individual-analysis-target connection group including more than predetermined proportions of connections in the distribution of the connections having the large size; and performing the local modeling analysis to the individual-analysis-target connection group and the remaining connection groups to determine the abnormality in the network.

A computer-implemented method utilizing a continuous discrete recurrent Kalman network, wherein the method includes receiving, at an encoder, an input from one or more sensors, wherein the input includes one or more time series data associating data at one or more points in time; outputting, to a Kalman filter, a latent observation and uncertainty estimate in response to the input at the encoder; determining a latent state prior and latent state posterior utilizing the Kalman filter; and outputting, via a decoder, a filtered observation utilizing at least the latent state posterior.

This application relates to apparatus and methods for determining confidence levels in associated data using machine learning algorithms. In some examples, a computing device may generate training graph data where each training graph connects at least two nodes by an edge, and each node represents data. The computing device may train a machine learning algorithm based on the generated training data. The computing device may then receive linked data, which associates at least two nodes, each representing data, with each other. The computing device may generate graph data based on the linking data, to provide to the machine learning algorithm as input. The computing device may then execute the machine learning algorithm on the generated graph data to generate values for each of its edges. The values may identify, for each edge, a confidence level in the connection between the two nodes for that edge.

Described herein are techniques for using a graph neural network to encode online job postings as embeddings. First, an input graph is defined by processing one or more rules to discover edges that connect nodes in an input graph, where the nodes of the input graph represent job postings or standardized job attributes, and the edges are determined based on analyzing a log of user activity directed to online job postings. Next, a graph neural network (GNN) is trained based on an edge prediction task. Finally, once trained, the GNN is used to derive node embeddings for the nodes (e.g., job postings) of the input graph, and in some instances, new online job postings not represented in the original input graph.