Abstract: Systems, methods, and computer program products to provide credit decisioning based on graph neural networks. A lending network graph of a plurality of loans may be received, each loan associated with a creditor and one account. A first node of the graph may be associated with a first creditor and the second node may be associated with a first account. A graph neural network may receive a respective message from each node connected to the first node, each message comprising an embedding vector reflecting a current state of the node. The graph neural network may update weights for the first node in a forward pass. The graph neural network may receive a respective message from each node connected to the second node, each message comprising the embedding vector reflecting the current state of the node. The graph neural network may update weights for the second node in a backward pass.

Abstract: Systems and methods are provided for generating a training dataset for training a molecule embedding module using contrastive learning, wherein the definition of similarity is based on molecular scaffold similarity. For example, systems access a molecular dataset and separate the molecular dataset into positive samples and negative samples. Systems then generate a training dataset comprising the positive samples and negative samples. Systems and methods are also provided for using the trained molecule embedding module to generate molecule embeddings and for building an end-to-end machine learning model configured to perform molecular embedding analysis and molecular property prediction, the model comprising the trained molecule embedding module and a property prediction module.

Abstract: Systems, methods, and computer program products to provide credit decisioning based on graph neural networks. A lending network graph of a plurality of loans may be received, each loan associated with a creditor and one account. A first node of the graph may be associated with a first creditor and the second node may be associated with a first account. A graph neural network may receive a respective message from each node connected to the first node, each message comprising an embedding vector reflecting a current state of the node. The graph neural network may update weights for the first node in a forward pass. The graph neural network may receive a respective message from each node connected to the second node, each message comprising the embedding vector reflecting the current state of the node. The graph neural network may update weights for the second node in a backward pass.

Abstract: An Encoder-Decoder architecture uses two neural networks that work together to learn molecule embedding without any labeled data by transform the molecule graph to an embedding, and then mapping that embedding to a character-based representation of that molecule. An encoder operates as a molecule embedding model to produce a vector of length “n” that reperesents the molecule as a point in an n-dimentional cartesian space. The generated vector is used by a decoder to predict the molecule's character-based representation such as a SMILES, only based on the molecule structure. A loss function is applied to the decoded character-based representation compared to the actual character-based representation of that molecyle, to generate a gradient of the error determined by the loss function which is used to modify weights in the encoder-decoder model during training.

Abstract: An embedding model maps a graph representation of a molecule to an embedding space. The embedding model may include one or more graph neural network layers that use a message passing framework and one or more attention layers. The one or more attention layers may determine an edge weight for each message received by a receiving node from one or more sending nodes. The edge weight may be based on features of the receiving node and features of the one or more sending nodes. The one or more graph neural network layers may determine embedded features for the graph based on the messages and the edge weights. The embedding model may determine molecule features for the molecule based on the embedded features. The molecule features may map to an embedding space. The embedding model may be trained using multi-task training to generate a more generic embedding space.

Abstract: Systems, methods, and computer program products to provide credit decisioning based on graph neural networks. A lending network graph of a plurality of loans may be received, each loan associated with a creditor and one account. A first node of the graph may be associated with a first creditor and the second node may be associated with a first account. A graph neural network may receive a respective message from each node connected to the first node, each message comprising an embedding vector reflecting a current state of the node. The graph neural network may update weights for the first node in a forward pass. The graph neural network may receive a respective message from each node connected to the second node, each message comprising the embedding vector reflecting the current state of the node. The graph neural network may update weights for the second node in a backward pass.

Abstract: Systems, methods, and computer program products to provide credit decisioning based on graph neural networks. A lending network graph of a plurality of loans may be received, each loan associated with a creditor and one account. A first node of the graph may be associated with a first creditor and the second node may be associated with a first account. A graph neural network may receive a respective message from each node connected to the first node, each message comprising an embedding vector reflecting a current state of the node. The graph neural network may update weights for the first node in a forward pass. The graph neural network may receive a respective message from each node connected to the second node, each message comprising the embedding vector reflecting the current state of the node. The graph neural network may update weights for the second node in a backward pass.

Abstract: Systems, methods, and computer program products to provide credit decisioning based on graph neural networks. A lending network graph of a plurality of loans may be received, each loan associated with a creditor and one account. A first node of the graph may be associated with a first creditor and the second node may be associated with a first account. A graph neural network may receive a respective message from each node connected to the first node, each message comprising an embedding vector reflecting a current state of the node. The graph neural network may update weights for the first node in a forward pass. The graph neural network may receive a respective message from each node connected to the second node, each message comprising the embedding vector reflecting the current state of the node. The graph neural network may update weights for the second node in a backward pass.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for localization and recognition of text from images. For instance, a first method may include: receiving an image; processing the image through a convolutional backbone to obtain feature maps(s); processing the feature maps through a region of interest (RoI) network to obtain RoIs; filtering the RoIs through a filtering block to obtain final RoIs; and processing the final RoIs through a text recognition stack to obtain predicted character sequences for the final RoIs. A second method may include: constructing a text localization and recognition neural network (TLaRNN); obtaining training data; training the TLaRNN on the training data; and storing trained weights of the TLaRNN. The constructing the TLaRNN may include: connecting a convolutional backbone to a region of interest (RoI) network; connecting the RoI network to a filtering block; and connecting the filtering block to a text recognition network.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for localization and recognition of text from images. For instance, a first method may include: receiving an image; processing the image through a convolutional backbone to obtain feature maps(s); processing the feature maps through a region of interest (Rol) network to obtain Rols; filtering the Rols through a filtering block to obtain final Rols; and processing the final Rols through a text recognition stack to obtain predicted character sequences for the final Rols. A second method may include: constructing a text localization and recognition neural network (TLaRNN); obtaining training data; training the TLaRNN on the training data; and storing trained weights of the TLaRNN. The constructing the TLaRNN may include: connecting a convolutional backbone to a region of interest (Rol) network; connecting the Rol network to a filtering block; and connecting the filtering block to a text recognition network.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for localization and recognition of text from images. For instance, a first method may include: receiving an image; processing the image through a convolutional backbone to obtain feature maps(s); processing the feature maps through a region of interest (RoI) network to obtain RoIs; filtering the RoIs through a filtering block to obtain final RoIs; and processing the final RoIs through a text recognition stack to obtain predicted character sequences for the final RoIs. A second method may include: constructing a text localization and recognition neural network (TLaRNN); obtaining training data; training the TLaRNN on the training data; and storing trained weights of the TLaRNN. The constructing the TLaRNN may include: connecting a convolutional backbone to a region of interest (RoI) network; connecting the RoI network to a filtering block; and connecting the filtering block to a text recognition network.