International Business Machines Corporation

Visa International Service Association

Western Digital (fremont), Inc.

Oracle International Corporation

Google Inc.

Hewlett-packard Development Company, L.p.

Palo Alto Research Center Inc.

Axt, Inc.

Facebook, Inc.

Hewlett Packard Enterprise Development LP

Nuance Communications, Inc.

Quantumscape Corporation

Visa International Service Application

Harbin Biyan Technology Co., Ltd.

Wei Zhang

Liang Wang

Hao Yang

Yan Zheng

Li Feng Zhang

Xiaodong Cui

Liang Gou

Yufei Ren

Michael Yeh

Yandong Wang

Junpeng Wang

Yingjian Chen

Alper Buyuktosunoglu

Abdullah Kayi

Amit Ganesh

Kothanda Umamageswaran

Sujatha Muthulingam

Mohamad Towfik Krounbi

Tong Zhang

Ming Mao

Satoru Araki

Fei Wang

Ulrich Alfons Finkler

Yuqing Gao

Michel H T Hack

Lingfei Wu

Niloy Mukherjee

Jia Shi

James M A Begole

Michelle Xue Zhou

Hongping Yuan

Guanxiong Li

Hugh Craig Hiner

Liang Gu

Benjamin P Chen

Weiguo Liu

Ying Zhang

Takashi Matsumoto

Lei Wang

Daniel R Tretter

Rui Zhang

Salih Burak Gokturk

Minsik Cho

Jinqiu Zhang

Juan Liu

Yada Zhu

Barton Allen Smith

Krishnan Meiyyappan

Maurice Kyojin Chu

David Shing-Ki Kung

Danning Yang

Yang Yu

Nicolas Scheffer

Murray Scott Campbell

Baris Sumengen

Brant Nease

Ying Hai Chen

Yimin Guo

Wei Tan

Mark Alan Lavin

Shaoping Li

Tim Holme

Mahendra Pakala

Feng Q Liu

Chia-Yu Chen

Rainer Johannes Fasching

Tarun Kumar

Harumi Kuno

Sining Mao

Siyuan Lu

Martin J Hirzel

Yun-Fei Li

Hai Sun

Xiaohong Gong

Dheeraj Pandey

Dmitry Mikhailovich Potapov

Kevin Wilson Warren

Wei Gao

Alexander Tsukerman

Kumar Bhaskaran

Chiranjeet Chetia

Marcus Oliver Freitag

Conrad M Albrecht

Lingyun Miao

Anup Roy

Andrew Paul Breen

Evgeniy Gabrilovich

David Paul Grove

Minghua Xu

Zhihong Zhang

Neil J S MacNaughton

Yuhang Wu

Xiaoyu Yang

Zuoyu Tao

Kevin Patrick Murphy

Ildar Khabibrakhmanov

Krishna Kunchithapadam

Aditya Pal

Vijay Sridharan

Upendra D Chitnis

Methods and wafers for vertical gradient freeze 8 inch gallium arsenide (GaAs) substrates. In disclosed examples, vertical gradient freeze systems for forming gallium arsenide (GaAs) substrates having silicon as a dopant, the system includes a crucible to contain a GaAs liquid melt and seed material during a formation process; one or more heating coils arranged in a plurality of heating zones; and a pedestal to move relative to the crucible, the system operable to control heating of the plurality of heating zones and movement of the pedestal to form a single crystal GaAs substrate.

Method and system for vertical gradient freeze 8 inch gallium arsenide substrates

An artificial intelligence (AI) platform to support random action replay for natural language (NL) learning. A NL conversation is subject to exploration to train a neural network. One or more tuples are leveraged for the training, with each tuple representing an input action, a vector, an output action, and a reward value. An action is sampled from the vector, with the sampling configured to assess a corresponding first gradient. The first gradient is applied to selectively adjust the neural network. As NL input is received and applied to the selectively adjusted neural network, an output corresponding to the NL input is identified and a corresponding action is subject to be executed.

Random action replay for reinforcement learning

A machine learning model can be trained to predict one or more financial indicators using earnings call transcripts augmented with counterfactual information. Using faithful gradient-based method, prediction results with respect to a particular counterfactual information can be explained. Based on the explanation, the counterfactual information determined to have most impact on prediction results can be selected for updating the machine learning model.

Counterfactual neural network learning for contextual enhanced earnings call analysis

Provided are systems for generating a machine learning model for classification tasks using unadversarial training that include a processor to perform an unadversarial training procedure to train a machine learning model to provide a trained machine learning model. When performing the unadversarial training procedure, the processor is programmed or configured to receive a training dataset including a plurality of training samples; generate a noise vector for the plurality of training samples based on a uniform distribution; perturb each training sample of the plurality of training samples; obtain a gradient; generate an updated noise vector based on the gradient; perturb each training sample of the plurality of training samples based on the updated noise vector; and update a model weight of the machine learning model based on the second plurality of perturbed training samples to provide the trained machine learning model. Methods and computer program products are also provided.

System, method, and computer program product for reducing dataset biases in natural language inference tasks using unadversarial training

Provided is a method for normalizing embeddings for cross-embedding alignment. The method may include applying mean centering to the at least one embedding set, applying spectral normalization to the at least one embedding set, and/or applying length normalization to the at least one embedding set. Spectral normalization may include decomposing the at least one embedding set, determining an average singular value of the at least one embedding set, determining a respective substitute singular value for each respective singular value of a diagonal matrix, and/or replacing the at least one embedding set with a product of the at least one embedding set, a right singular vector, and an inverse of the substitute diagonal matrix. The mean centering, spectral normalization, and/or length normalization may be iteratively repeated for a configurable number of iterations. A system and computer program product are also disclosed.

Method, system, and computer program product for normalizing embeddings for cross-embedding alignment

An approach to determine the quality of encodings assigned to a word by a word embedding model. The approach may include determining the asymmetry of two embeddings associated with two words from a word embedding model. The asymmetry of the two words from a preexisting evocation dataset may be determined. The asymmetry of the two embeddings may be compared to the asymmetry from the evocation dataset to generate an encoding quality score.

Word embedding quality assessment through asymmetry

Provided is a system for analyzing features associated with entities using an embedding tree, the system including at least one processor programmed or configured to receive a dataset associated with a plurality of entities, wherein the dataset comprises a plurality of data instances for a plurality of entities. The processor may be programmed or configured to generate at least two embeddings based on the dataset and determine split criteria for partitioning an embedding space of at least one embedding tree associated with the dataset based on feature data associated with an entity and embedding data associated with the at least two embeddings. The processor may be programmed or configured to generate at least one embedding tree having a plurality of nodes based on the split criteria. Methods and computer program products are also provided.

System, method, and computer program product for feature analysis using an embedding tree

Provided is a method for detecting group activities in a network. The method may include receiving interaction data associated with a plurality of interactions. For each account identifier associated with at least one interaction, a value may be determined for each of a first set of categories, and a vector may be generated based on the value for each category. The length of each vector may be determined. At least one relational graph may be generated based on the interaction data. Each relational graph may be associated with a respective category of a second set of categories. At least one cluster of nodes may be determined based on the relational graph(s). A score for each cluster may be determined based on the length of the vector associated with the account identifier of each node of the cluster of nodes. A system and computer program product are also disclosed.

Method, system, and computer program product for detecting group activities in a network

Described are a system, method, and computer program product for dynamic node classification in temporal-based machine learning classification models. The method includes receiving graph data of a discrete time dynamic graph including graph snapshots, and node classifications associated with all nodes in the discrete time dynamic graph. The method includes converting the discrete time dynamic graph to a time-augmented spatio-temporal graph and generating an adjacency matrix based on a temporal walk of the time-augmented spatio-temporal graph. The method includes generating an adaptive information transition matrix based on the adjacency matrix and determining feature vectors based on the nodes and the node attribute matrix of each graph snapshot. The method includes generating and propagating initial node representations across information propagation layers using the adaptive information transition matrix and classifying a node of the discrete time dynamic graph subsequent to the first time period based on final node representations.

System, method, and computer program product for dynamic node classification in temporal-based machine learning classification models

A method includes extracting, by an analysis computer, a plurality of first datasets from a plurality of graph snapshots using a structural self-attention module. The analysis computer can then extract at least a second dataset from the plurality of first datasets using a temporal self-attention module across the plurality of graph snapshots. The analysis computer can then perform graph context prediction with at least the second dataset.

Dynamic graph representation learning via attention networks

Embodiments for training a recommendation system to provide merchant recommendations comprise receiving, by a processor, raw merchant embeddings and raw user embeddings generated from payment transaction records, wherein the raw merchant embeddings include a plurality of embedded features. A generative adversarial network (GAN) is trained to generate modified merchant embeddings from the raw merchant embeddings, where the modified embeddings remove a location feature. Subsequent to training and responsive to receiving a request for merchant recommendations in the target location for the target user, the GAN and a trained preference model are used to generate a list of merchant rankings based on a new set of modified merchant embeddings, past preferences of a target user, and the target location to recommend merchants in the target location.

Unsupervised embeddings disentanglement using a gan for merchant recommendations

The system and method may assess the merchant risk level on a more continuous scale rather than a binary categorization. It may produce a continuous risk score proportional to the likelihood of a merchant being risky, effectively addressing the issue of shades of gray encountered by the traditional blacklisting approach. The continuous risk score feature provides greater flexibility as it allows the payment network to make dynamic pricing decisions (known as interchange optimization) based on the merchant risk level. Using collective intelligence from transactions across the payment network, the system and method may be able to assess the merchant risk level with high accuracy. The system and method may be particularly beneficial to small merchants with low transaction volume as even a few fraudulent transactions can easily put them in the high-risk merchant category. Further, the system and method may help payment processing networks make better decision on cross-border transactions.

Method and system for assessing the reputation of a merchant

Described are a system, method, and computer program product for multivariate event prediction using multi-stream recurrent neural networks. The method includes receiving event data from a sample time period and generating feature vectors for each subperiod of each day. The method also includes providing the feature vectors as inputs to a set of first recurrent neural network (RNN) models and generating first outputs for each RNN node. The method further includes merging the first outputs for each same subperiod to form aggregated time-series layers. The method further includes providing the aggregated time-series layers as an input to a second RNN model and generating final outputs for each RNN node of the second RNN model.

System, method, and computer program product for multivariate event prediction using multi-stream recurrent neural networks

Methods and wafers for low etch pit density, low slip line density, and low strain indium phosphide are disclosed and may include an indium phosphide single crystal wafer having a diameter of 4 inches or greater, having a measured etch pit density of less than 500 cm, and having fewer than 5 dislocations or slip lines as measured by x-ray diffraction imaging. The wafer may have a measured etch pit density of 200 cmor less, or 100 cmor less, or 10 cmor less. The wafer may have a diameter of 6 inches or greater. An area of the wafer with a measured etch pit density of zero may at least 80% of the total area of the surface. An area of the wafer with a measured etch pit density of zero may be at least 90% of the total area of the surface.

Low etch pit density, low slip line density, and low strain indium phosphide

A system, method, and computer program product for incorporating knowledge from more complex models in simpler models. A method may include obtaining first training data associated with a first set of features and second training data associated with a second set of features different than the first set of features; training a first model based on the first training data and the second training data; and training a second model, using a loss function that depends on an output of an intermediate layer of the first model and an output of the second model, based on the second training data.

System, method, and computer program product for incorporating knowledge from more complex models in simpler models

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Fremont, CA, United States of America

Wei Zhang