Amazon Technologies, Inc.

Columbia University

Andrew Oliver Arnold

Bing Xiang

Ramesh M Nallapati

Atul Deo

Matthew Lee

Srinivas Iragavarapu

Ankur Deepak Desai

Salvatore J Stolfo

Sudipta Sengupta

Eleazar Eskin

Parminder Bhatia

Yuchen Tian

Henghui Zhu

Dejiao Zhang

Zijian Wang

Yanitsa Donchev

Praphruetpong Athiwaratkun

Leonid Portnoy

Michael Prerau

Wei Xiao

Rama Krishna Sandeep Pokkunuri

Alexander Johannes Smola

Zhiguo Wang

Cicero Nogueira Dos Santos

Patrick Ng

Anuroop Arora

Yifan Gao

Xiaofei Ma

Vamshidhar Krishnamurthy Dantu

Sri Ranga Akhilesh Bontala

Feng Nan

Kaustubh Kishor Khanke

Mingyue Shang

Zixuan Lin

Ramana Keerthi

Hantian Ding

Wasi Uddin Ahmad

Sathish Arumugam Selvaraj

Venkat Rakshith Reddy Swamireddy

Lei Gao

Wei Fang

Code completion suggestions may be proactively obtained and validated. An event that triggers obtaining a code completion suggestion for inclusion in a code file being edited using an integrated development environment may be detected. The code completion suggestion may be obtained. The characters of the code completion suggestion may be compared with characters added to the code file after the detection of the event that triggered obtaining the code completion suggestion to determine whether the code completion suggestion is valid. A valid code completion suggestion may then be displayed.

Validating and providing proactively generated code completion suggestions for code development

Systems and methods develop and apply one or more extractive summarization models for locating contact center conversation details in a transcript, extracting pertinent verbiage, and, in a transformation of the communication details, automatically generating summaries at one or more levels of abstraction, the summaries in full sentences, in a manner that a contact center agent understands. The models are trained using machine learning algorithms.

Systems and methods for automated communication summarization

Techniques for predicting an answer to a question using a machine learning model are described. In some examples, the model predicts one or more answers to the question by: predicting at least two answers to the question using a first component of the question-answer model from a set of passages, generating, using a second component of the question-answer model, at least one question for each of the predicted at least two answers, and performing roundtrip predictions until each generated question only has one answer.

Questions disambiguation using generative evidence fusion and round-trip prediction

Evaluation data sets may be programmatically generated for code generation models. An evaluation data set is obtained that includes items that correspond to different evaluation tests for a code generation system. The individual items of the evaluation data set maybe converted, including the conversion of a function signature for the items, the test statements for the items and using a code generation system to generate the body of the function.

Programmatically generating evaluation data sets for code generation models

Pre-fix matching may constrain the generation of next token predictions. Input text to perform a next token prediction may be received. Multiple tokens may be determined from the input text, including a partial token. From possible tokens, one or more matching possible tokens with the partial token may be identified. Next token predictions may then be filtered using the identified possible tokens in order to ensure that the partial token is matched.

Constrained prefix matching for generating next token predictions

A method for unsupervised anomaly detection, which are algorithms that are designed to process unlabeled data. Data elements are mapped to a feature space which is typically a vector space. Anomalies are detected by determining which points lies in sparse regions of the feature space. Two feature maps are used for mapping data elements to a feature apace. A first map is a data-dependent normalization feature map which we apply to network connections. A second feature map is a spectrum kernel which we apply to system call traces.

Methods of unsupervised anomaly detection using a geometric framework

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New York, NY, United States of America

Andrew Oliver Arnold