V
Vivek Srikumar
Researcher at University of Utah
Publications - 106
Citations - 5474
Vivek Srikumar is an academic researcher from University of Utah. The author has contributed to research in topics: Inference & Structured prediction. The author has an hindex of 26, co-authored 106 publications receiving 3650 citations. Previous affiliations of Vivek Srikumar include Allen Institute for Artificial Intelligence & Stanford University.
Papers
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Journal ArticleDOI
ISAAC: a convolutional neural network accelerator with in-situ analog arithmetic in crossbars
Ali Shafiee,Anirban Nag,Naveen Muralimanohar,Rajeev Balasubramonian,John Paul Strachan,Miao Hu,R. Stanley Williams,Vivek Srikumar +7 more
TL;DR: This work explores an in-situ processing approach, where memristor crossbar arrays not only store input weights, but are also used to perform dot-product operations in an analog manner.
Proceedings ArticleDOI
DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning
TL;DR: DeepLog, a deep neural network model utilizing Long Short-Term Memory (LSTM), is proposed, to model a system log as a natural language sequence, which allows DeepLog to automatically learn log patterns from normal execution, and detect anomalies when log patterns deviate from the model trained from log data under normal execution.
Journal ArticleDOI
Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks
TL;DR: A recurrent neural network model to make medium-to-long term predictions of electricity consumption profiles in commercial and residential buildings at one-hour resolution and uses the deep NN to perform imputation on an electricity consumption dataset containing segments of missing values is presented.
Proceedings Article
Importance of semantic representation: dataless classification
TL;DR: This paper introduces Dataless Classification, a learning protocol that uses world knowledge to induce classifiers without the need for any labeled data, and proposes a model for dataless classification and shows that the label name alone is often sufficient to induceclassifiers.
Proceedings ArticleDOI
Modeling Biological Processes for Reading Comprehension
Jonathan Berant,Vivek Srikumar,Pei-Chun Chen,Abby Vander Linden,Brittany Harding,Brad Huang,Peter Clark,Christopher D. Manning +7 more
TL;DR: This paper focuses on a new reading comprehension task that requires complex reasoning over a single document, and demonstrates that answering questions via predicted structures substantially improves accuracy over baselines that use shallower representations.