activity
20162019
most citedFaster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference

134 citations · 343 across the 10 of their papers we have counts for

collaborators

11 papers

cs.CL2019★ 5 cited

End-to-End Spoken Language Translation

Michelle Guo, Albert Haque, Prateek Verma

In this paper, we address the task of spoken language understanding. We present a method for translating spoken sentences from one language into spoken sentences in another languag…

cs.SD2019★ 5 cited

Audio-Linguistic Embeddings for Spoken Sentences

Albert Haque, Michelle Guo, Prateek Verma +1

We propose spoken sentence embeddings which capture both acoustic and linguistic content. While existing works operate at the character, phoneme, or word level, our method learns l…

cs.CV2018★ 99 cited

Measuring Depression Symptom Severity from Spoken Language and 3D Facial Expressions

Albert Haque, Michelle Guo, Adam S Miner +1

With more than 300 million people depressed worldwide, depression is a global problem. Due to access barriers such as social stigma, cost, and treatment availability, 60% of mental…

cs.CR2018★ 134 cited

Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference

Edward Chou, Josh Beal, Daniel Levy +3

Homomorphic encryption enables arbitrary computation over data while it remains encrypted. This privacy-preserving feature is attractive for machine learning, but requires signific…

cs.CR2018★ 4 cited

A Fully Private Pipeline for Deep Learning on Electronic Health Records

Edward Chou, Thao Nguyen, Josh Beal +2

We introduce an end-to-end private deep learning framework, applied to the task of predicting 30-day readmission from electronic health records. By using differential privacy durin…

cs.CV2018★ 14 cited

Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images

Edward Chou, Matthew Tan, Cherry Zou +4

Computer-vision hospital systems can greatly assist healthcare workers and improve medical facility treatment, but often face patient resistance due to the perceived intrusiveness…