Search Results for author: Suvrat Bhooshan

Found 5 papers, 4 papers with code

Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition

1 code implementation ACL 2020 Paloma Jeretic, Alex Warstadt, Suvrat Bhooshan, Adina Williams

We use IMPPRES to evaluate whether BERT, InferSent, and BOW NLI models trained on MultiNLI (Williams et al., 2018) learn to make pragmatic inferences.

Implicatures Natural Language Inference +3

Supervised Multimodal Bitransformers for Classifying Images and Text

6 code implementations6 Sep 2019 Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Ethan Perez, Davide Testuggine

Self-supervised bidirectional transformer models such as BERT have led to dramatic improvements in a wide variety of textual classification tasks.

 Ranked #1 on Natural Language Inference on V-SNLI (using extra training data)

General Classification Natural Language Inference

Needles in Haystacks: On Classifying Tiny Objects in Large Images

1 code implementation16 Aug 2019 Nick Pawlowski, Suvrat Bhooshan, Nicolas Ballas, Francesco Ciompi, Ben Glocker, Michal Drozdzal

In some important computer vision domains, such as medical or hyperspectral imaging, we care about the classification of tiny objects in large images.

Classification General Classification +2

Drive2Vec: Multiscale State-Space Embedding of Vehicular Sensor Data

no code implementations12 Jun 2018 David Hallac, Suvrat Bhooshan, Michael Chen, Kacem Abida, Rok Sosic, Jure Leskovec

With automobiles becoming increasingly reliant on sensors to perform various driving tasks, it is important to encode the relevant CAN bus sensor data in a way that captures the general state of the vehicle in a compact form.

ShortFuse: Biomedical Time Series Representations in the Presence of Structured Information

1 code implementation13 May 2017 Madalina Fiterau, Suvrat Bhooshan, Jason Fries, Charles Bournhonesque, Jennifer Hicks, Eni Halilaj, Christopher Ré, Scott Delp

In healthcare applications, temporal variables that encode movement, health status and longitudinal patient evolution are often accompanied by rich structured information such as demographics, diagnostics and medical exam data.

Time Series Time Series Analysis

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