Search Results for author: Shreya Shankar

Found 7 papers, 1 papers with code

Revisiting Prompt Engineering via Declarative Crowdsourcing

no code implementations7 Aug 2023 Aditya G. Parameswaran, Shreya Shankar, Parth Asawa, Naman jain, Yujie Wang

Large language models (LLMs) are incredibly powerful at comprehending and generating data in the form of text, but are brittle and error-prone.

Entity Resolution Imputation +1

Operationalizing Machine Learning: An Interview Study

no code implementations16 Sep 2022 Shreya Shankar, Rolando Garcia, Joseph M. Hellerstein, Aditya G. Parameswaran

Organizations rely on machine learning engineers (MLEs) to operationalize ML, i. e., deploy and maintain ML pipelines in production.

Autonomous Vehicles

Rethinking Streaming Machine Learning Evaluation

no code implementations23 May 2022 Shreya Shankar, Bernease Herman, Aditya G. Parameswaran

While most work on evaluating machine learning (ML) models focuses on computing accuracy on batches of data, tracking accuracy alone in a streaming setting (i. e., unbounded, timestamp-ordered datasets) fails to appropriately identify when models are performing unexpectedly.

BIG-bench Machine Learning Position

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming

2 code implementations NeurIPS 2020 Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, aditi raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian Goodfellow, Percy Liang, Pushmeet Kohli

In this work, we propose a first-order dual SDP algorithm that (1) requires memory only linear in the total number of network activations, (2) only requires a fixed number of forward/backward passes through the network per iteration.

Optimal Transfer Learning Model for Binary Classification of Funduscopic Images through Simple Heuristics

no code implementations11 Feb 2020 Rohit Jammula, Vishnu Rajan Tejus, Shreya Shankar

Deep learning models have the capacity to fundamentally revolutionize medical imaging analysis, and they have particularly interesting applications in computer-aided diagnosis.

Binary Classification General Classification +2

Adversarial Examples that Fool both Computer Vision and Time-Limited Humans

no code implementations NeurIPS 2018 Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein

Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich.

BIG-bench Machine Learning Open-Ended Question Answering

No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World

no code implementations22 Nov 2017 Shreya Shankar, Yoni Halpern, Eric Breck, James Atwood, Jimbo Wilson, D. Sculley

Further, we analyze classifiers trained on these data sets to assess the impact of these training distributions and find strong differences in the relative performance on images from different locales.

General Classification

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