Search Results for author: Shital Shah

Found 12 papers, 7 papers with code

LiteTransformerSearch: Training-free On-device Search for Efficient Autoregressive Language Models

1 code implementation4 Mar 2022 Mojan Javaheripi, Shital Shah, Subhabrata Mukherjee, Tomasz L. Religa, Caio C. T. Mendes, Gustavo H. de Rosa, Sebastien Bubeck, Farinaz Koushanfar, Debadeepta Dey

In this work, we leverage the somewhat surprising empirical observation that the number of non-embedding parameters in autoregressive transformers has a high rank correlation with task performance, irrespective of the architectural hyperparameters.

Language Modelling

Ranking Convolutional Architectures by their Feature Extraction Capabilities

no code implementations29 Sep 2021 Debadeepta Dey, Shital Shah, Sebastien Bubeck

We propose a simple but powerful method which we call FEAR, for ranking architectures in any search space.

Neural Architecture Search

FEAR: A Simple Lightweight Method to Rank Architectures

1 code implementation7 Jun 2021 Debadeepta Dey, Shital Shah, Sebastien Bubeck

We propose a simple but powerful method which we call FEAR, for ranking architectures in any search space.

Neural Architecture Search

Ranking Architectures by Feature Extraction Capabilities

no code implementations ICML Workshop AutoML 2021 Debadeepta Dey, Shital Shah, Sebastien Bubeck

By training different architectures in the search space to the same training or validation error and subsequently comparing the usefulness of the features extracted on the task-dataset of interest by freezing most of the architecture we obtain quick estimates of the relative performance.

Neural Architecture Search

Understanding Failures of Deep Networks via Robust Feature Extraction

1 code implementation CVPR 2021 Sahil Singla, Besmira Nushi, Shital Shah, Ece Kamar, Eric Horvitz

Traditional evaluation metrics for learned models that report aggregate scores over a test set are insufficient for surfacing important and informative patterns of failure over features and instances.

An Empirical Analysis of Backward Compatibility in Machine Learning Systems

no code implementations11 Aug 2020 Megha Srivastava, Besmira Nushi, Ece Kamar, Shital Shah, Eric Horvitz

In many applications of machine learning (ML), updates are performed with the goal of enhancing model performance.

A System for Real-Time Interactive Analysis of Deep Learning Training

1 code implementation5 Jan 2020 Shital Shah, Roland Fernandez, Steven Drucker

To achieve this, we model various exploratory inspection and diagnostic tasks for deep learning training processes as specifications for streams using a map-reduce paradigm with which many data scientists are already familiar.

3D Action Recognition

A High-Fidelity Open Embodied Avatar with Lip Syncing and Expression Capabilities

1 code implementation19 Sep 2019 Deepali Aneja, Daniel McDuff, Shital Shah

Embodied avatars as virtual agents have many applications and provide benefits over disembodied agents, allowing non-verbal social and interactional cues to be leveraged, in a similar manner to how humans interact with each other.

AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

23 code implementations15 May 2017 Shital Shah, Debadeepta Dey, Chris Lovett, Ashish Kapoor

Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process.

Autonomous Vehicles

Submodular Trajectory Optimization for Aerial 3D Scanning

no code implementations ICCV 2017 Mike Roberts, Debadeepta Dey, Anh Truong, Sudipta Sinha, Shital Shah, Ashish Kapoor, Pat Hanrahan, Neel Joshi

Drones equipped with cameras are emerging as a powerful tool for large-scale aerial 3D scanning, but existing automatic flight planners do not exploit all available information about the scene, and can therefore produce inaccurate and incomplete 3D models.

Trajectory Planning

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