Search Results for author: Kavya Srinet

Found 7 papers, 3 papers with code

droidlet: modular, heterogenous, multi-modal agents

1 code implementation25 Jan 2021 Anurag Pratik, Soumith Chintala, Kavya Srinet, Dhiraj Gandhi, Rebecca Qian, Yuxuan Sun, Ryan Drew, Sara Elkafrawy, Anoushka Tiwari, Tucker Hart, Mary Williamson, Abhinav Gupta, Arthur Szlam

In recent years, there have been significant advances in building end-to-end Machine Learning (ML) systems that learn at scale.

Why Build an Assistant in Minecraft?

1 code implementation22 Jul 2019 Arthur Szlam, Jonathan Gray, Kavya Srinet, Yacine Jernite, Armand Joulin, Gabriel Synnaeve, Douwe Kiela, Haonan Yu, Zhuoyuan Chen, Siddharth Goyal, Demi Guo, Danielle Rothermel, C. Lawrence Zitnick, Jason Weston

In this document we describe a rationale for a research program aimed at building an open "assistant" in the game Minecraft, in order to make progress on the problems of natural language understanding and learning from dialogue.

Minecraft Natural Language Understanding

CraftAssist: A Framework for Dialogue-enabled Interactive Agents

3 code implementations19 Jul 2019 Jonathan Gray, Kavya Srinet, Yacine Jernite, Haonan Yu, Zhuoyuan Chen, Demi Guo, Siddharth Goyal, C. Lawrence Zitnick, Arthur Szlam

This paper describes an implementation of a bot assistant in Minecraft, and the tools and platform allowing players to interact with the bot and to record those interactions.

Minecraft

CraftAssist Instruction Parsing: Semantic Parsing for a Minecraft Assistant

no code implementations17 Apr 2019 Yacine Jernite, Kavya Srinet, Jonathan Gray, Arthur Szlam

We propose a large scale semantic parsing dataset focused on instruction-driven communication with an agent in Minecraft.

Minecraft Semantic Parsing

Trace norm regularization and faster inference for embedded speech recognition RNNs

no code implementations ICLR 2018 Markus Kliegl, Siddharth Goyal, Kexin Zhao, Kavya Srinet, Mohammad Shoeybi

We propose and evaluate new techniques for compressing and speeding up dense matrix multiplications as found in the fully connected and recurrent layers of neural networks for embedded large vocabulary continuous speech recognition (LVCSR).

Large Vocabulary Continuous Speech Recognition Speech Recognition

Cannot find the paper you are looking for? You can Submit a new open access paper.