Search Results for author: Dhaval Adjodah

Found 5 papers, 1 papers with code

Multilingual Detection of Personal Employment Status on Twitter

1 code implementation ACL 2022 Manuel Tonneau, Dhaval Adjodah, João Palotti, Nir Grinberg, Samuel Fraiberger

Detecting disclosures of individuals' employment status on social media can provide valuable information to match job seekers with suitable vacancies, offer social protection, or measure labor market flows.

Active Learning

A Study of Compositional Generalization in Neural Models

no code implementations16 Jun 2020 Tim Klinger, Dhaval Adjodah, Vincent Marois, Josh Joseph, Matthew Riemer, Alex 'Sandy' Pentland, Murray Campbell

One difficulty in the development of such models is the lack of benchmarks with clear compositional and relational task structure on which to systematically evaluate them.

Image Classification Relational Reasoning

Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning

no code implementations16 Feb 2019 Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey, Anirudh Goyal, Peter Krafft, Esteban Moro, Alex Pentland

A common technique to improve learning performance in deep reinforcement learning (DRL) and many other machine learning algorithms is to run multiple learning agents in parallel.

reinforcement-learning

How to Organize your Deep Reinforcement Learning Agents: The Importance of Communication Topology

no code implementations30 Nov 2018 Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey, Peter Krafft, Esteban Moro, Alex `Sandy' Pentland

This is an important problem because a common technique to improve speed and robustness of learning in deep reinforcement learning and many other machine learning algorithms is to run multiple learning agents in parallel.

reinforcement-learning

Improved Learning in Evolution Strategies via Sparser Inter-Agent Network Topologies

no code implementations30 Nov 2017 Dhaval Adjodah, Dan Calacci, Yan Leng, Peter Krafft, Esteban Moro, Alex Pentland

We draw upon a previously largely untapped literature on human collective intelligence as a source of inspiration for improving deep learning.

reinforcement-learning

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