Search Results for author: David C. Noelle

Found 7 papers, 0 papers with code

End-to-End Auditory Object Recognition via Inception Nucleus

no code implementations25 May 2020 Mohammad K. Ebrahimpour, Timothy Shea, Andreea Danielescu, David C. Noelle, Christopher T. Kello

Machine learning approaches to auditory object recognition are traditionally based on engineered features such as those derived from the spectrum or cepstrum.

Classification General Classification +1

Ventral-Dorsal Neural Networks: Object Detection via Selective Attention

no code implementations15 May 2020 Mohammad K. Ebrahimpour, Jiayun Li, Yen-Yun Yu, Jackson L. Reese, Azadeh Moghtaderi, Ming-Hsuan Yang, David C. Noelle

The coarse functional distinction between these streams is between object recognition -- the "what" of the signal -- and extracting location related information -- the "where" of the signal.

Image Classification object-detection +2

WW-Nets: Dual Neural Networks for Object Detection

no code implementations15 May 2020 Mohammad K. Ebrahimpour, J. Ben Falandays, Samuel Spevack, Ming-Hsuan Yang, David C. Noelle

Inspired by this structure, we have proposed an object detection framework involving the integration of a "What Network" and a "Where Network".

object-detection Object Detection

Learning sparse representations in reinforcement learning

no code implementations4 Sep 2019 Jacob Rafati, David C. Noelle

This has motivated methods that learn internal representations of the agent's state, effectively reducing the size of the state space and restructuring state representations in order to support generalization.

Acrobot reinforcement-learning +1


no code implementations ICLR 2019 Mohammad K. Ebrahimpour, David C. Noelle

We demonstrate that a simple linear mapping can be learned from sensitivity maps to bounding box coordinates, localizing the recognized object.

General Classification Image Classification +2

Learning Representations in Model-Free Hierarchical Reinforcement Learning

no code implementations23 Oct 2018 Jacob Rafati, David C. Noelle

When combined with an intrinsic motivation learning mechanism, this method learns both subgoals and skills, based on experiences in the environment.

Hierarchical Reinforcement Learning Montezuma's Revenge +2

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