no code implementations • 23 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.
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.
no code implementations • 4 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.
no code implementations • 18 Nov 2019 • Jacob Rafati, David C. Noelle
Efficient exploration for automatic subgoal discovery is a challenging problem in Hierarchical Reinforcement Learning (HRL).
Efficient Exploration Hierarchical Reinforcement Learning +2
no code implementations • 15 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".
no code implementations • 15 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.
no code implementations • 25 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.