Search Results for author: Shirin Dora

Found 6 papers, 1 papers with code

Towards Improved Imbalance Robustness in Continual Multi-Label Learning with Dual Output Spiking Architecture (DOSA)

no code implementations7 Feb 2024 Sourav Mishra, Shirin Dora, Suresh Sundaram

A novel imbalance-aware loss function is also proposed, improving the multi-label classification performance of the model by making it more robust to data imbalance.

Multi-Label Classification Multi-Label Learning

Deep Predictive Coding with Bi-directional Propagation for Classification and Reconstruction

no code implementations29 May 2023 Senhui Qiu, Saugat Bhattacharyya, Damien Coyle, Shirin Dora

Each layer in the networks trained using DBPC learn to predict the activities of neurons in the previous and next layer which allows the network to simultaneously perform classification and reconstruction tasks using feedforward and feedback propagation, respectively.

Classification

Sharing Lifelong Reinforcement Learning Knowledge via Modulating Masks

1 code implementation18 May 2023 Saptarshi Nath, Christos Peridis, Eseoghene Ben-Iwhiwhu, Xinran Liu, Shirin Dora, Cong Liu, Soheil Kolouri, Andrea Soltoggio

The key idea is that the isolation of specific task knowledge to specific masks allows agents to transfer only specific knowledge on-demand, resulting in robust and effective distributed lifelong learning.

reinforcement-learning

MuPNet: Multi-modal Predictive Coding Network for Place Recognition by Unsupervised Learning of Joint Visuo-Tactile Latent Representations

no code implementations16 Sep 2019 Oliver Struckmeier, Kshitij Tiwari, Shirin Dora, Martin J. Pearson, Sander M. Bohte, Cyriel MA Pennartz, Ville Kyrki

Extracting and binding salient information from different sensory modalities to determine common features in the environment is a significant challenge in robotics.

A Deep Predictive Coding Network for Learning Latent Representations

no code implementations ICLR 2018 Shirin Dora, Cyriel Pennartz, Sander Bohte

In this paper, we describe an algorithm to build a deep generative model using predictive coding that can be used to infer latent representations about the stimuli received from external environment.

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