17 citations · 44 across the 14 of their papers we have counts for
5 papers · 1 filter
Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss
Michael L. Iuzzolino, Michael C. Mozer, Samy Bengio
Although deep feedforward neural networks share some characteristics with the primate visual system, a key distinction is their dynamics. Deep nets typically operate in serial stag…
Wandering Within a World: Online Contextualized Few-Shot Learning
Mengye Ren, Michael L. Iuzzolino, Michael C. Mozer +1
We aim to bridge the gap between typical human and machine-learning environments by extending the standard framework of few-shot learning to an online, continual setting. In this s…
In Automation We Trust: Investigating the Role of Uncertainty in Active Learning Systems
Michael L. Iuzzolino, Tetsumichi Umada, Nisar R. Ahmed +1
We investigate how different active learning (AL) query policies coupled with classification uncertainty visualizations affect analyst trust in automated classification systems. A…
Convolutional Bipartite Attractor Networks
Michael Iuzzolino, Yoram Singer, Michael C. Mozer
In human perception and cognition, a fundamental operation that brains perform is interpretation: constructing coherent neural states from noisy, incomplete, and intrinsically ambi…
Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails
Michael L. Iuzzolino, Michael E. Walker, Daniel Szafir
Robots hold promise in many scenarios involving outdoor use, such as search-and-rescue, wildlife management, and collecting data to improve environment, climate, and weather foreca…