1 code implementation • 13 Sep 2021 • Mengye Ren, Tyler R. Scott, Michael L. Iuzzolino, Michael C. Mozer, Richard Zemel
Real world learning scenarios involve a nonstationary distribution of classes with sequential dependencies among the samples, in contrast to the standard machine learning formulation of drawing samples independently from a fixed, typically uniform distribution.
1 code implementation • NeurIPS 2021 • 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.
1 code implementation • ICLR 2021 • Mengye Ren, Michael L. Iuzzolino, Michael C. Mozer, Richard S. Zemel
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.
no code implementations • 2 Apr 2020 • Michael L. Iuzzolino, Tetsumichi Umada, Nisar R. Ahmed, Danielle A. Szafir
A current standard policy for AL is to query the oracle (e. g., the analyst) to refine labels for datapoints where the classifier has the highest uncertainty.
1 code implementation • CVPR 2020 • Hamid Reza Vaezi Joze, Amirreza Shaban, Michael L. Iuzzolino, Kazuhito Koishida
In late fusion, each modality is processed in a separate unimodal Convolutional Neural Network (CNN) stream and the scores of each modality are fused at the end.
Ranked #3 on Hand Gesture Recognition on NVGesture
no code implementations • 17 Jan 2019 • Michael L. Iuzzolino, Michael E. Walker, Daniel Szafir
Although this approach has achieved state-of-the-art results, the deep learning paradigm may be limited due to a reliance on large amounts of annotated training data.