Set prediction in the latent space
K Preechakul, C Piansaddhayanon… - Advances in …, 2021 - proceedings.neurips.cc
Set prediction tasks require the matching between predicted set and ground truth set in
order to propagate the gradient signal. Recent works have performed this matching in the …
order to propagate the gradient signal. Recent works have performed this matching in the …
Towards pointsets representation learning via self-supervised learning and set augmentation
P Arsomngern, C Long… - … on Pattern Analysis …, 2021 - ieeexplore.ieee.org
Deep metric learning is a supervised learning paradigm to construct a meaningful vector
space to represent complex objects. A successful application of deep metric learning to …
space to represent complex objects. A successful application of deep metric learning to …
Machine Learning Methods in Applied Chemical Research
A Buin - 2021 - atrium.lib.uoguelph.ca
Recent advancements in machine learning have led to widespread application of its
algorithms to synthetic planning and reaction predictions in the eld of chemistry. One major …
algorithms to synthetic planning and reaction predictions in the eld of chemistry. One major …
Representation Learning with Multisets
V Portilheiro - arXiv preprint arXiv:1911.08577, 2019 - arxiv.org
We study the problem of learning permutation invariant representations that can capture"
flexible" notions of containment. We formalize this problem via a measure theoretic definition …
flexible" notions of containment. We formalize this problem via a measure theoretic definition …