no code implementations • 5 Oct 2023 • Ninon Lizé Masclef, T. Anderson Keller
In prior work, this idea has been operationalized in the form of probabilistic models of music which allow for precise computation of song (or note-by-note) probabilities, conditioned on a 'training set' of prior musical or cultural experiences.
1 code implementation • NeurIPS 2023 • Yue Song, T. Anderson Keller, Nicu Sebe, Max Welling
A prominent goal of representation learning research is to achieve representations which are factorized in a useful manner with respect to the ground truth factors of variation.
1 code implementation • 3 Sep 2023 • T. Anderson Keller, Lyle Muller, Terrence Sejnowski, Max Welling
Traveling waves of neural activity have been observed throughout the brain at a diversity of regions and scales; however, their precise computational role is still debated.
1 code implementation • 28 Jun 2023 • Xavier Suau, Federico Danieli, T. Anderson Keller, Arno Blaas, Chen Huang, Jason Ramapuram, Dan Busbridge, Luca Zappella
We propose 2D strUctured and EquivarianT representations (coined DUET), which are 2d representations organized in a matrix structure, and equivariant with respect to transformations acting on the input data.
1 code implementation • 25 Apr 2023 • Yue Song, T. Anderson Keller, Nicu Sebe, Max Welling
In this work, we instead propose to model latent structures with a learned dynamic potential landscape, thereby performing latent traversals as the flow of samples down the landscape's gradient.
no code implementations • 15 Nov 2022 • T. Anderson Keller, Xavier Suau, Luca Zappella
In this work, we observe that many existing self-supervised learning algorithms can be both unified and generalized when seen through the lens of equivariant representations.
1 code implementation • NeurIPS Workshop SVRHM 2021 • T. Anderson Keller, Qinghe Gao, Max Welling
Category-selectivity in the brain describes the observation that certain spatially localized areas of the cerebral cortex tend to respond robustly and selectively to stimuli from specific limited categories.
1 code implementation • NeurIPS 2021 • T. Anderson Keller, Max Welling
Finally, we demonstrate approximate equivariance to complex transformations, expanding upon the capabilities of existing group equivariant neural networks.
1 code implementation • 29 Jun 2021 • Fiorella Wever, T. Anderson Keller, Laura Symul, Victor Garcia
High levels of missing data and strong class imbalance are ubiquitous challenges that are often presented simultaneously in real-world time series data.
Ranked #1 on Time Series Classification on PhysioNet Challenge 2012 (AUPRC metric)
1 code implementation • 14 Nov 2020 • T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling
Efficient gradient computation of the Jacobian determinant term is a core problem in many machine learning settings, and especially so in the normalizing flow framework.
no code implementations • 18 Apr 2018 • T. Anderson Keller, Sharath Nittur Sridhar, Xin Wang
Associative memory using fast weights is a short-term memory mechanism that substantially improves the memory capacity and time scale of recurrent neural networks (RNNs).