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Found 11 papers, 7 papers with code
Manifold Contrastive Learning with Variational Lie Group Operators
In this work, we propose a contrastive learning approach that directly models the latent manifold using Lie group operators parameterized by coefficients with a sparsity-promoting prior.
PrefGen: Preference Guided Image Generation with Relative Attributes
Using information from a sequence of query responses, we can estimate user preferences over a set of image attributes and perform preference-guided image editing and generation.
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics
Learning interpretable representations of neural dynamics at a population level is a crucial first step to understanding how observed neural activity relates to perception and behavior.
Variational Sparse Coding with Learned Thresholding
Sparse coding strategies have been lauded for their parsimonious representations of data that leverage low dimensional structure.
Late fusion of machine learning models using passively captured interpersonal social interactions and motion from smartphones predicts decompensation in heart failure
Objective: Worldwide, heart failure (HF) is a major cause of morbidity and mortality and one of the leading causes of hospitalization.
Variational Autoencoder with Learned Latent Structure
This enables us to learn the nonlinear manifold structure from the data and use that structure to define a prior in the latent space.
Parallel Unbalanced Optimal Transport Regularization for Large Scale Imaging Problems
The modeling of phenomenological structure is a crucial aspect in inverse imaging problems.
Hierarchical Optimal Transport for Multimodal Distribution Alignment
Our results demonstrate that when clustered structure exists in datasets, and is consistent across trials or time points, a hierarchical alignment strategy that leverages such structure can provide significant improvements in cross-domain alignment.
Active embedding search via noisy paired comparisons
Suppose that we wish to estimate a user's preference vector $w$ from paired comparisons of the form "does user $w$ prefer item $p$ or item $q$?," where both the user and items are embedded in a low-dimensional Euclidean space with distances that reflect user and item similarities.
Efficient Tracking of Sparse Signals via an Earth Mover's Distance Dynamics Regularizer
Tracking algorithms such as the Kalman filter aim to improve inference performance by leveraging the temporal dynamics in streaming observations.
Short Term Memory Capacity in Networks via the Restricted Isometry Property
Cortical networks are hypothesized to rely on transient network activity to support short term memory (STM).
