1 citations · 1 across the 8 of their papers we have counts for
10 papers
Dynamic Neural Optimal Transport for Subset Alignment
Austin J. Brockmeier, Bilal Riaz
We study optimal transport between distributions when only a subset of the target population is relevant to the source. The goal is to map the source distribution to a reweighted t…
QSMP: finding representative time series subsequences through Quick Shift+Matrix Profile
Carlos H. Mendoza-Cardenas, Rogers F. Silva, Austin J. Brockmeier
Finding representative waveforms in long time series has scientific and practical value in many domains, as it enables summarization and visualization of large time series datasets…
Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS)
Zhi Li, Hau Phan, Matthew Emigh +1
Vision-language co-embedding networks, such as CLIP, provide a latent embedding space with semantic information that is useful for downstream tasks. We hypothesize that the embeddi…
Anomaly Detection via Autoencoder Composite Features and NCE
Yalin Liao, Austin J. Brockmeier
Unsupervised anomaly detection is a challenging task. Autoencoders (AEs) or generative models are often employed to model the data distribution of normal inputs and subsequently id…
Contrastive Learning to Fine-Tune Feature Extraction Models for the Visual Cortex
Alex Mulrooney, Austin J. Brockmeier
Predicting the neural response to natural images in the visual cortex requires extracting relevant features from the images and relating those feature to the observed responses. In…
DiME: Maximizing Mutual Information by a Difference of Matrix-Based Entropies
Oscar Skean, Jhoan Keider Hoyos Osorio, Austin J. Brockmeier +1
We introduce an information-theoretic quantity with similar properties to mutual information that can be estimated from data without making explicit assumptions on the underlying d…
The Representation Jensen-Rényi Divergence
Jhoan Keider Hoyos Osorio, Oscar Skean, Austin J. Brockmeier +1
We introduce a divergence measure between data distributions based on operators in reproducing kernel Hilbert spaces defined by kernels. The empirical estimator of the divergence i…
Exploring latent networks in resting-state fMRI using voxel-to-voxel causal modeling feature selection
Hassan Baker, Austin J. Brockmeier
Functional networks characterize the coordinated neural activity observed by functional neuroimaging. The prevalence of different networks during resting state periods provide usef…
Shift-invariant waveform learning on epileptic ECoG
Carlos H. Mendoza-Cardenas, Austin J. Brockmeier
Seizure detection algorithms must discriminate abnormal neuronal activity associated with a seizure from normal neural activity in a variety of conditions. Our approach is to seek…
Searching for waveforms on spatially-filtered epileptic ECoG
Carlos H. Mendoza-Cardenas, Austin J. Brockmeier
Seizures are one of the defining symptoms in patients with epilepsy, and due to their unannounced occurrence, they can pose a severe risk for the individual that suffers it. New re…