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Found 22 papers, 16 papers with code
Revisiting Structured Variational Autoencoders
Here, we revisit SVAEs using modern machine learning tools and demonstrate their advantages over more general alternatives in terms of both accuracy and efficiency.
Simplified State Space Layers for Sequence Modeling
Models using structured state space sequence (S4) layers have achieved state-of-the-art performance on long-range sequence modeling tasks.
Ranked #3 on
Long-range modeling
on LRA
Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models
This construction is similar to Bayesian nonparametric mixture models like the Dirichlet process mixture model (DPMM) in that the number of latent events (i. e. clusters) is a random variable, but the point process formulation makes the NSP especially well suited to modeling spatiotemporal data.
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems
The results are a trained SLDS variant that closely approximates the RNN, an auxiliary function that can produce a fixed point for each point in state-space, and a trained nonlinear RNN whose dynamics have been regularized such that its first-order terms perform the computation, if possible.
Generalized Shape Metrics on Neural Representations
In doing so, we identify relationships between neural representations that are interpretable in terms of anatomical features and model performance.
Statistical Neuroscience in the Single Trial Limit
Individual neurons often produce highly variable responses over nominally identical trials, reflecting a mixture of intrinsic "noise" and systematic changes in the animal's cognitive and behavioral state.
Fast deep learning correspondence for neuron tracking and identification in C.elegans using synthetic training
The same pre-trained model both tracks neurons across time and identifies corresponding neurons across individuals.
Point process models for sequence detection in high-dimensional neural spike trains
Sparse sequences of neural spikes are posited to underlie aspects of working memory, motor production, and learning.
Unifying and generalizing models of neural dynamics during decision-making
An open question in systems and computational neuroscience is how neural circuits accumulate evidence towards a decision.
Poisson-Randomized Gamma Dynamical Systems
This paper presents the Poisson-randomized gamma dynamical system (PRGDS), a model for sequentially observed count tensors that encodes a strong inductive bias toward sparsity and burstiness.
Dose-response modeling in high-throughput cancer drug screenings: An end-to-end approach
Personalized cancer treatments based on the molecular profile of a patient's tumor are an emerging and exciting class of treatments in oncology.
Applications
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling
Many real-world systems studied are governed by complex, nonlinear dynamics.
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
Many matching, tracking, sorting, and ranking problems require probabilistic reasoning about possible permutations, a set that grows factorially with dimension.
Variational Sequential Monte Carlo
The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior.
Recurrent switching linear dynamical systems
Many natural systems, such as neurons firing in the brain or basketball teams traversing a court, give rise to time series data with complex, nonlinear dynamics.
Bayesian latent structure discovery from multi-neuron recordings
Neural circuits contain heterogeneous groups of neurons that differ in type, location, connectivity, and basic response properties.
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
Variational inference using the reparameterization trick has enabled large-scale approximate Bayesian inference in complex probabilistic models, leveraging stochastic optimization to sidestep intractable expectations.
Scalable Bayesian Inference for Excitatory Point Process Networks
We build on previous work that has taken a Bayesian approach to this problem, specifying prior distributions over the latent network structure and a likelihood of observed activity given this network.
Dependent Multinomial Models Made Easy: Stick Breaking with the Pólya-Gamma Augmentation
Many practical modeling problems involve discrete data that are best represented as draws from multinomial or categorical distributions.
A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation
Rodent hippocampal population codes represent important spatial information about the environment during navigation.
A framework for studying synaptic plasticity with neural spike train data
Learning and memory in the brain are implemented by complex, time-varying changes in neural circuitry.
Discovering Latent Network Structure in Point Process Data
Networks play a central role in modern data analysis, enabling us to reason about systems by studying the relationships between their parts.
