# SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States

arXiv: https://arxiv.org/abs/2306.04817 · HTML: https://mlsanity.com/paper/2306.04817/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 106 references that are in the map.
- First posted on arXiv 7 Jun 2023.

## Builds on (most cited first, 22 of 22)

- [Learnable latent embeddings for joint behavioral and neural analysis](https://mlsanity.com/paper/2204.00673/) (2022)
- [TensorLy: Tensor Learning in Python](https://mlsanity.com/paper/1610.09555/) (2016)
- [Spherical Latent Spaces for Stable Variational Autoencoders](https://mlsanity.com/paper/1808.10805/) (2018)
- [Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity](https://mlsanity.com/paper/2109.04463/) (2021)
- [Accurate Uncertainty Estimation and Decomposition in Ensemble Learning](https://mlsanity.com/paper/1911.04061/) (2019)
- [Recurrent switching linear dynamical systems](https://mlsanity.com/paper/1610.08466/) (2016)
- [Sparse Gaussian Process Variational Autoencoders](https://mlsanity.com/paper/2010.10177/) (2020)
- [Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers](https://mlsanity.com/paper/2206.06131/) (2022)
- [Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning](https://mlsanity.com/paper/2305.18962/) (2023)
- [Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics](https://mlsanity.com/paper/2206.02972/) (2022)
- [Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters](https://mlsanity.com/paper/1410.6031/) (2014)
- [Distributed Flexible Nonlinear Tensor Factorization](https://mlsanity.com/paper/1604.07928/) (2016)
- [Hierarchical Coupled Geometry Analysis for Neuronal Structure and Activity Pattern Discovery](https://mlsanity.com/paper/1511.02086/) (2015)
- [A guide to state-space modeling of ecological time series](https://mlsanity.com/paper/2002.02001/) (2020)
- [Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition](https://mlsanity.com/paper/2207.02446/) (2022)
- [Dynamic Filtering of Time-Varying Sparse Signals via l1 Minimization](https://mlsanity.com/paper/1507.06145/) (2015)
- [Data-Driven Tree Transforms and Metrics](https://mlsanity.com/paper/1708.05768/) (2017)
- [Neural Tensor Factorization](https://mlsanity.com/paper/1802.04416/) (2018)
- [Stochastic Greedy Algorithms For Multiple Measurement Vectors](https://mlsanity.com/paper/1711.01521/) (2017)
- [Co-manifold learning with missing data](https://mlsanity.com/paper/1810.06803/) (2018)
- [Open and free EEG datasets for epilepsy diagnosis](https://mlsanity.com/paper/2108.01030/) (2021)
- [Understanding and improving social factors in education: a computational social science approach](https://mlsanity.com/paper/2301.05619/) (2023)

## Built on by (most cited first, 5 of 5)

- [LINOCS: Lookahead Inference of Networked Operators for Continuous Stability](https://mlsanity.com/paper/2404.18267/) (2024)
- [BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics](https://mlsanity.com/paper/2505.20480/) (2025)
- [Multi-Integration of Labels across Categories for Component Identification (MILCCI)](https://mlsanity.com/paper/2602.04270/) (2026)
- [Behavior-dLDS: A decomposed linear dynamical systems model for neural activity partially constrained by behavior](https://mlsanity.com/paper/2603.05612/) (2026)
- [Neighbor Embedding for High-Dimensional Sparse Poisson Data](https://mlsanity.com/paper/2604.16932/) (2026)

## Abstract (arXiv)

Time series data across scientific domains are often collected under distinct states (e.g., tasks), wherein latent processes (e.g., biological factors) create complex inter- and intra-state variability. A key approach to capture this complexity is to uncover fundamental interpretable units within the data, Building Blocks (BBs), which modulate their activity and adjust their structure across observations. Existing methods for identifying BBs in multi-way data often overlook inter- vs. intra-state variability, produce uninterpretable components, or do not align with properties of real-world data, such as missing samples and sessions of different duration. Here, we present a framework for Similarity-driven Building Block Inference using Graphs across States (SiBBlInGS). SiBBlInGS offers a graph-based dictionary learning approach for discovering sparse BBs along with their temporal traces, based on co-activity patterns and inter- vs. intra-state relationships. Moreover, SiBBlInGS captures per-trial temporal variability and controlled cross-state structural BB adaptations, identifies state-specific vs. state-invariant components, and accommodates variability in the number and duration of observed sessions across states. We demonstrate SiBBlInGS's ability to reveal insights into complex phenomena as well as its robustness to noise and missing samples through several synthetic and real-world examples, including web search and neural data.