2.3k citations
- University of California San DiegoUS25 papers
- Center for Theoretical Biological PhysicsUS6 papers
- Tel Aviv UniversityIL6 papers
- Stanford UniversityUS5 papers
- Harvard UniversityUS4 papers
- Institut national de recherche en sciences et technologies du numériqueFR4 papers
- Northeastern UniversityUS4 papers
- Baylor College of MedicineUS3 papers
- Columbia UniversityUS3 papers
- Georgia Institute of TechnologyUS3 papers
- Imperial College LondonGB3 papers
- Lawrence Berkeley National LaboratoryUS3 papers
6 papers · 1 filter
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach
Alexander Lin, Yingzhuo Zhang, Jeremy Heng +4
We propose a general statistical framework for clustering multiple time series that exhibit nonlinear dynamics into an a-priori-unknown number of sub-groups. Our motivation comes f…
Spatial Stochastic Modeling with MCell and CellBlender
Sanjana Gupta, Jacob Czech, Robert Kuczewski +4
This chapter provides a brief introduction to the theory and practice of spatial stochastic simulations. It begins with an overview of different methods available for biochemical s…
FanStore: Enabling Efficient and Scalable I/O for Distributed Deep Learning
Zhao Zhang, Lei Huang, Uri Manor +5
Emerging Deep Learning (DL) applications introduce heavy I/O workloads on computer clusters. The inherent long lasting, repeated, and random file access pattern can easily saturate…
Learning Moment Closure in Reaction-Diffusion Systems with Spatial Dynamic Boltzmann Distributions
Oliver K. Ernst, Tom Bartol, Terrence Sejnowski +1
Many physical systems are described by probability distributions that evolve in both time and space. Modeling these systems is often challenging to due large state space and analyt…
Robust Landmark Detection for Alignment of Mouse Brain Section Images
Yuncong Chen, David Kleinfeld, Martyn Goulding +1
Brightfield and fluorescent imaging of whole brain sections are funda- mental tools of research in mouse brain study. As sectioning and imaging become more efficient, there is an i…
Learning Dynamic Boltzmann Distributions as Reduced Models of Spatial Chemical Kinetics
Oliver K. Ernst, Thomas Bartol, Terrence Sejnowski +1
Finding reduced models of spatially-distributed chemical reaction networks requires an estimation of which effective dynamics are relevant. We propose a machine learning approach t…