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Linderman, Scott W.

  1. Simplified State Space Layers for Sequence Modeling
    2022/08/09 by Jimmy T. H. Smith, Andrew Warrington, Smith, Jimmy T. H. +3 · 103 citations
    Computer Science · #Age of Information Optimization #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel Computing and Optimization Techniques
  2. Generalized Shape Metrics on Neural Representations
    2021/10/27 by Alex H. Williams, Williams, Alex H., Erin M. Kunz +5 · 14 citations
    Biochemistry, Genetics and Molecular Biology · Mathematics · #Cell Image Analysis Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Morphological variations and asymmetry
  3. Variational Sequential Monte Carlo
    2017/05/31 by Christian A. Naesseth, Naesseth, Christian A., Scott W. Linderman +5 · 8 citations
    Computer Science · Mathematics · #Gaussian Processes and Bayesian Inference #Bayesian Methods and Mixture Models #Markov Chains and Monte Carlo Methods
  4. Discovering Latent Network Structure in Point Process Data
    2014/02/04 by Scott W. Linderman, Linderman, Scott W., Ryan P. Adams +1 · 4 citations
    Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Morphological variations and asymmetry #Point processes and geometric inequalities
  5. Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems
    2021/11/01 by Jimmy T. H. Smith, Scott W. Linderman, Smith, Jimmy T. H. +3 · 5 citations
    Neuroscience · Computer Science · Physics and Astronomy · #Neural dynamics and brain function #Neural Networks and Applications #Model Reduction and Neural Networks
  6. Dependent Multinomial Models Made Easy: Stick Breaking with the Pólya-Gamma Augmentation
    2015/06/18 by Linderman, Scott W., Johnson, Matthew J., Adams, Ryan P. · 3 citations
    #FOS: Computer and information sciences #Machine Learning (stat.ML)
  7. Towards Scalable and Stable Parallelization of Nonlinear RNNs
    2024/07/26 by Xavier González, Gonzalez, Xavier, Andrew Warrington +5 · 10 citations
    Computer Science · #Quantum-Dot Cellular Automata #Neural Networks and Applications
  8. Recurrent switching linear dynamical systems
    2016/10/26 by Scott W. Linderman, Andrew C. Miller, Linderman, Scott W. +9 · 3 citations
    Computer Science · Engineering · #Control Systems and Identification #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Time Series Analysis and Forecasting
  9. Tree-Structured Recurrent Switching Linear Dynamical Systems for\n Multi-Scale Modeling
    2018/11/29 by Josue Nassar, Scott W. Linderman, Nassar, Josue +5 · 3 citations
    Biochemistry, Genetics and Molecular Biology · Computer Science · Decision Sciences · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Gene Regulatory Network Analysis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Modeling and Simulation Systems #Neural Networks and Applications #Simulation Techniques and Applications
  10. A framework for studying synaptic plasticity with neural spike train\n data
    2014/11/14 by Scott W. Linderman, Christopher H. Stock, Linderman, Scott W. +3 · 2 citations
    Neuroscience · Engineering · #Neural dynamics and brain function #Advanced Memory and Neural Computing #Neuroscience and Neuropharmacology Research
  11. Scalable Bayesian Inference for Excitatory Point Process Networks
    2015/07/12 by Linderman, Scott W., Adams, Ryan P. · 1 citation
    #FOS: Computer and information sciences #Machine Learning (stat.ML)
  12. Bayesian latent structure discovery from multi-neuron recordings
    2016/10/26 by Linderman, Scott W., Adams, Ryan P., Pillow, Jonathan W. · 1 citation
    #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (stat.ML) #Neurons and Cognition (q-bio.NC)
  13. Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
    2016/10/18 by Naesseth, Christian A., Ruiz, Francisco J. R., Linderman, Scott W. +1 · 1 citation
    #FOS: Computer and information sciences #Machine Learning (stat.ML) #Methodology (stat.ME)
  14. Convolutional State Space Models for Long-Range Spatiotemporal Modeling
    2023/10/30 by Smith, Jimmy T. H., De Mello, Shalini, Kautz, Jan +2 · 2 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG)
  15. Point process models for sequence detection in high-dimensional neural spike trains
    2020/10/10 by Williams, Alex H., Degleris, Anthony, Wang, Yixin +1 · 1 citation
    #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neurons and Cognition (q-bio.NC)
  16. Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models
    2022/01/13 by Zhaoran Wang, Wang, Yixin, Anthony Degleris +5 · 1 citation
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Statistical Methods and Bayesian Inference #Target Tracking and Data Fusion in Sensor Networks
  17. Brain-to-Text Benchmark '24: Lessons Learned
    2024/12/23 by Francis R. Willett, Jingyuan Li, Willett, Francis R. +29 · 2 citations
    Neuroscience · Psychology · #Neuroscience, Education and Cognitive Function #Educational and Psychological Assessments
  18. Predictability Enables Parallelization of Nonlinear State Space Models
    2025/08/22 by Gonzalez, Xavier, Kozachkov, Leo, Zoltowski, David M. +2 · 6 citations
    #37N40 #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #G.1.6 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering
  19. Revisiting Structured Variational Autoencoders
    2023/05/25 by Yixiu Zhao, Zhao, Yixiu, Scott W. Linderman +1 · 1 citation
    Computer Science · #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #Domain Adaptation and Few-Shot Learning
  20. Parallelizing MCMC Across the Sequence Length
    2025/08/25 by David M. Zoltowski, Zoltowski, David M., Skyler Wu +7 · 4 voices · 1 citation
    #stat.CO