Author Info
Name
Affiliation
Papers
KRISTOFER E BOUCHARD
Depts. of Neurological Surg. & Physiol., Univ. of California, San Francisco, San Francisco, CA, USA|c|
25
Collaborators
Citations 
PageRank 
80
18
8.99
Referers 
Referees 
References 
83
463
133
Search Limit
100463
Title
Citations
PageRank
Year
Critical Point-Finding Methods Reveal Gradient-Flat Regions Of Deep Network Losses00.342021
Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models.00.342020
State-Based Network Similarity Visualization40.432020
Scaling of Union of Intersections for Inference of Granger Causal Networks from Observational Data00.342020
Laminar Origin Of Evoked Ecog High-Gamma Activity00.342019
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis.00.342019
Numerically Recovering the Critical Points of a Deep Linear Autoencoder.00.342019
HDMF: Hierarchical Data Modeling Framework for Modern Science Data Standards00.342019
Sparse, Predictive, and Interpretable Functional Connectomics with UoILasso.00.342019
Hangul Fonts Dataset: a Hierarchical and Compositional Dataset for Interrogating Learned Representations.00.342019
Optimizing the Union of Intersections LASSO (UoILASSO) and Vector Autoregressive (UoIVAR) Algorithms for Improved Statistical Estimation at Scale.00.342018
Deep learning as a tool for neural data analysis: speech classification and cross-frequency coupling in human sensorimotor cortex.00.342018
Run Procrustes, Run! On the convergence of accelerated Procrustes Flow.00.342018
Spiking Linear Dynamical Systems on Neuromorphic Hardware for Low-Power Brain-Machine Interfaces.00.342018
International Neuroscience Initiatives through the Lens of High-Performance Computing.00.342018
UoI-NMF Cluster: A Robust Nonnegative Matrix Factorization Algorithm for Improved Parts-Based Decomposition and Reconstruction of Noisy Data.00.342017
Sparse Coding Of Ecog Signals Identifies Interpretable Components For Speech Control In Human Sensorimotor Cortex00.342017
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction.10.342017
Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions.30.402017
Methods for Specifying Scientific Data Standards and Modeling Relationships with Applications to Neuroscience.00.342016
Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data.10.352016
Usage Pattern-Driven Dynamic Data Layout Reorganization30.382016
Role of the site of synaptic competition and the balance of learning forces for Hebbian encoding of probabilistic Markov sequences.10.442015
An adapting auditory-motor feedback loop can contribute to generating vocal repetition00.342015
Neural decoding of spoken vowels from human sensory-motor cortex with high-density electrocorticography.50.572014