ceodspspectrum
I am a staff scientist in the Theoretical division at the Los Alamos National Laboratory in Los Alamos, NM.
Los Alamos National LabsLos Alamos
Pinned Repositories
benchmarking
Benchmarking of Matrix multiplications and Inversion in CARC Server
bionmf-gpu
NMF-mGPU web site:
CARC_WORK
Documentation
ceodspspectrum.github.io
pyCP_APR
CP-APR Tensor Decomposition with PyTorch backend. pyCP_APR can perform non-negative Poisson Tensor Factorization on GPU, and includes an interface for anomaly detection using the extracted latent patterns.
AdversarialTensors
Tensors-based framework for adversarial robustness
pyDNA_EPBD
pyDNA-EPBD: A Python-based Implementation of the Extended Peyrard-Bishop-Dauxois Model for DNA Breathing Dynamics Simulation
pyDNMFk
Python Distributed Non Negative Matrix Factorization with custom clustering
pyDNTNK
Python Distributed Non Negative Tensor Networks
pyDRESCALk
Distributed Non Negative RESCAL decomposition with estimation of latent features
ceodspspectrum's Repositories
ceodspspectrum/benchmarking
Benchmarking of Matrix multiplications and Inversion in CARC Server
ceodspspectrum/bionmf-gpu
NMF-mGPU web site:
ceodspspectrum/CARC_WORK
Documentation
ceodspspectrum/ceodspspectrum.github.io
ceodspspectrum/ConvLSTM-PyTorch
ConvLSTM/ConvGRU (Encoder-Decoder) with PyTorch on Moving-MNIST
ceodspspectrum/early-stopping-pytorch
Early stopping for PyTorch
ceodspspectrum/pyCP_APR
CP-APR Tensor Decomposition with PyTorch backend. pyCP_APR can perform non-negative Poisson Tensor Factorization on GPU, and includes an interface for anomaly detection using the extracted latent patterns.
ceodspspectrum/notes
my dev notes
ceodspspectrum/pyDRESCALk
Distributed Non Negative RESCAL decomposition with estimation of latent features
ceodspspectrum/QuickBytes
Development of short tutorials for UNM's Center for Advanced Research Computing
ceodspspectrum/ResidualAttentionNetwork-pytorch
a pytorch code about Residual Attention Network. This code is based on two projects from
ceodspspectrum/Search-Algorithms
DFS,BFS, UCS and A* Search
ceodspspectrum/T-ELF
Tensor Extraction of Latent Features (T-ELF). Within T-ELF's arsenal are non-negative matrix and tensor factorization solutions, equipped with automatic model determination (also known as the estimation of latent factors - rank) for accurate data modeling. Our software suite encompasses cutting-edge data pre-processing and post-processing modules.