Search Results for author:
Found 19 papers, 7 papers with code
FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs
R is one of the most popular programming languages for statistics and machine learning, but the R framework is relatively slow and unable to scale to large datasets.
Distributed, Parallel, and Cluster Computing
Semi-External Memory Sparse Matrix Multiplication for Billion-Node Graphs
In contrast, we scale sparse matrix multiplication beyond memory capacity by implementing sparse matrix dense matrix multiplication (SpMM) in a semi-external memory (SEM) fashion; i. e., we keep the sparse matrix on commodity SSDs and dense matrices in memory.
Distributed, Parallel, and Cluster Computing
Active Community Detection in Massive Graphs
A canonical problem in graph mining is the detection of dense communities.
Social and Information Networks Physics and Society
Sparse Projection Oblique Randomer Forests
Unfortunately, these extensions forfeit one or more of the favorable properties of decision forests based on axis-aligned splits, such as robustness to many noise dimensions, interpretability, or computational efficiency.
Supervised Dimensionality Reduction for Big Data
To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences.
knor: A NUMA-Optimized In-Memory, Distributed and Semi-External-Memory k-means Library
The \textit{k-means NUMA Optimized Routine} (\textsf{knor}) library has (i) in-memory (\textsf{knori}), (ii) distributed memory (\textsf{knord}), and (iii) semi-external memory (\textsf{knors}) modules that radically improve the performance of k-means for varying memory and hardware budgets.
Distributed, Parallel, and Cluster Computing
Edge-Parallel Graph Encoder Embedding
New algorithms for embedding graphs have reduced the asymptotic complexity of finding low-dimensional representations.
VESICLE: Volumetric Evaluation of Synaptic Interfaces using Computer vision at Large Scale
An open challenge problem at the forefront of modern neuroscience is to obtain a comprehensive mapping of the neural pathways that underlie human brain function; an enhanced understanding of the wiring diagram of the brain promises to lead to new breakthroughs in diagnosing and treating neurological disorders.
An Automated Images-to-Graphs Framework for High Resolution Connectomics
Finally, we deploy a reference end-to-end version of the pipeline on a large, publicly available data set.
Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes using High-Resolution Neural EM Data
Accurately estimating the wiring diagram of a brain, known as a connectome, at an ultrastructure level is an open research problem.
Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes
The contribution of this work is the introduction of a straightforward and robust pipeline which annotates axoplasmic reticula with high precision, contributing towards advancements in automatic feature annotations in neural EM data.
A Community-Developed Open-Source Computational Ecosystem for Big Neuro Data
Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies.
Neurons and Cognition Quantitative Methods
Geodesic Learning via Unsupervised Decision Forests
Empirical results on simulated and real data demonstrate that URerF is robust to high-dimensional noise, where as other methods, such as Isomap, UMAP, and FLANN, quickly deteriorate in such settings.
Graphyti: A Semi-External Memory Graph Library for FlashGraph
Emerging frameworks avoid the network bottleneck of distributed data with Semi-External Memory (SEM) that uses a single multicore node and operates on graphs larger than memory.
Distributed, Parallel, and Cluster Computing Databases
PACSET (Packed Serialized Trees): Reducing Inference Latency for Tree Ensemble Deployment
We present methods to serialize and deserialize tree ensembles that optimize inference latency when models are not already loaded into memory.
NeuroStorm: Accelerating Brain Science Discovery in the Cloud
Neuroscientists are now able to acquire data at staggering rates across spatiotemporal scales.
Prospective Learning: Principled Extrapolation to the Future
We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning.
Understanding Patterns of Deep Learning ModelEvolution in Network Architecture Search
We describe how evolutionary patterns appear in distributed settings and opportunities for caching and improved scheduling.
Masked Matrix Multiplication for Emergent Sparsity
Artificial intelligence workloads, especially transformer models, exhibit emergent sparsity in which computations perform selective sparse access to dense data.
