Search Results for author: Aleksandar Zlateski

Found 11 papers, 3 papers with code

LoopTune: Optimizing Tensor Computations with Reinforcement Learning

no code implementations4 Sep 2023 Dejan Grubisic, Bram Wasti, Chris Cummins, John Mellor-Crummey, Aleksandar Zlateski

Advanced compiler technology is crucial for enabling machine learning applications to run on novel hardware, but traditional compilers fail to deliver performance, popular auto-tuners have long search times and expert-optimized libraries introduce unsustainable costs.

reinforcement-learning

LoopStack: a Lightweight Tensor Algebra Compiler Stack

1 code implementation2 May 2022 Bram Wasti, José Pablo Cambronero, Benoit Steiner, Hugh Leather, Aleksandar Zlateski

We present LoopStack, a domain specific compiler stack for tensor operations, composed of a frontend, LoopTool, and an efficient optimizing code generator, LoopNest.

BIG-bench Machine Learning

Large-scale image segmentation based on distributed clustering algorithms

no code implementations21 Jun 2021 Ran Lu, Aleksandar Zlateski, H. Sebastian Seung

Many approaches to 3D image segmentation are based on hierarchical clustering of supervoxels into image regions.

Chunking Clustering +2

L3 Fusion: Fast Transformed Convolutions on CPUs

no code implementations4 Dec 2019 Rati Gelashvili, Nir Shavit, Aleksandar Zlateski

Fast convolutions via transforms, either Winograd or FFT, had emerged as a preferred way of performing the computation of convolutional layers, as it greatly reduces the number of required operations.

Benchmarking

PZnet: Efficient 3D ConvNet Inference on Manycore CPUs

no code implementations18 Mar 2019 Sergiy Popovych, Davit Buniatyan, Aleksandar Zlateski, Kai Li, H. Sebastian Seung

Convolutional nets have been shown to achieve state-of-the-art accuracy in many biomedical image analysis tasks.

ZNNi - Maximizing the Inference Throughput of 3D Convolutional Networks on Multi-Core CPUs and GPUs

no code implementations17 Jun 2016 Aleksandar Zlateski, Kisuk Lee, H. Sebastian Seung

Other things being equal, processing a larger image tends to increase throughput, because fractionally less computation is wasted on the borders of the image.

Image Segmentation object-detection +2

Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction

no code implementations NeurIPS 2015 Kisuk Lee, Aleksandar Zlateski, Vishwanathan Ashwin, H. Sebastian Seung

Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics.

3D Architecture Boundary Detection +1

ZNN - A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-Core and Many-Core Shared Memory Machines

2 code implementations22 Oct 2015 Aleksandar Zlateski, Kisuk Lee, H. Sebastian Seung

Applying Brent's theorem to the task dependency graph implies that linear speedup with the number of processors is attainable within the PRAM model of parallel computation, for wide network architectures.

Benchmarking

Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection

2 code implementations NeurIPS 2015 Kisuk Lee, Aleksandar Zlateski, Ashwin Vishwanathan, H. Sebastian Seung

Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics.

3D Architecture Boundary Detection

Image Segmentation by Size-Dependent Single Linkage Clustering of a Watershed Basin Graph

no code implementations1 May 2015 Aleksandar Zlateski, H. Sebastian Seung

We present a method for hierarchical image segmentation that defines a disaffinity graph on the image, over-segments it into watershed basins, defines a new graph on the basins, and then merges basins with a modified, size-dependent version of single linkage clustering.

Clustering Image Segmentation +1

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