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Found 11 papers, 2 papers with code
SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks
The fast growth of computational power and scales of modern super-computing systems have raised great challenges for the management of exascale scientific data.
ByteTransformer: A High-Performance Transformer Boosted for Variable-Length Inputs
In this paper, we present ByteTransformer, a high-performance transformer boosted for variable-length inputs.
Multi-granularity Association Learning Framework for on-the-fly Fine-Grained Sketch-based Image Retrieval
We consider that there is a significant correlation among these incomplete sketches in the sketch drawing episode of each photo.
A Unified Granular-ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set
The granular-ball rough set can simultaneously represent Pawlak rough sets, and the neighborhood rough set, so as to realize the unified representation of the two.
An Efficient and Accurate Rough Set for Feature Selection, Classification and Knowledge Representation
This paper present a strong data mining method based on rough set, which can realize feature selection, classification and knowledge representation at the same time.
Exploring Autoencoder-based Error-bounded Compression for Scientific Data
(1) We provide an in-depth investigation of the characteristics of various autoencoder models and develop an error-bounded autoencoder-based framework in terms of the SZ model.
Ball k-means
The assigned cluster of the points in the stable area is not changed in the current iteration while the points in the annulus area will be adjusted within a few neighbor clusters in the current iteration.
FT-CNN: Algorithm-Based Fault Tolerance for Convolutional Neural Networks
(1) We propose several systematic ABFT schemes based on checksum techniques and analyze their fault protection ability and runtime thoroughly. Unlike traditional ABFT based on matrix-matrix multiplication, our schemes support any convolution implementations.
Normalization of Input-output Shared Embeddings in Text Generation Models
Neural Network based models have been state-of-the-art models for various Natural Language Processing tasks, however, the input and output dimension problem in the networks has still not been fully resolved, especially in text generation tasks (e. g. Machine Translation, Text Summarization), in which input and output both have huge sizes of vocabularies.
Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization
One serious challenge is that the data prediction has to be performed based on the preceding decompressed values during the compression in order to guarantee the error bounds, which may degrade the prediction accuracy in turn.
Information Theory Information Theory
Z-checker: A Framework for Assessing Lossy Compression of Scientific Data
However, lossy compressor developers and users are missing a tool to explore the features of scientific datasets and understand the data alteration after compression in a systematic and reliable way.
Other Computer Science Instrumentation and Methods for Astrophysics Computational Engineering, Finance, and Science
