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Found 19 papers, 9 papers with code
Tools and Benchmarks for Automated Log Parsing
Logs are imperative in the development and maintenance process of many software systems.
Software Engineering
Logzip: Extracting Hidden Structures via Iterative Clustering for Log Compression
Data compression is essential to reduce the cost of log storage.
Databases Software Engineering
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.
BARS-CTR: Open Benchmarking for Click-Through Rate Prediction
We have publicly released the benchmarking code, evaluation protocols, and hyper-parameter settings of our work to promote reproducible research in this field.
Ensemble Knowledge Distillation for CTR Prediction
Recently, deep learning-based models have been widely studied for click-through rate (CTR) prediction and lead to improved prediction accuracy in many industrial applications.
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.
Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection
To better understand the characteristics of different anomaly detectors, in this paper, we provide a comprehensive review and evaluation of five popular neural networks used by six state-of-the-art methods.
Graph-based Incident Aggregation for Large-Scale Online Service Systems
The proposed framework is evaluated with real-world incident data collected from a large-scale online service system of Huawei Cloud.
BARS: Towards Open Benchmarking for Recommender Systems
Despite significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field.
Geometry of Score Based Generative Models
We demonstrate that looking from geometric perspective enables us to answer many of these questions and provide new interpretations to some known results.
Log-based Anomaly Detection based on EVT Theory with feedback
System logs play a critical role in maintaining the reliability of software systems.
Performance Issue Identification in Cloud Systems with Relational-Temporal Anomaly Detection
Performance issues permeate large-scale cloud service systems, which can lead to huge revenue losses.
Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Services
As modern software systems continue to grow in terms of complexity and volume, anomaly detection on multivariate monitoring metrics, which profile systems' health status, becomes more and more critical and challenging.
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.
MTAD: Tools and Benchmarks for Multivariate Time Series Anomaly Detection
These methods are evaluated with five multivariate KPI datasets that are publicly available.
FaultProfIT: Hierarchical Fault Profiling of Incident Tickets in Large-scale Cloud Systems
To date, FaultProfIT has analyzed 10, 000+ incidents from 30+ cloud services, successfully revealing several fault trends that have informed system improvements.
Knowledge-aware Alert Aggregation in Large-scale Cloud Systems: a Hybrid Approach
We also share our experience in deploying COLA in our real-world cloud system, Cloud X.
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
In addition to the algorithmic perspective, we overview various real-world system designs to investigate the implementation costs associated with different PEFT algorithms.
Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers
Solving image and video jigsaw puzzles poses the challenging task of rearranging image fragments or video frames from unordered sequences to restore meaningful images and video sequences.
