ecml-pkdd

Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activ…

Repository for the paper "Better Capturing Interactions between Products in Retail: Revisited Negative Sampling for Basket Choice Modeling".

Change detection for Burned area Delineation (ChaBuD) ECML/PKDD 2023 challenge

[ECML-PKDD 2024 Research Track] Official source code of "Adaptive Seasonal-Trend Decomposition for Streaming Time Series Data with Transitions and Fluctuations in Seasonality"

Resource for misinformation research on Twitter. Official resource of the paper "DeMis: Data-efficient Misinformation Detection using Reinforcement Learning", ECML-PKDD 2022

R2-AD2: Detecting Anomalies by Analysing the Raw Gradient

Official implementation of SpikF-GO: Spiking Fourier Graph Operators for Multivariate Time Series Forecasting, accepted at ECML PKDD 2026.

Adaptive Momentum Coefficient for Neural Network Optimization

ECML-PKDD 已接收（CCF B）：面向低资源长尾情感分析的 HiPro-LoRA PEFT 方法与严格评测包

Risk-based sequential stopping: when to invoke an LLM in streaming systems. Code for ECML PKDD 2026 (Research Track). arXiv:2607.13048 · Zenodo DOI 10.5281/zenodo.20783549