7 papers
AIA: A 16nm Multicore SoC for Approximate Inference Acceleration Exploiting Non-normalized Knuth-Yao Sampling and Inter-Core Register Sharing
Shirui Zhao, Nimish Shah, Wannes Meert +1
Probabilistic graphical models (PMs) are popular to empower machine learning with the ability of reasoning and decision-making. To perform approximate inference in PMs, sampling-ba…
AIA: A Customized Multi-core RISC-V SoC for Discrete Sampling Workloads in 16 nm
Shirui Zhao, Nimish Shah, Wannes Meert +1
Probabilistic models (PMs) are essential in advancing machine learning capabilities, particularly in safety-critical applications involving reasoning and decision-making. Among the…
Tailored Transformation Invariance for Industrial Anomaly Detection
Mariette Schönfeld, Wannes Meert, Hendrik Blockeel
Industrial Anomaly Detection (IAD) is a subproblem within Computer Vision Anomaly Detection that has been receiving increasing amounts of attention due to its applicability to real…
Warping and Matching Subsequences Between Time Series
Simiao Lin, Wannes Meert, Pieter Robberechts +1
Comparing time series is essential in various tasks such as clustering and classification. While elastic distance measures that allow warping provide a robust quantitative comparis…
Steering the LoCoMotif: Using Domain Knowledge in Time Series Motif Discovery
Aras Yurtman, Daan Van Wesenbeeck, Wannes Meert +1
Time Series Motif Discovery (TSMD) identifies repeating patterns in time series data, but its unsupervised nature might result in motifs that are not interesting to the user. To ad…
Quantitative Evaluation of Motif Sets in Time Series
Daan Van Wesenbeeck, Aras Yurtman, Wannes Meert +1
Time Series Motif Discovery (TSMD), which aims at finding recurring patterns in time series, is an important task in numerous application domains, and many methods for this task ex…