863 citations · 1.1k across the 19 of their papers we have counts for
26 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…
LoCoMotif: Discovering time-warped motifs in time series
Daan Van Wesenbeeck, Aras Yurtman, Wannes Meert +1
Time Series Motif Discovery (TSMD) refers to the task of identifying patterns that occur multiple times (possibly with minor variations) in a time series. All existing methods for…
Pruning-Based Extraction of Descriptions from Probabilistic Circuits
Sieben Bocklandt, Vincent Derkinderen, Koen Vanderstraeten +3
Concept learning is a general task with applications in various domains. As a motivating example we consider the application of music playlist generation, where a playlist is repre…
DPU-v2: Energy-efficient execution of irregular directed acyclic graphs
Nimish Shah, Wannes Meert, Marian Verhelst
A growing number of applications like probabilistic machine learning, sparse linear algebra, robotic navigation, etc., exhibit irregular data flow computation that can be modeled w…
Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming
Kshitij Goyal, Wannes Meert, Hendrik Blockeel +4
Internet based businesses and products (e.g. e-commerce, music streaming) are becoming more and more sophisticated every day with a lot of focus on improving customer satisfaction.…
Elastic Product Quantization for Time Series
Pieter Robberechts, Wannes Meert, Jesse Davis
Analyzing numerous or long time series is difficult in practice due to the high storage costs and computational requirements. Therefore, techniques have been proposed to generate c…
Machine Learning with a Reject Option: A survey
Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas +2
Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can…
GRAPHOPT: constrained-optimization-based parallelization of irregular graphs
Nimish Shah, Wannes Meert, Marian Verhelst
Sparse, irregular graphs show up in various applications like linear algebra, machine learning, engineering simulations, robotic control, etc. These graphs have a high degree of pa…
Acceleration of probabilistic reasoning through custom processor architecture
Nimish Shah, Laura I. Galindez Olascoaga, Wannes Meert +1
Probabilistic reasoning is an essential tool for robust decision-making systems because of its ability to explicitly handle real-world uncertainty, constraints and causal relations…
ProbLP: A framework for low-precision probabilistic inference
Nimish Shah, Laura I. Galindez Olascoaga, Wannes Meert +1
Bayesian reasoning is a powerful mechanism for probabilistic inference in smart edge-devices. During such inferences, a low-precision arithmetic representation can enable improved…
Versatile Verification of Tree Ensembles
Laurens Devos, Wannes Meert, Jesse Davis
Machine learned models often must abide by certain requirements (e.g., fairness or legal). This has spurred interested in developing approaches that can provably verify whether a m…
A general anomaly detection framework for fleet-based condition monitoring of machines
Kilian Hendrickx, Wannes Meert, Yves Mollet +4
Machine failures decrease up-time and can lead to extra repair costs or even to human casualties and environmental pollution. Recent condition monitoring techniques use artificial…
Verifying Tree Ensembles by Reasoning about Potential Instances
Laurens Devos, Wannes Meert, Jesse Davis
Imagine being able to ask questions to a black box model such as "Which adversarial examples exist?", "Does a specific attribute have a disproportionate effect on the model's predi…
An Automated Engineering Assistant: Learning Parsers for Technical Drawings
Dries Van Daele, Nicholas Decleyre, Herman Dubois +1
From a set of technical drawings and expert knowledge, we automatically learn a parser to interpret such a drawing. This enables automatic reasoning and learning on top of a large…
Crowdsourced wireless spectrum anomaly detection
Sreeraj Rajendran, Vincent Lenders, Wannes Meert +1
Automated wireless spectrum monitoring across frequency, time and space will be essential for many future applications. Manual and fine-grained spectrum analysis is becoming imposs…
Learning Relational Representations with Auto-encoding Logic Programs
Sebastijan Dumancic, Tias Guns, Wannes Meert +1
Deep learning methods capable of handling relational data have proliferated over the last years. In contrast to traditional relational learning methods that leverage first-order lo…
SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable Features
Sreeraj Rajendran, Wannes Meert, Vincent Lenders +1
Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide r…
COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series
Toon Van Craenendonck, Wannes Meert, Sebastijan Dumancic +1
Clustering is ubiquitous in data analysis, including analysis of time series. It is inherently subjective: different users may prefer different clusterings for a particular dataset…
Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors
Sreeraj Rajendran, Wannes Meert, Domenico Giustiniano +2
This paper looks into the technology classification problem for a distributed wireless spectrum sensing network. First, a new data-driven model for Automatic Modulation Classificat…