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Found 21 papers, 8 papers with code
A Machine Learning Approach Towards SKILL Code Autocompletion
In this study, a novel, data-efficient methodology for generating SKILL code is proposed and experimentally validated.
LoCoMotif: Discovering time-warped motifs in time series
All existing methods for TSMD have one or more of the following limitations: they only look for the two most similar occurrences of a pattern; they only look for patterns of a pre-specified, fixed length; they cannot handle variability along the time axis; and they only handle univariate time series.
Deriving Comprehensible Theories from Probabilistic Circuits
The field of Explainable AI (XAI) is seeking to shed light on the inner workings of complex AI models and uncover the rationale behind their decisions.
Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Review
This inherent noise is one of the main challenges for defect inspection.
AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly Detection
Moreover, by identifying patterns and conditions in (low-dimensional) subspaces, the anomaly detector can provide simple explanations of why something is considered an anomaly.
Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming
In this work, we represent product concepts using database queries and tackle two learning problems.
Adversarial Example Detection in Deployed Tree Ensembles
We take an alternative approach and attempt to detect adversarial examples in a post-deployment setting.
Elastic Product Quantization for Time Series
Analyzing numerous or long time series is difficult in practice due to the high storage costs and computational requirements.
Machine Learning with a Reject Option: A survey
Machine learning models always make a prediction, even when it is likely to be inaccurate.
ProbLP: A framework for low-precision probabilistic inference
Bayesian reasoning is a powerful mechanism for probabilistic inference in smart edge-devices.
Versatile Verification of Tree Ensembles
This paper introduces a generic algorithm called Veritas that enables tackling multiple different verification tasks for tree ensemble models like random forests (RFs) and gradient boosting decision trees (GBDTs).
Verifying Tree Ensembles by Reasoning about Potential Instances
Imagine being able to ask questions to a black box model such as "Which adversarial examples exist?
A general anomaly detection framework for fleet-based condition monitoring of machines
In most of these applications, it is safe to assume healthy conditions for the majority of machines.
Towards Hardware-Aware Tractable Learning of Probabilistic Models
We showcase our framework on a mobile activity recognition scenario, and on a variety of benchmark datasets representative of the field of tractable learning and of the applications of interest.
An Automated Engineering Assistant: Learning Parsers for Technical Drawings
From a set of technical drawings and expert knowledge, we automatically learn a parser to interpret such a drawing.
Learning Relational Representations with Auto-encoding Logic Programs
This framework, inspired by the auto-encoding principle, uses first-order logic as a data representation language, and the mapping between the original and latent representation is done by means of logic programs instead of neural networks.
SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable Features
Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use.
COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series
This paper studies semi-supervised clustering in the context of time series.
Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors
We show that a LSTM based model can learn good representations of variable length time domain sequences, which is useful in classifying modulation signals with different symbol rates.
Networking and Internet Architecture
Theory reconstruction: a representation learning view on predicate invention
With this positional paper we present a representation learning view on predicate invention.
Skolemization for Weighted First-Order Model Counting
First-order model counting emerged recently as a novel reasoning task, at the core of efficient algorithms for probabilistic logics.
