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Found 12 papers, 0 papers with code
Unsupervised Learning of Graph from Recipes
We iteratively learn the graph structure and the parameters of a $\mathsf{GNN}$ encoding the texts (text-to-graph) one sequence at a time while providing the supervision by decoding the graph into text (graph-to-text) and comparing the generated text to the input.
PizzaCommonSense: Learning to Model Commonsense Reasoning about Intermediate Steps in Cooking Recipes
Decoding the core of procedural texts, exemplified by cooking recipes, is crucial for intelligent reasoning and instruction automation.
A Graphical Formalism for Commonsense Reasoning with Recipes
Whilst cooking is a very important human activity, there has been little consideration given to how we can formalize recipes for use in a reasoning framework.
Repurposing of Resources: from Everyday Problem Solving through to Crisis Management
Repurposing arises in everyday situations such as finding substitutes for missing ingredients when cooking, or for unavailable tools when doing DIY.
Reservoir-Based Distributed Machine Learning for Edge Operation
We introduce a novel design for in-situ training of machine learning algorithms built into smart sensors, and illustrate distributed training scenarios using radio frequency (RF) spectrum sensors.
Reservoir Based Edge Training on RF Data To Deliver Intelligent and Efficient IoT Spectrum Sensors
We propose a solution via Deep Delay Loop Reservoir Computing (DLR), a processing architecture that supports general machine learning algorithms on compact mobile devices by leveraging delay-loop reservoir computing in combination with innovative electrooptical hardware.
On the Effects of Knowledge-Augmented Data in Word Embeddings
This paper investigates techniques for knowledge injection into word embeddings learned from large corpora of unannotated data.
AutoEncoders for Training Compact Deep Learning RF Classifiers for Wireless Protocols
We show that compact fully connected (FC) deep learning networks trained to classify wireless protocols using a hierarchy of multiple denoising autoencoders (AEs) outperform reference FC networks trained in a typical way, i. e., with a stochastic gradient based optimization of a given FC architecture.
Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-training
Adversarial examples in machine learning for images are widely publicized and explored.
Adversarial Examples in RF Deep Learning: Detection of the Attack and its Physical Robustness
It is not clear if the RF AdExs maintain their effects in the physical world, i. e., when AdExs are delivered over-the-air (OTA).
Foundations for a Probabilistic Event Calculus
We also describe an ASP implementation of PEC and show the sense in which this is sound and complete.
Non-Monotonic Reasoning and Story Comprehension
This paper develops a Reasoning about Actions and Change framework integrated with Default Reasoning, suitable as a Knowledge Representation and Reasoning framework for Story Comprehension.
