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Found 25 papers, 4 papers with code
Clue-Instruct: Text-Based Clue Generation for Educational Crossword Puzzles
By gathering from Wikipedia pages informative content associated with relevant keywords, we use Large Language Models to automatically generate pedagogical clues related to the given input keyword and its context.
Multitask Kernel-based Learning with Logic Constraints
This paper presents a general framework to integrate prior knowledge in the form of logic constraints among a set of task functions into kernel machines.
Neural paraphrasing by automatically crawled and aligned sentence pairs
In this paper we present a method for the automatic generation of large aligned corpora, that is based on the assumption that news and blog websites talk about the same events using different narrative styles.
ArabIcros: AI-Powered Arabic Crossword Puzzle Generation for Educational Applications
This paper presents the first Arabic crossword puzzle generator driven by advanced AI technology.
Italian Crossword Generator: Enhancing Education through Interactive Word Puzzles
On the other hand, for generating crossword clues from a given text, Zero/Few-shot learning techniques were used to extract clues from the input text, adding variety and creativity to the puzzles.
Multitask Kernel-based Learning with First-Order Logic Constraints
In this paper we propose a general framework to integrate supervised and unsupervised examples with background knowledge expressed by a collection of first-order logic clauses into kernel machines.
SortNet: Learning To Rank By a Neural-Based Sorting Algorithm
Two main approaches exist in literature for the task of learning to rank: 1) a score function, learned by examples, which evaluates the properties of each object yielding an absolute relevance value that can be used to order the objects or 2) a pairwise approach, where a "preference function" is learned using pairs of objects to define which one has to be ranked first.
Logic Explained Networks
The language used to communicate the explanations must be formal enough to be implementable in a machine and friendly enough to be understandable by a wide audience.
Generate and Revise: Reinforcement Learning in Neural Poetry
Writers, poets, singers usually do not create their compositions in just one breath.
Vulgaris: Analysis of a Corpus for Middle-Age Varieties of Italian Language
Italian is a Romance language that has its roots in Vulgar Latin.
Focus of Attention Improves Information Transfer in Visual Features
In order to better structure the input probability distribution, we use a human-like focus of attention model that, coherently with the information maximization model, is also based on second-order differential equations.
Deep Constraint-based Propagation in Graph Neural Networks
The popularity of deep learning techniques renewed the interest in neural architectures able to process complex structures that can be represented using graphs, inspired by Graph Neural Networks (GNNs).
Local Propagation in Constraint-based Neural Network
In this paper we study a constraint-based representation of neural network architectures.
A Lagrangian Approach to Information Propagation in Graph Neural Networks
GNNs exploit a set of state variables, each assigned to a graph node, and a diffusion mechanism of the states among neighbor nodes, to implement an iterative procedure to compute the fixed point of the (learnable) state transition function.
Relational Neural Machines
Deep learning has been shown to achieve impressive results in several tasks where a large amount of training data is available.
Learning in Text Streams: Discovery and Disambiguation of Entity and Relation Instances
We consider a scenario where an artificial agent is reading a stream of text composed of a set of narrations, and it is informed about the identity of some of the individuals that are mentioned in the text portion that is currently being read.
Video Surveillance of Highway Traffic Events by Deep Learning Architectures
In this paper we describe a video surveillance system able to detect traffic events in videos acquired by fixed videocameras on highways.
Conditions for Unnecessary Logical Constraints in Kernel Machines
A main property of support vector machines consists in the fact that only a small portion of the training data is significant to determine the maximum margin separating hyperplane in the feature space, the so called support vectors.
Neural Poetry: Learning to Generate Poems using Syllables
Motivated by the recent progresses on machine learning-based models that learn artistic styles, in this paper we focus on the problem of poem generation.
T-Norms Driven Loss Functions for Machine Learning
Neural-symbolic approaches have recently gained popularity to inject prior knowledge into a learner without requiring it to induce this knowledge from data.
An Unsupervised Character-Aware Neural Approach to Word and Context Representation Learning
In the last few years, neural networks have been intensively used to develop meaningful distributed representations of words and contexts around them.
On the relation between Loss Functions and T-Norms
Deep learning has been shown to achieve impressive results in several domains like computer vision and natural language processing.
The principle of cognitive action - Preliminary experimental analysis
In this document we shows a first implementation and some preliminary results of a new theory, facing Machine Learning problems in the frameworks of Classical Mechanics and Variational Calculus.
Collapsing of dimensionality
We analyze a new approach to Machine Learning coming from a modification of classical regularization networks by casting the process in the time dimension, leading to a sort of collapse of dimensionality in the problem of learning the model parameters.
Learning to see like children: proof of concept
In the last few years we have seen a growing interest in machine learning approaches to computer vision and, especially, to semantic labeling.
