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Found 39 papers, 10 papers with code
The KANDY Benchmark: Incremental Neuro-Symbolic Learning and Reasoning with Kandinsky Patterns
Artificial intelligence is continuously seeking novel challenges and benchmarks to effectively measure performance and to advance the state-of-the-art.
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources
Supervised models for Word Sense Disambiguation (WSD) currently yield to state-of-the-art results in the most popular benchmarks.
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.
On the Resurgence of Recurrent Models for Long Sequences -- Survey and Research Opportunities in the Transformer Era
A longstanding challenge for the Machine Learning community is the one of developing models that are capable of processing and learning from very long sequences of data.
Neural Time-Reversed Generalized Riccati Equation
This paper introduces a novel neural-based approach to optimal control, with the aim of working forward-in-time.
Collectionless Artificial Intelligence
By and large, the professional handling of huge data collections is regarded as a fundamental ingredient of the progress of machine learning and of its spectacular results in related disciplines, with a growing agreement on risks connected to the centralization of such data collections.
Continual Learning with Pretrained Backbones by Tuning in the Input Space
The intrinsic difficulty in adapting deep learning models to non-stationary environments limits the applicability of neural networks to real-world tasks.
PARTIME: Scalable and Parallel Processing Over Time with Deep Neural Networks
nature of the streamed data with samples that are smoothly evolving over time for efficient gradient computations.
Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off
Deploying AI-powered systems requires trustworthy models supporting effective human interactions, going beyond raw prediction accuracy.
Learning to Identify Drilling Defects in Turbine Blades with Single Stage Detectors
The application is challenging due to the large image resolutions in which defects are very small and hardly captured by the commonly used anchor sizes, and also due to the small size of the available dataset.
Deep Learning to See: Towards New Foundations of Computer Vision
The remarkable progress in computer vision over the last few years is, by and large, attributed to deep learning, fueled by the availability of huge sets of labeled data, and paired with the explosive growth of the GPU paradigm.
Stochastic Coherence Over Attention Trajectory For Continuous Learning In Video Streams
Our experiments leverage 3D virtual environments and they show that the proposed agents can learn to distinguish objects just by observing the video stream.
Can machines learn to see without visual databases?
This paper sustains the position that the time has come for thinking of learning machines that conquer visual skills in a truly human-like context, where a few human-like object supervisions are given by vocal interactions and pointing aids only.
Messing Up 3D Virtual Environments: Transferable Adversarial 3D Objects
In the last few years, the scientific community showed a remarkable and increasing interest towards 3D Virtual Environments, training and testing Machine Learning-based models in realistic virtual worlds.
Evaluating Continual Learning Algorithms by Generating 3D Virtual Environments
However, in order to devise continual learning algorithms that operate in more realistic conditions, it is fundamental to gain access to rich, fully customizable and controlled experimental playgrounds.
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.
Friendly Training: Neural Networks Can Adapt Data To Make Learning Easier
In the last decade, motivated by the success of Deep Learning, the scientific community proposed several approaches to make the learning procedure of Neural Networks more effective.
Entropy-based Logic Explanations of Neural Networks
Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains.
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Generate and Revise: Reinforcement Learning in Neural Poetry
Writers, poets, singers usually do not create their compositions in just one breath.
Gravitational Models Explain Shifts on Human Visual Attention
Another where the information from these maps is merged in order to select a single location to be attended for further and more complex computations and reasoning.
Developing Constrained Neural Units Over Time
In this paper we present a foundational study on a constrained method that defines learning problems with Neural Networks in the context of the principle of least cognitive action, which very much resembles the principle of least action in mechanics.
SAILenv: Learning in Virtual Visual Environments Made Simple
Recently, researchers in Machine Learning algorithms, Computer Vision scientists, engineers and others, showed a growing interest in 3D simulators as a mean to artificially create experimental settings that are very close to those in the real world.
Wave Propagation of Visual Stimuli in Focus of Attention
Fast reactions to changes in the surrounding visual environment require efficient attention mechanisms to reallocate computational resources to most relevant locations in the visual field.
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.
Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers
Adversarial attacks on machine learning-based classifiers, along with defense mechanisms, have been widely studied in the context of single-label classification problems.
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.
Toward Improving the Evaluation of Visual Attention Models: a Crowdsourcing Approach
A computational modeling of this phenomenon must take into account where people look in order to evaluate which are the salient locations (spatial distribution of the fixations), when they look in those locations to understand the temporal development of the exploration (temporal order of the fixations), and how they move from one location to another with respect to the dynamics of the scene and the mechanics of the eyes (dynamics).
Asynchronous Distributed Learning from Constraints
In this paper, the extension of the framework of Learning from Constraints (LfC) to a distributed setting where multiple parties, connected over the network, contribute to the learning process is studied.
Jointly Learning to Detect Emotions and Predict Facebook Reactions
In this paper we focus on Facebook posts paired with reactions of multiple users, and we investigate their relationships with classes of emotions that are typically considered in the task of emotion detection.
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.
Learning Visual Features Under Motion Invariance
Humans are continuously exposed to a stream of visual data with a natural temporal structure.
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.
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.
Cognitive Action Laws: The Case of Visual Features
A special choice of the functional index, which leads to forth-order differential equations---Cognitive Action Laws (CAL)---exhibits a structure that mirrors classic formulation of machine learning.
Motion Invariance in Visual Environments
The puzzle of computer vision might find new challenging solutions when we realize that most successful methods are working at image level, which is remarkably more difficult than processing directly visual streams, just as happens in nature.
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.
