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Found 17 papers, 3 papers with code
Benchmarking GPT-4 on Algorithmic Problems: A Systematic Evaluation of Prompting Strategies
Large Language Models (LLMs) have revolutionized the field of Natural Language Processing thanks to their ability to reuse knowledge acquired on massive text corpora on a wide variety of downstream tasks, with minimal (if any) tuning steps.
A Neural Rewriting System to Solve Algorithmic Problems
Modern neural network architectures still struggle to learn algorithmic procedures that require to systematically apply compositional rules to solve out-of-distribution problem instances.
Large-scale Generative AI Models Lack Visual Number Sense
Humans can readily judge the number of objects in a visual scene, even without counting, and such a skill has been documented in a variety of animal species and in babies prior to language development and formal schooling.
A Hybrid System for Systematic Generalization in Simple Arithmetic Problems
Solving symbolic reasoning problems that require compositionality and systematicity is considered one of the key ingredients of human intelligence.
Investigating the generative dynamics of energy-based neural networks
Generative neural networks can produce data samples according to the statistical properties of their training distribution.
Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models
Creating learning models that can exhibit sophisticated reasoning skills is one of the greatest challenges in deep learning research, and mathematics is rapidly becoming one of the target domains for assessing scientific progress in this direction.
Learning to solve arithmetic problems with a virtual abacus
Acquiring mathematical skills is considered a key challenge for modern Artificial Intelligence systems.
Automated Detection of Dolphin Whistles with Convolutional Networks and Transfer Learning
Effective conservation of maritime environments and wildlife management of endangered species require the implementation of efficient, accurate and scalable solutions for environmental monitoring.
A developmental approach for training deep belief networks
Deep belief networks (DBNs) are stochastic neural networks that can extract rich internal representations of the environment from the sensory data.
Transformers discover an elementary calculation system exploiting local attention and grid-like problem representation
Mathematical reasoning is one of the most impressive achievements of human intellect but remains a formidable challenge for artificial intelligence systems.
Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control
The proposed framework relies on the possibility for the UAVs to exchange some information through a communication channel, in order to achieve context-awareness and implicitly coordinate the swarm's actions.
Machine Learning-aided Design of Thinned Antenna Arrays for Optimized Network Level Performance
With the advent of millimeter wave (mmWave) communications, the combination of a detailed 5G network simulator with an accurate antenna radiation model is required to analyze the realistic performance of complex cellular scenarios.
Emergence of Network Motifs in Deep Neural Networks
Network science can offer fundamental insights into the structural and functional properties of complex systems.
Enabling Simulation-Based Optimization Through Machine Learning: A Case Study on Antenna Design
Therefore, it is possible to perform a global numerical optimization over the vast multi-dimensional parameter space, in a fraction of the time that would be required by a simple brute-force search.
On the difficulty of learning and predicting the long-term dynamics of bouncing objects
In recent years, a variety of approaches have been proposed for learning to predict the physical dynamics of objects interacting in a visual scene.
Perception of visual numerosity in humans and machines
Numerosity perception is foundational to mathematical learning, but its computational bases are strongly debated.
Deep learning systems as complex networks
Thanks to the availability of large scale digital datasets and massive amounts of computational power, deep learning algorithms can learn representations of data by exploiting multiple levels of abstraction.
