Search Results for author:
Found 16 papers, 7 papers with code
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.
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.
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.
Graph Neural Networks for Graph Drawing
Graph Drawing techniques have been developed in the last few years with the purpose of producing aesthetically pleasing node-link layouts.
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.
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.
Vulgaris: Analysis of a Corpus for Middle-Age Varieties of Italian Language
Italian is a Romance language that has its roots in Vulgar Latin.
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.
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).
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.
Local Propagation in Constraint-based Neural Network
In this paper we study a constraint-based representation of neural network architectures.
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.
