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09/15/2023
Constraint-Free Structure Learning with Smooth Acyclic Orientations
The structure learning problem consists of fitting data generated by a D...
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08/17/2023
Modeling Edge Features with Deep Bayesian Graph Networks
We propose an extension of the Contextual Graph Markov Model, a deep and...
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07/27/2023
Graph-based Polyphonic Multitrack Music Generation
Graphs can be leveraged to model polyphonic multitrack symbolic music, w...
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07/12/2023
Deep learning for dynamic graphs: models and benchmarks
Recent progress in research on Deep Graph Networks (DGNs) has led to a m...
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06/19/2023
Partial Hypernetworks for Continual Learning
Hypernetworks mitigate forgetting in continual learning (CL) by generati...
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05/18/2023
Neural Algorithmic Reasoning for Combinatorial Optimisation
Solving NP-hard/complete combinatorial problems with neural networks is ...
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03/28/2023
Projected Latent Distillation for Data-Agnostic Consolidation in Distributed Continual Learning
Distributed learning on the edge often comprises self-centered devices (...
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02/09/2023
Dual Algorithmic Reasoning
Neural Algorithmic Reasoning is an emerging area of machine learning whi...
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01/26/2023
Class-Incremental Learning with Repetition
Real-world data streams naturally include the repetition of previous con...
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01/23/2023
ECGAN: Self-supervised generative adversarial network for electrocardiography
High-quality synthetic data can support the development of effective pre...
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11/22/2022
Causal Abstraction with Soft Interventions
Causal abstraction provides a theory describing how several causal model...
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10/18/2022
Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
Deep Graph Networks (DGNs) currently dominate the research landscape of ...
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10/05/2022
ChemAlgebra: Algebraic Reasoning on Chemical Reactions
While showing impressive performance on various kinds of learning tasks,...
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09/12/2022
Modular Representations for Weak Disentanglement
The recently introduced weakly disentangled representations proposed to ...
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08/06/2022
Graph Pooling with Maximum-Weight k-Independent Sets
Graph reductions are fundamental when dealing with large scale networks ...
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07/04/2022
It's all About Consistency: A Study on Memory Composition for Replay-Based Methods in Continual Learning
Continual Learning methods strive to mitigate Catastrophic Forgetting (C...
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07/01/2022
Studying the impact of magnitude pruning on contrastive learning methods
We study the impact of different pruning techniques on the representatio...
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06/29/2022
Continual Learning for Human State Monitoring
Continual Learning (CL) on time series data represents a promising but u...
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06/23/2022
Sample Condensation in Online Continual Learning
Online Continual learning is a challenging learning scenario where the m...
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06/14/2022
Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models
Predictive machine learning models nowadays are often updated in a state...
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05/25/2022
Federated Adaptation of Reservoirs via Intrinsic Plasticity
We propose a novel algorithm for performing federated learning with Echo...
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05/20/2022
Leveraging Relational Information for Learning Weakly Disentangled Representations
Disentanglement is a difficult property to enforce in neural representat...
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05/19/2022
Continual Pre-Training Mitigates Forgetting in Language and Vision
Pre-trained models are nowadays a fundamental component of machine learn...
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05/18/2022
Deep Features for CBIR with Scarce Data using Hebbian Learning
Features extracted from Deep Neural Networks (DNNs) have proven to be ve...
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04/11/2022
Learning heuristics for A*
Path finding in graphs is one of the most studied classes of problems in...
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03/19/2022
Practical Recommendations for Replay-based Continual Learning Methods
Continual Learning requires the model to learn from a stream of dynamic,...
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02/03/2022
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving
This paper presents a proof-of-concept implementation of the AI-as-a-Ser...
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12/13/2021
Ex-Model: Continual Learning from a Stream of Trained Models
Learning continually from non-stationary data streams is a challenging r...
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12/06/2021
Is Class-Incremental Enough for Continual Learning?
The ability of a model to learn continually can be empirically assessed ...
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11/03/2021
Predictive Auto-scaling with OpenStack Monasca
Cloud auto-scaling mechanisms are typically based on reactive automation...
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10/04/2021
Inductive learning for product assortment graph completion
Global retailers have assortments that contain hundreds of thousands of ...
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07/18/2021
GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation
The problem of labeled graph generation is gaining attention in the Deep...
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07/14/2021
TEACHING – Trustworthy autonomous cyber-physical applications through human-centred intelligence
This paper discusses the perspective of the H2020 TEACHING project on th...
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07/08/2021
Calliope – A Polyphonic Music Transformer
The polyphonic nature of music makes the application of deep learning to...
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05/17/2021
Continual Learning with Echo State Networks
Continual Learning (CL) refers to a learning setup where data is non sta...
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05/14/2021
A causal learning framework for the analysis and interpretation of COVID-19 clinical data
We present a workflow for clinical data analysis that relies on Bayesian...
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05/13/2021
Addressing Fairness, Bias and Class Imbalance in Machine Learning: the FBI-loss
Resilience to class imbalance and confounding biases, together with the ...
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04/16/2021
MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks
Explainable AI (XAI) is a research area whose objective is to increase t...
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03/29/2021
Distilled Replay: Overcoming Forgetting through Synthetic Samples
Replay strategies are Continual Learning techniques which mitigate catas...
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03/22/2021
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification
In this work, we study the phenomenon of catastrophic forgetting in the ...
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03/12/2021
Continual Learning for Recurrent Neural Networks: a Review and Empirical Evaluation
Learning continuously during all model lifetime is fundamental to deploy...
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12/05/2020
Graph Mixture Density Networks
We introduce the Graph Mixture Density Network, a new family of machine ...
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11/09/2020
Explaining Deep Graph Networks with Molecular Counterfactuals
We present a novel approach to tackle explainability of deep graph netwo...
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11/05/2020
Short-Term Memory Optimization in Recurrent Neural Networks by Autoencoder-based Initialization
Training RNNs to learn long-term dependencies is difficult due to vanish...
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11/02/2020
Learning from Non-Binary Constituency Trees via Tensor Decomposition
Processing sentence constituency trees in binarised form is a common and...
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10/26/2020
Generative Tomography Reconstruction
We propose an end-to-end differentiable architecture for tomography reco...
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10/18/2020
FADER: Fast Adversarial Example Rejection
Deep neural networks are vulnerable to adversarial examples, i.e., caref...
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10/03/2020
Perplexity-free Parametric t-SNE
The t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm is a u...
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08/31/2020
ROS-Neuro Integration of Deep Convolutional Autoencoders for EEG Signal Compression in Real-time BCIs
Typical EEG-based BCI applications require the computation of complex fu...
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07/14/2020
