49 research outputs found
A survey on modern trainable activation functions
In neural networks literature, there is a strong interest in identifying and
defining activation functions which can improve neural network performance. In
recent years there has been a renovated interest of the scientific community in
investigating activation functions which can be trained during the learning
process, usually referred to as "trainable", "learnable" or "adaptable"
activation functions. They appear to lead to better network performance.
Diverse and heterogeneous models of trainable activation function have been
proposed in the literature. In this paper, we present a survey of these models.
Starting from a discussion on the use of the term "activation function" in
literature, we propose a taxonomy of trainable activation functions, highlight
common and distinctive proprieties of recent and past models, and discuss main
advantages and limitations of this type of approach. We show that many of the
proposed approaches are equivalent to adding neuron layers which use fixed
(non-trainable) activation functions and some simple local rule that
constraints the corresponding weight layers.Comment: Published in "Neural Networks" journal (Elsevier
Integration of Context Information through Probabilistic Ontological Knowledge into Image Classification
The use of ontological knowledge to improve classification results is a promising line of research. The availability of a probabilistic ontology raises the possibility of combining the probabilities coming from the ontology with the ones produced by a multi-class classifier that detects particular objects in an image. This combination not only provides the relations existing between the different segments, but can also improve the classification accuracy. In fact, it is known that the contextual information can often give information that suggests the correct class. This paper proposes a possible model that implements this integration, and the experimental assessment shows the effectiveness of the integration, especially when the classifier’s accuracy is relatively low. To assess the performance of the proposed model, we designed and implemented a simulated classifier that allows a priori decisions of its performance with sufficient precision
Toward the application of XAI methods in EEG-based systems
An interesting case of the well-known Dataset Shift Problem is the
classification of Electroencephalogram (EEG) signals in the context of
Brain-Computer Interface (BCI). The non-stationarity of EEG signals can lead to
poor generalisation performance in BCI classification systems used in different
sessions, also from the same subject. In this paper, we start from the
hypothesis that the Dataset Shift problem can be alleviated by exploiting
suitable eXplainable Artificial Intelligence (XAI) methods to locate and
transform the relevant characteristics of the input for the goal of
classification. In particular, we focus on an experimental analysis of
explanations produced by several XAI methods on an ML system trained on a
typical EEG dataset for emotion recognition. Results show that many relevant
components found by XAI methods are shared across the sessions and can be used
to build a system able to generalise better. However, relevant components of
the input signal also appear to be highly dependent on the input itself.Comment: Accepted to be presented at XAI.it 2022 - Italian Workshop on
Explainable Artificial Intelligenc
Middle-Level Features for the Explanation of Classification Systems by Sparse Dictionary Methods.
Machine learning (ML) systems are affected by a pervasive lack of transparency. The eXplainable Artificial Intelligence (XAI) research area addresses this problem and the related issue of explaining the behavior of ML systems in terms that are understandable to human beings. In many explanation of XAI approaches, the output of ML systems are explained in terms of low-level features of their inputs. However, these approaches leave a substantive explanatory burden with human users, insofar as the latter are required to map low-level properties into more salient and readily understandable parts of the input. To alleviate this cognitive burden, an alternative model-agnostic framework is proposed here. This framework is instantiated to address explanation problems in the context of ML image classification systems, without relying on pixel relevance maps and other low-level features of the input. More specifically, one obtains sets of middle-level properties of classification inputs that are perceptually salient by applying sparse dictionary learning techniques. These middle-level properties are used as building blocks for explanations of image classifications. The achieved explanations are parsimonious, for their reliance on a limited set of middle-level image properties. And they can be contrastive, because the set of middle-level image properties can be used to explain why the system advanced the proposed classification over other antagonist classifications. In view of its model-agnostic character, the proposed framework is adaptable to a variety of other ML systems and explanation problems
On The Effects Of Data Normalisation For Domain Adaptation On EEG Data
In the Machine Learning (ML) literature, a well-known problem is the Dataset
Shift problem where, differently from the ML standard hypothesis, the data in
the training and test sets can follow different probability distributions,
leading ML systems toward poor generalisation performances. This problem is
intensely felt in the Brain-Computer Interface (BCI) context, where bio-signals
as Electroencephalographic (EEG) are often used. In fact, EEG signals are
highly non-stationary both over time and between different subjects. To
overcome this problem, several proposed solutions are based on recent transfer
learning approaches such as Domain Adaption (DA). In several cases, however,
the actual causes of the improvements remain ambiguous. This paper focuses on
the impact of data normalisation, or standardisation strategies applied
together with DA methods. In particular, using \textit{SEED}, \textit{DEAP},
and \textit{BCI Competition IV 2a} EEG datasets, we experimentally evaluated
the impact of different normalization strategies applied with and without
several well-known DA methods, comparing the obtained performances. It results
that the choice of the normalisation strategy plays a key role on the
classifier performances in DA scenarios, and interestingly, in several cases,
the use of only an appropriate normalisation schema outperforms the DA
technique.Comment: Published in its final version on Engineering Applications of
Artificial Intelligence (EAAI) https://doi.org/10.1016/j.engappai.2023.10620
A study on recovering the cloud-top height for the EUSO mission
In this paper we present some preliminary results on an optical-flow based technique aimed at recovering the cloud-top height from infra-red image sequences. This work has been carried out in the context of the development of the "Extreme Universe Space Observatory" mission (EUSO), an ESA led international mission for the investigation of the nature and origin of Extreme Energy Cosmic Rays. The knowledge of the cloud scenario is critical to measure the primary energy and the composition of EECRs. In this work we explore the feasibility for the cloud-top height recovery, of a technique based on a robust multi-resolution optical-flow algorithm. The robustness is achieved adopting a Least Median of Squares paradigm. The algorithm has been tested on semi-synthetic data (i.e. real data that have been synthetically warped in order to have a reliable ground truth for the motion field), and on real short sequences (pairs of frames) coming from the ATSR2 data set. Since we assumed the same geometry as for the ATSR2 data, the cloud top height could be recovered from the motion field by means of the widely used Prata and Turner equation
Strategies to exploit XAI to improve classification systems
Explainable Artificial Intelligence (XAI) aims to provide insights into the
decision-making process of AI models, allowing users to understand their
results beyond their decisions. A significant goal of XAI is to improve the
performance of AI models by providing explanations for their decision-making
processes. However, most XAI literature focuses on how to explain an AI system,
while less attention has been given to how XAI methods can be exploited to
improve an AI system. In this work, a set of well-known XAI methods typically
used with Machine Learning (ML) classification tasks are investigated to verify
if they can be exploited, not just to provide explanations but also to improve
the performance of the model itself. To this aim, two strategies to use the
explanation to improve a classification system are reported and empirically
evaluated on three datasets: Fashion-MNIST, CIFAR10, and STL10. Results suggest
that explanations built by Integrated Gradients highlight input features that
can be effectively used to improve classification performance.Comment: This work has been accepted to be presented to The 1st World
Conference on eXplainable Artificial Intelligence (xAI 2023), July 26-28,
2023 - Lisboa, Portuga
Psoriasis and psoriasiform reactions secondary to immune checkpoint inhibitors
The advent of Immune Checkpoint Inhibitors (ICIs) as a standard of care for several cancers, including melanoma and head/neck squamous cell carcinoma has changed the therapeutic approach to these conditions, drawing at the same time the attention on some safety issues related to their use. To assess the incidence of psoriasis as a specific immune-related cutaneous adverse event attributing to ICIs using the Eudravigilance reporting system. All reports of adverse drug reactions (ADRs) concerning either exacerbation of psoriasis or de novo onset of psoriasis/psoriasiform reactions associated to the use of Cytotoxic T-Lymphocyte Antigen-4 (CTLA-4) inhibitors ipilimumab and tremelimumab, and the Programmed cell Death protein 1/Programmed Death-Ligand 1 (PD-1/PD-L1) inhibitors nivolumab, pembrolizumab, atezolizumab, durvalumab, avelumab, and cemiplimab were identified and extracted from the Eudravigilance reporting system, during the period between the date of market licensing (for each study drug) and 30 October 2020. 8213 reports of cutaneous ADRs associated with at least one of study drug have been recorded, of which 315 (3.8%) reporting psoriasis and/or psoriasiform reactions as ADR. In 70.8% of reports patients had pre-existing disease. ICIs-related skin toxicity is a well-established phenomenon, presenting with several conditions, sustained by an immune background based on the activity of some cells (CD4+/CD8+ T-cells, neutrophils, eosinophils, and plasmocytes), inflammatory mediators, chemokines, and tumor-specific antibodies. In this setting, psoriasis represents probably the most paradigmatic model of these reactions, thus requiring adequate recognition as no guidelines on management are now available