Publications by profiles

Latest News – Editorial Line – Volunteers – Events – Specialties – Books– Contact – About


Research Profile

R0:8477de576deaf0ef41a00ad9e17c7171-Partial Differential Equations is All You Need for Generating Neural Architectures -- A Theory for Physical Artificial Intelligence Systems

Partial Differential Equations is All You Need for Generating Neural Architectures — A Theory for Physical Artificial Intelligence Systems

In this work, we generalize the reaction-diffusion equation in statistical physics, Schrödinger equation in quantum mechanics, Helmholtz equation in paraxial optics into the neural partial differential equations (NPDE), which can be considered as the fundamental equations in the field of artificial intelligence research

Federated Quantum Machine Learning

Federated Quantum Machine Learning

We present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model.

Classification based on Topological Data Analysis

Classification based on Topological Data Analysis

Topological Data Analysis (TDA) is an emergent field that aims to discover topological information hidden in a dataset. TDA tools have been commonly used to create filters and topological descriptors to improve Machine Learning (ML) methods. This paper proposes an algorithm that applies TDA directly to multi-class classification problems, even imbalanced datasets, without any further ML stage

Hugging Face datasets

Hugging Face datasets

One-line dataloaders for many public datasets & Efficient data pre-processing

S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training

S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training

We introduce S++, a simple, robust, and deployable framework for training a neural network (NN) using private data from multiple sources, using secret-shared secure function evaluation. In short, consider a virtual third party to whom every data-holder sends their inputs, and which computes the neural network: in our case, this virtual third party is actually a set of servers which individually learn nothing, even with a malicious (but non-colluding) adversary.

Multi-Image Steganography Using Deep Neural Networks

Multi-Image Steganography Using Deep Neural Networks

Steganography is the science of hiding a secret message within an ordinary public message. Over the years, steganography has been used to encode a lower resolution image into a higher resolution image by simple methods like LSB manipulation. We aim to utilize deep neural networks for the encoding and decoding of multiple secret images inside a single cover image of the same resolution.

El arte de la Inteligencia Artificial desde una perspectiva léxica

El arte de la Inteligencia Artificial desde una perspectiva léxica

El principal objetivo de este documento es construir un glosario, a partir de las propuestas léxicas realizadas por los diferentes entes tecnológicos (ISO, IEEE, Wikipedia y Oxford University Press). Adicionalmente, el glosario estará estructurado según las ramas de conocimiento de esta área de trabajo, determinando exhaustiva y detalladamente las características de los términos que se incluirán en él para así facilitar una lectura amigable a la par que eficiente al usuario.

R0:3d92323b5375746d21dcb172e8950adc-Explainability in Graph Neural Networks: A Taxonomic Survey

Explainability in Graph Neural Networks: A Taxonomic Survey

We summarize current datasets and metrics for evaluating GNN explainability. Altogether, this work provides a unified methodological treatment of GNN explainability and a standardized testbed for evaluations.

Multi-Image Steganography Using Deep Neural Networks

Fourier Neural Operator for Parametric Partial Differential Equations

The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric dependence to the solution.

R0_b6959f4e35b2c24097cf630fa7cea606-Evaluating and Characterizing Human Rationales

Evaluating and Characterizing Human Rationales

Two main approaches for evaluating the quality of machine-generated rationales are: 1) using human rationales as a gold standard; and 2) automated metrics based on how rationales affect model behavior.

->Artificial intelligence towards data science

Probabilistic Machine Learning for Healthcare

Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.


Meaningful privacy and how it is applied in technology will be the focus of 60 privacy preserving leaders from around the globe during the OpenMined Privacy conference Sept 26 and 27 2020 with more than 2000 in attendance virtually.

Report on Publications Norms for Responsible AI

The history of science and technology shows that seemingly innocuous developments in scientific theories and research have enabled real-world applications with significant negative consequences for humanity.

COCIR analyses application of medical device legislation to Artificial Intelligence

“The European Commission has shown its ambition in the area of artificial intelligence (AI) in its recent White Paper on Artificial Intelligence – a European approach to excellence and trust. This White Paper is at the same time a precursor of possible legislation of AI in products and services in the European Union. However, COCIR sees no need for novel regulatory frameworks for AI-based devices in Healthcare, because the requirements of EU MDR and EU IVDR in combination with GDPR are adequate to ensure that same excellence and trust.” (COCIR paper).

IEEE Use Case–Criteria for Addressing Ethical Challenges in Transparency, Accountability, and Privacy of CTA/CTT

There are substantial public health benefits gained through successfully alerting individuals and relevant public health institutions of a person’s exposure to a communicable disease. Contact tracing techniques have been applied to epidemiology for centuries, traditionally involving a manual process of interview and follow-up. This is time-consuming, difficult, and dangerous work. Manual processes are also open to incomplete information because they rely on individuals being willing and able to remember and report all contact possibilities.

TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

This paper introduces TextAttack, a Python framework for adversarial attacks, data augmentation, and adversarial training in NLP. TextAttack builds attacks from four components: a goal function, a set of constraints, a transformation, and a search method. TextAttack’s modular design enables researchers to easily construct attacks from combinations of novel and existing components. TextAttack provides implementations of 16 adversarial attacks from the literature and supports a variety of models and datasets, including BERT and other transformers, and all GLUE tasks.


Diagnostic uncertainty calibration: towards reliable machine predictions in medical domain

We further formalize the metrics for higher-order statistics, including inter-rater disagreement, in a unified way, which enables us to assess the quality of distributional uncertainty. In addition, we propose a novel post-hoc calibration method that equips trained neural networks with calibrated distributions over class probability estimates. With a large-scale medical imaging application, we show that our approach significantly improves the quality of uncertainty estimates in multiple metrics.


Something went wrong. Please refresh the page and/or try again.

Share this on: