Hugging Face datasets
One-line dataloaders for many public datasets & Efficient data pre-processing
Research
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
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.
Multi-Image Steganography Using Deep Neural Networks Leer más »
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.
El arte de la Inteligencia Artificial desde una perspectiva léxica Leer más »
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.
Explainability in Graph Neural Networks: A Taxonomic Survey Leer más »
Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets
«We have demonstrated a proof-of-principle application of unsupervised deep clustering and reinforcement learning to segment brain tumors. The approach represents human-allied AI that requires minimal input from the radiologist without the need for hand-traced annotation».
Side-Channel Sensing: Exploiting Side-Channels to Extract Information for Medical Diagnostics and Monitoring
Information within systems can be extracted through side-channels; unintended communication channels that leak information. The concept of side-channel sensing is explored, in which sensor data is analysed in non-trivial ways to recover subtle, hidden or unexpected information.
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.
Fourier Neural Operator for Parametric Partial Differential Equations Leer más »
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.
Medical notes summariser: «Characterizing the Value of Information in Medical Notes»
Machine learning models depend on the quality of input data. As electronic health records are widely adopted, the amount of data in health care is growing, along with complaints about the quality of medical notes.
Medical notes summariser: «Characterizing the Value of Information in Medical Notes» Leer más »