Spiking Neural Networks (SNNs) are emerging as a brain-inspired alternative to traditional Artificial Neural Networks (ANNs), prized for their potential energy efficiency
Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews the main challenges in sampling from the parameter posterior of a neural network via MCMC. Such challenges culminate to lack of convergence to the parameter posterior. Nevertheless, this paper shows that a non-converged Markov chain, generated via MCMC sampling from the parameter space of a neural network, can yield via Bayesian marginalization a valuable posterior predictiv
Abstract page for arXiv paper 1911.11455: Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs
You often heard about AI learning to trade, predicting stock prices, recognizing speech, translating language, and even generating human-level text from scratch in a modern scenario. All the advancements in these areas started from the idea of sequence modeling . Sequence modeling is a method of…
Our aim is to investigate the ability of neural networks to model different two-locus disease models. We conduct a simulation study to compare neural netwo
User profiling approaches that model the interaction between users and items (behavioral user profiling) via Graph Neural Networks (GNNs) are unfair toward certain demographic groups. In a CIKM 2022 study, conducted with Erasmo Purificato and Ernesto William De Luca, we perform a beyond-accuracy analysis of the state-of-the-art approaches to assess the presence of disparate impact
Skip to content Publish AI, ML & data-science insights to a global community of data professionals. Sign in Submit an Article Toggle Mobile Navigation Toggle Search Search Logic Explained Networks Deep learning models explainable by design Pietro Barbiero Aug 12, 2021 13 min read Share Thoughts and Theory TL;DR Problem – Neural networks cannot explain how they arrive to a prediction, hence their deployment in safety-critical applications is discouraged. Solution – Logic Explained Networks are novel
Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret “hairball” layouts. Here we use Graph Neural
Toggle navigation Alianna J. Maren Alianna J. Maren Statistical Mechanics, Neural Networks, Artificial Intelligence Selecting a Neural Network Transfer Function: Classic vs. Current Selecting a Neural Network Transfer Function: Classic vs. Current October 4, 2017 AJMaren Comments 2 comments Neural Network Transfer Functions: Sigmoid, Tanh, and ReLU Making it or breaking it with neural networks: how to make smart choices. Why We Weren’t Getting Convergence This last week, in working with a very simple and
Miguel Aguilera's academic website. Complex Systems, Computational Neuroscience, Cognitive Systems, Artificial Life, Adaptive Behavior