Bias Detection Techniques What are the potential risks of relying too heavily on neural networks in critical applications

Here we employ an information-decomposition framework to investigate neural networks performing cognitive tasks. Our results show that synergy increases as ... Feb 6, 2025 ... Abstract and Figures. While there is much knowledge about local neuronal circuitry, considerably less is known about how neuronal input is ... Jan 16, 2021 ... ... informational and non-informational classes, respectively ... The deep neural network (DNN) classifier obtained the highest accuracy (95.2%). Cognitive Neuroscience 2018: On the biological (or social) expedience of the mechanism of information integration in neural networks of the brain through. View details for Real-time Twitter data sentiment analysis to predict the recession in the UK using Graph Neural Networks. Neural Networks, pp. 271–280. RESEARCH IN PROGRESS. • “Seller-Provided Information and Return Policies for Experience Goods,” under review at. Decision ... Apr 17, 2019 ... ... learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are ... Oct 6, 2022 ... Our results show that synergy increases as neural networks learn multiple diverse tasks. Furthermore, performance in tasks requiring integration ... Computational scientist seeking data science and ML/AI engineering positions · Currently, I am a senior undergraduate student at UVM working on my thesis ... Feb 28, 2024 ... Intellectualization, cybersecurity, biometric image, deepfake, information technology, neural networks, detection system, instrumental platform, ...