Neuro-symbolic AI
Neuro-symbolic AI combines neural networks and symbolic reasoning to enhance artificial intelligence capabilities, aiming for improved understanding and reasoning.
Neural networks
Ilya Sutskever
Ilya Sutskever is a prominent artificial intelligence researcher and co-founder of OpenAI, known for his contributions to deep learning and neural networks.
Fast gradient sign method (FGSM)
The Fast Gradient Sign Method (FGSM) is an adversarial attack technique in machine learning that generates perturbations to deceive neural networks. It uses the gradient of the loss with respect to the input data to create small but effective modifications that cause misclassification.
Deeplearning4j
Deeplearning4j is an open-source, distributed deep learning library for the Java Virtual Machine (JVM). It is designed to facilitate the development and deployment of neural networks and machine learning models within Java and Scala environments.
Batch learning
Batch learning is a machine learning paradigm where the model is trained on a fixed dataset in a single iteration. It contrasts with online learning, where models are updated continuously.
Model predictive control with neural dynamics
Model predictive control with neural dynamics integrates neural network models into the framework of model predictive control for enhanced decision-making in dynamic systems.
Laplace approximation for neural networks
The Laplace approximation for neural networks is a Bayesian technique that approximates the posterior distribution of network parameters using a Gaussian centered at the maximum a posteriori estimate. It provides a computationally efficient way to quantify uncertainty in neural network predictions.
Layer normalization
Layer normalization is a technique used in machine learning to improve the training of deep neural networks by normalizing the inputs across the features.
Catastrophic interference (neural networks)
Catastrophic interference refers to the problem in neural networks where learning new information leads to the loss of previously learned knowledge.
Yoshua Bengio
Yoshua Bengio is a Canadian computer scientist known for his pioneering work in artificial intelligence and deep learning. He is a professor at the University of Montreal and a co-recipient of the 2018 Turing Award for his contributions to neural networks and machine learning.