The fundamentals — Part 1 of a deep dive into LLMs
The text discusses the comparison of different machine learning models, including neural networks and decision trees, on a dataset with new predictors. It highlights that while the neural network's performance remained similar, the decision tree showed significant improvement. The analysis suggests that generating a large number of features can enhance model performance, and convolutional networks may offer better results than traditional methods. The author also reflects on the challenges of feature engine
Modern machine learning models are beginning to rival human performance on some realistic object recognition tasks, but we still lack a full understanding of how the human brain solves this same problem. This thesis combines knowledge from machine learning and computational neuroscience to create models of human object recognition that are increasingly realistic both in their treatment of low-level neural mechanisms and in their reproduction of high-level human behaviour. First, I present extensions to the
Deep neural networks have been shown to be highly miscalibrated. often they tend to be overconfident in their predictions. It poses a significant challenge for safety-critical systems to utilise deep neural networks (DNNs), reliably. Many recently proposed approaches to mitigate this have demonstrated substantial progress in improving DNN calibration. However, they hardly touch upon refinement, which historically has been an essential aspect of calibration. Refinement indicates separability of a network's c
Conditional Neural Processes (CNPs) from DeepMind introduce a neural architecture that learns to approximate functions and quantify uncertainty from limited observations. This framework achieves
dynamic neural networks, efficient neural networks
Search # End-To-End Memory Networks Sainbayar Sukhbaatar, arthur szlam, Jason Weston, Rob Fergus Advances in Neural Information Processing Systems 28 (NIPS 2015) ## Abstract We introduce a neural network with a recurrent attention model over a possibly large external memory. The architecture is a form of Memory Network (Weston et al., 2015) but unlike the model in that work, it is trained end-to-end, and hence requires significantly less supervision during training, making it more generally applicable
Search # End-To-End Memory Networks Sainbayar Sukhbaatar, arthur szlam, Jason Weston, Rob Fergus Advances in Neural Information Processing Systems 28 (NIPS 2015) ## Abstract We introduce a neural network with a recurrent attention model over a possibly large external memory. The architecture is a form of Memory Network (Weston et al., 2015) but unlike the model in that work, it is trained end-to-end, and hence requires significantly less supervision during training, making it more generally applicable
Examples / # NEURAL.RPF NEURAL.RPF fits a neural network to a binary choice model using the data set from the PROBIT.RPF example. Like a probit model (also estimated here), the neural net attempts to explain the YESVM data given the characteristics of the individuals. Aside from a different functional form, the neural net model also differs by using the sum of squared errors rather than the likelihood as a criterion function. This first does a linear probability model (LPM) with "fitted" values. Because
Open Menu Proceedings of the AAAI Conference on Artificial Intelligence Search - Home - / - Archives - / - Vol. 37 No. 8: AAAI-23 Technical Tracks 8 - / - AAAI Technical Track on Machine Learning III # Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic Parse-Tree Assumption ## Authors - Matthias Mitterreiter - Friedrich-Schiller-University, Jena, Germany - Data Assessment Solutions GmbH, Hannover, Germany - Marcel Koch - Ernst Abbe University of Applied Sciences, Jena, Germany - Joachim