Yup, it's a very good question. I'm thinking of a very specific definition of learning, Tom Mitchell's definition, which is basically any system that gets better at doing some task when exposed to more experience. In this sense, the NN underlying the LLM is definitely not learning, but the chatbot as a system can be seen as a learning system, because with more experience (more data to do RAG from) it gets better at answering questions. So yes, nuances :)
Modern Hopfield networks have enjoyed recent interest due to their connection to attention in transformers. Our paper provides a unified framework for sparse Hopfield networks by establishing a link with Fenchel-Young losses. The result is a new family of Hopfield-Fenchel-Young energies whose update rules are end-to-end differentiable sparse transformations. We reveal a connection between loss margins, sparsity, and exact memory retrieval. We further extend this framework to structured Hopfield networks via
Dynamics of neural circuits, representation of information, and behavior on Simons Foundation
Fundamentals of Statistics contains material of various lectures and courses of H. Lohninger on statistics, data analysis and chemometrics... ...click here for more . Index ANN - Recurrent Networks Networks with feedback loops belong to the group of recurrent networks. There, unit activations are not only delayed while being fed forward through the network, but are also delayed and fed back to preceding layers. This way, information can cycle in the network. At least theoretically, this allows an unlimited
Python, machine learning, Neural Network
Convolutional neural networks, despite their profound impact in countless domains, suffer from significant shortcomings. Linearly-combined scalar feature representations and max pooling operations lead to spatial ambiguities and a lack of robustness to pose variations. Capsule networks can potentially alleviate these issues by storing and routing the pose information of extracted features through their architectures, seeking agreement between the lower-level predictions of higher-level poses at each layer
What is Nengo? Examples Documentation All documentation Community Forum Coming from Nengo to NengoDL Coming from TensorFlow to NengoDL Integrating a Keras model into a Nengo network Optimizing a spiking neural network Converting a Keras model to a spiking neural network Converting a Keras model to a spiking neural network ¶ A key feature of NengoDL is the ability to convert non-spiking networks into spiking networks. We can build both spiking and non-spiking networks in NengoDL, but often we may have an
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Discover the Surprising Dangers of Radial Basis Function Networks in AI and Brace Yourself for Hidden GPT Risks
Neural Network Attributions: A Causal PerspectiveAditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, Vineeth N BalasubramanianWe propose