In the previous blog post, we discussed about perceptrons. We learnt how to train a perceptron in Python to achieve a simple classification task. If you need a quick refresher on perceptrons, you can check out that blog post before proceeding further. In a way, perceptron is a single layer neural network with a single
Unlocking the Secrets of Neural Comparison Comparing brains and AI models is a fascinating endeavor, but it's not as simple as measuring likeness. We're on a quest to understand the intricate mechanisms that make two neural systems tick in similar ways. This journey is riddled with challenges, from
Understand Word2Vec's key insight — words appearing in similar contexts have similar meanings — and how CBOW and Skip-gram learn word vectors.
# Constructing Organism Networks from Collaborative Self-Replicatorstest Steffen Illium, Maximilian Zorn, Cristian Lenta, Michael Kölle, Claudia Linnhoff-Popien, Thomas Gabor Affiliation: Institute of Informatics, LMU Munich [email protected] ###### Abstract We introduce on, which function like a single nn but are composed of several neural pn; while each pn fulfils the role of a single weight application within the on, it is also trained to self-replicate its own weights. As on feature vastly m
Decrypting intelligence from the human brain construct is vital in the detection of particular neurological disorders. Recently, functional brain connectomes have been used successfully to predict behavioral scores. However, state-of-the-art methods, on one hand, neglect the topological properties of the connectomes and, on the other hand, fail to solve the high inter-subject brain heterogeneity. To address these limitations, we propose a novel regression graph neural network through meta-learning namely Me
So … why aren’t deep neural nets better at language? They’ve done well at object recognition, a sort-of-analogous process in the visual system, where you synthesize lower-level features into higher
Shallow feed-forward networks can emulate the performance of the attention mechanism in Transformers, as demonstrated by their competitive results on sequence-to-sequence
Python, PRML, machine learning
Python, PRML, machine learning
A lot of “capture-the-flag” style ML puzzles give you a black box neural net, and your job is to figure out what it does. When we were thinking of creating our own ML puzzle early last year, we wanted to do something a little different. We thought it’d be neat to give users a complete specification of the neural net, weights and all. They would then be forced to use the tools of mechanistic interpretability to reverse engineer the network—which is a situation we sometimes find ourselves facing in