Relational inductive biases, deep learning, and graph networks Battaglia et al., arXiv'18 Earlier this week we saw the argument that causal reasoning (where most of the interesting questions lie!) requires more than just associational machine learning. Structural causal models have at their core a graph of entities and relationships between them. Today we’ll be looking

Giacomo Tesio - A decompiler for artificial neural network

I encountered a problem with extremely large neural network that was created in KERAS, using Tensorflow backend. The memory footprint in one of the layer is already bigger than the size of current GPU memory (it has just

Python, neural network, Chainer

Bayesian Flow Networks introduce a new class of generative models that achieve a fully continuous and differentiable information transmission process, even for discrete data, by combining iterative

PMML 4.2 - Neural Network Models ## PMML 4.2 - Neural Network Models Neural Network Models for Backpropagation The description of neural network models assumes that the reader has a general knowledge of artificial neural network technology. A neural network has one or more input nodes and one or more neurons. Some neurons' outputs are the output of the network. The network is defined by the neurons and their connections, aka weights. All neurons are organized into layers; the sequence of layers defines t

Summaries of the research papers published in Neural Information Processing Systems Conference

top of page Meritocratic.Capital Ventures Knowledge Hub About Tech Blog Careers Tryout Program More Use tab to navigate through the menu items. Alphanome Log In All Posts Search Understanding Graph Attention Networks (GATs) and Causal AI: A Guide for Investors Aki Kakko Oct 31, 2023 4 min read Updated: Nov 27, 2025 Graph Attention Networks (GATs) are an exciting frontier in the domain of machine learning , specifically in the realm of graph -based deep learning . They are designed to handle data structured

The text discusses the challenges of training multiple neural networks, the unreliability of complex systems, and the issues faced in financial data analysis and trading. It also mentions personal experiences with model training, the importance of simplicity, and the difficulties in managing financial investments and trading strategies

EDIT 4/25/23This blog post went viral in 2020 and this idea is now widely accepted by the deep learning community. In fact, this is not only the most read post on my blog, but I might say that this is my most influential scientific idea! So, if you want to mention it in a paper,…