Home # Technical Indicator Networks ## From Charts to Circuits: How TINs Rewire Technical Analysis for the AI Era TL;DR for operators Trading platforms have spent decades giving users fixed technical indicators and then, more recently, neural models that treat those indicators as just another column in a feature table. Longfei Lu’s paper on Technical Indicator Networks, or TINs, proposes a different wiring job: make the indicator itself into the neural architecture.1 ... August 3, 2025 · 14 min · Zelina
Skip to main content Neural Network A neural network is an AI model that teaches computers to process data by modeling it on how the human brain works. It is a type of machine learning (ML) process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. It creates an adaptive system that computers use to learn from their mistakes and continually improve. Artificial neural networks thus attempt to solve complex problems. How a Neural Network wor
Learning probability distributions on the weights of neural networks has recently proven beneficial in many applications. Bayesian methods such as Stochastic
Convolutional neural networks (CNNs) achieve state-of-the-art performance in a wide variety of tasks in computer vision. However, interpreting CNNs still remains a challenge. This is mainly due to the large number of parameters in these networks. Here, we investigate the role of compression and particularly pruning filters in the interpretation of CNNs. We exploit our recently-proposed greedy structural compression scheme that prunes filters in a trained CNN. In our compression, the filter importance index
For decades, theorists argued connectionist models fundamentally lack the structure needed for compositionality. But modern LLMs exhibit sophisticated compositional behaviors despite sharing the same design principles. What changed?
A neural network is a machine learning algorithm that is designed to recognize patterns. Neural networks are similar to the human brain in that they are made
R-CNN (Region-based Convolutional Neural Network) is an object detection model that finds and labels objects in an image by proposing candidate regions, then
Skip to content Publish AI, ML & data-science insights to a global community of data professionals. Sign in Submit an Article Toggle Mobile Navigation Toggle Search Search Deep Learning Do Vision Transformers See Like Convolutional Neural Networks? (Paper Explained) I will take a closer look at the differences in the obtained representations between CNN and Transformers Akihiro FUJII Oct 9, 2021 18 min read Share Thoughts and Theory Vision Transformer (ViT) has been gaining momentum in recent years. This ar
## Graph-based Dependency Parsing with Graph Neural Networks Tao Ji , Yuanbin Wu , Man Lan We investigate the problem of efficiently incorporating high-order features into neural graph-based dependency parsing. Instead of explicitly extracting high-order features from intermediate parse trees, we develop a more powerful dependency tree node representation which captures high-order information concisely and efficiently. We use graph neural networks (GNNs) to learn the representations and discuss several ne
In the previous article, I was talking about what Neural Networks are and how they are trying to imitate biological neural system. Also, the structure of the neuron, smallest building unit of these networks, was presented. Neurons have this simple structure, and one might say that they alone are useless. Nevertheless, when they are connected with