Your hub for Neural Networks news and research — curated daily from 50 top AI sources including OpenAI, Anthropic, Google DeepMind, and more. Every article is reviewed and enriched with editorial analysis by the DeepTrendLab team.
When ChatGPT launched as an experimental prototype in late 2022, OpenAI’s chatbot became an everyday everything app for hundreds of millions of people. LLMs like ChatGPT were the new future: The entire tech industry was consumed by the inferno, with companies racing to spin up rival products. The ashes of the old tech world still…
What exactly does word2vec learn, and how? Answering this question amounts to understanding representation learning in a minimal yet interesting language modeling task. Despite the fact that word2vec is a…
Large Language Models (LLMs) have come a long way since their early days of mimicking autocomplete on steroids. But generating fluent text isn’t enough – true intelligence demands reasoning. That…
Exploring the utility of large language models in autonomous driving: Can they be trusted for self-driving cars, and what are the key challenges?
In this article, we will talk about classical computation : the kind of computation typically found in an undergraduate Computer Science course on Algorithms and Data Structures [1]. Think shortest…
Summary: recently while fine-tuning a large language model (LLM) on multiple-choice science exam questions, we observed some highly unusual training loss curves. In particular, it appeared the model was able…
Understanding the building blocks and design choices of graph neural networks.
Weights in the final layer of common visual models appear as horizontal bands. We investigate how and why.
When a neural network layer is divided into multiple branches, neurons self-organize into coherent groupings.
We report the existence of multimodal neurons in artificial neural networks, similar to those found in the human brain.
Neural Cellular Automata learn to generate textures, exhibiting surprising properties.
We present techniques for visualizing, contextualizing, and understanding neural network weights.
Reverse engineering the curve detection algorithm from InceptionV1 and reimplementing it from scratch.
A family of early-vision neurons reacting to directional transitions from high to low spatial frequency.
Neural networks naturally learn many transformed copies of the same feature, connected by symmetric weights.
Part one of a three part deep dive into the curve neuron family.
An overview of all the neurons in the first five layers of InceptionV1, organized into a taxonomy of 'neuron groups.'
By focusing on linear dimensionality reduction, we show how to visualize many dynamic phenomena in neural networks.
What can we learn if we invest heavily in reverse engineering a single neural network?
By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.
Training an end-to-end differentiable, self-organising cellular automata model of morphogenesis, able to both grow and regenerate specific patterns.
Exploring the baseline input hyperparameter, and how it impacts interpretations of neural network behavior.
The main hypothesis in Ilyas et al. (2019) happens to be a special case of a more general principle that is commonly accepted in the robustness to distributional shift literature
An example project using webpack and svelte-loader and ejs to inline SVGs