Explore the latest AI news and research tagged #deep learning — curated from top sources including OpenAI, Anthropic, Google DeepMind, and more.
Exploring the inner workings of a decoder-only Transformer foundation model The post Timer-XL: A Long-Context Foundation Model for Time-Series Forecasting appeared first on Towards Data Science .
Solving multiplayer games with function approximation The post Playing Connect Four with Deep Q-Learning appeared first on Towards Data Science .
A review of the Cross-Stage Partial Network paper — and a from-scratch PyTorch implementation The post CSPNet Paper Walkthrough: Just Better, No Tradeoffs appeared first on Towards Data Science .
One scale parameter determines accuracy in rotation-based vector quantization. The post How a 2021 Quantization Algorithm Quietly Outperforms Its 2026 Successor appeared first on Towards Data Science .
NaNs don’t crash your training — they quietly destroy it. After losing hours to a silent failure in a ResNet training run, I built a lightweight detector that pinpoints the…
Google just changed how developers do research. On April 21, 2026, they launched Deep Research Max. It runs on Gemini 3.1 Pro and is not just another chatbot upgrade. This…
Why learn 8 scripts when you can learn 256 bytes? The post Bytes Speak All Languages: Cross-Script Name Retrieval via Contrastive Learning appeared first on Towards Data Science .
Apple is presenting new research at the annual International Conference on Learning Representations (ICLR) , which takes place in person in Rio de Janeiro, Brazil, from April 23 to 27.…
What is the Role of Mathematics in Modern Machine Learning? The past decade has witnessed a shift in how progress is made in machine learning. Research involving carefully designed and…
Is Attention all you need? Mamba, a novel AI model based on State Space Models (SSMs), emerges as a formidable alternative to the widely used Transformer models, addressing their inefficiency…
By AI Trends Staff Advances in the AI behind speech recognition are driving growth in the market, attracting venture capital and funding startups, posing challenges to established players. The growing…
What components are needed for building learning algorithms that leverage the structure and properties of graphs?
We present techniques for visualizing, contextualizing, and understanding neural network weights.
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.
Detailed derivations and open-source code to analyze the receptive fields of convnets.