This blog post is the second in our Neural Super Sampling (NSS) series. The post explores why we introduced NSS and explains its architecture, training, and inference components. In August 2025, we ...
Compared to other regression techniques, a well-tuned neural network regression system can produce the most accurate prediction model, says Dr. James McCaffrey of Microsoft Research in presenting this ...
Stop throwing money at GPUs for unoptimized models; using smart shortcuts like fine-tuning and quantization can slash your ...
A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from the ...
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A simple physics-inspired model sheds light on how AI learns
Artificial intelligence systems based on neural networks—such as ChatGPT, Claude, DeepSeek or Gemini—are extraordinarily ...
Researchers use statistical physics and "toy models" to explain how neural networks avoid overfitting and stabilize learning in high-dimensional spaces.
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AI Researchers Are Confronting the Gap Between Neural Network Power and True Generalization
In 2026, neural networks are achieving unprecedented capabilities across industries, yet large-scale tests reveal persistent struggles with generalization. Researchers are exploring adaptive ...
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