AI systems now operate on a very large scale. Modern deep learning models contain billions of parameters and are trained on large datasets. Therefore, they produce strong accuracy. However, their ...
Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...
When faced with something new, human beings instinctively reach for comparisons. A child learning about atoms might hear that electrons orbit the nucleus “like planets orbit the sun.” An entrepreneur ...
Throughout the year, Covia team members showcased how they solved problems with curiosity, led with courage, and stayed grounded in the communities and teams that shaped them. Here's a look back at ...
Unlock smarter work in NotebookLM with Deep Research and source filters, so you find credible papers faster and build ...
WiMi Studies Quantum Hybrid Neural Network Model to Empower Intelligent Image Classification BEIJING, Jan. 15, 2026––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global ...
Spatiotemporal Evolution Patterns and Intelligent Forecasting of Passenger Flow in Megacity High-Speed Rail Hubs: A Case ...
Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...
Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, structureless data. Yet when trained on datasets with structure, they learn the ...
What if your notebook could think with you, not just for you? Imagine a tool that doesn’t just store your notes but actively helps you connect ideas, solve problems, and even learn faster. That’s the ...
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