Abstract: Neural-network model trading raises two unmet requirements for watermarking: exclusive ownership verification and updateability (transfer/revoke) without retraining. We present a ...

The Heisenberg uncertainty principle puts a limit on how precisely we can measure certain properties of quantum objects. But researchers may have found a way to bypass this limitation using a quantum ...

NeuralStockTrader/ ├── src/ │ ├── data_layer/ # Data management & feature engineering │ │ ├── data_manager.py # OHLCV data fetching, technical indicators │ │ └── feature_engineer.py # Feature ...

In this tutorial, we explore how neural memory agents can learn continuously without forgetting past experiences. We design a memory-augmented neural network that integrates a Differentiable Neural ...

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...

When US officials sanctioned a shipping company named Oceanlink Maritime in April 2024, they had barely scratched the surface of a shipping and oil network that spans Russia’s and Iran’s shadow trades ...

Biomarker discovery and drug response prediction are central to personalized medicine, driving demand for predictive models that also offer biological insights. Biologically informed neural networks ...

This study provides an important set of analyses and theoretical derivations to understand the mechanisms used by recurrent neural networks (RNNs) to perform context-dependent accumulation of evidence ...

The brain criticality hypothesis has been a central research topic in theoretical neuroscience for two decades. This hypothesis suggests that the brain operates near the critical point at the boundary ...