Reproducible Deep Learning

Reproducible deep learning aims to ensure that deep learning experiments can be reliably replicated, yielding consistent results across different hardware, software, and random seeds. Current research focuses on identifying and mitigating sources of variability, including hardware non-determinism and software randomness, often employing techniques like record-and-replay or profile-and-patch methods across various architectures such as convolutional neural networks (CNNs) and transformer-based models. Achieving reproducibility is crucial for validating research findings, building trust in AI systems, and facilitating the reliable deployment of deep learning models in real-world applications. This includes developing frameworks and APIs that streamline the process of creating and managing reproducible deep learning pipelines.