Google & Lund U’s Optimus Learned Optimization Architecture Efficiently Captures Complex Dependencies | Synced
In the new paper Transformer-Based Learned Optimization, a Google Research and Lund University team presents Optimus, an expressive neural network architecture for learned optimization that capture...
Source: Synced | AI Technology & Industry Review
In the new paper Transformer-Based Learned Optimization, a Google Research and Lund University team presents Optimus, an expressive neural network architecture for learned optimization that captures complex dependencies in the parameter space and achieves competitive results on real-world tasks and benchmark optimization problems.