Tag: deep learning
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Effective Depth vs Nominal Depth in Deep CNN Trainability
Explore how architectural topology impacts trainability and performance of deep CNNs, focusing on effective depth versus nominal depth.
PathFinder: Enhanced Path Loss Prediction for Multi-Transmitters
Discover PathFinder, a novel model improving path loss prediction in multi-transmitter 5G scenarios with advanced environmental modeling.
Nemotron-Cascade: Scalable Cascade RL for Reasoning Models
Discover Nemotron-Cascade, a scalable cascaded reinforcement learning model enhancing general-purpose reasoning with state-of-the-art performance and effic...
Error Bounds for PINNs Solving Semilinear Wave Equations
Discover rigorous error bounds for physics-informed neural networks approximating semilinear wave equations to improve accuracy and training efficiency.
Efficient Hybrid Sequence Models via Generation-Focused Distillation
Discover how generation-focused distillation improves hybrid sequence models, reducing inference costs while maintaining high-quality output generation.