Recurrent Neural Networks (RNNs) are naturally suited to efficient inference, requiring far less memory and compute than attention-based architectures, but the sequential nature of their computation has historically made it impractical to scale up RNNs to billions of parameters. A new advancement from Apple researchers makes RNN training dramatically more efficient — enabling large-scale training for the first time and widening the set of…
Apple is advancing AI and ML with fundamental research, much of which is shared through publications and engagement at conferences in order to accelerate progress in this important field and support the broader community. This week, the Fourteenth International Conference on Learning Representations (ICLR) will be held in Rio de Janeiro, Brazil, and Apple is proud to again participate in this important event for the research…
