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Poster
Memory Efficient Adaptive Optimization
Rohan Anil · Vineet Gupta · Tomer Koren · Yoram Singer

Thu Dec 12 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #168
in Deep Learning -- Optimization for Deep Networks »

Adaptive gradient-based optimizers such as Adagrad and Adam are crucial for achieving state-of-the-art performance in machine translation and language modeling. However, these methods maintain second-order statistics for each parameter, thus introducing significant memory overheads that restrict the size of the model being used as well as the number of examples in a mini-batch. We describe an effective and flexible adaptive optimization method with greatly reduced memory overhead. Our method retains the benefits of per-parameter adaptivity while allowing significantly larger models and batch sizes. We give convergence guarantees for our method, and demonstrate its effectiveness in training very large translation and language models with up to 2-fold speedups compared to the state-of-the-art.

Keywords: Optimization · Algorithms -> Online Learning; Deep Learning -> Optimization for Deep Networks; Optimization · Convex Optimization; Optimizati
[ Paper]  [ Slides] 

Author Information

Rohan Anil (Google)
Vineet Gupta (Google)
Tomer Koren (Google)
Yoram Singer (Google)

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