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Dimension-Free Bounds for Low-Precision Training
Zheng Li, Christopher M. De Sa
Neural Information Processing Systems http://nips.cc/
Appendix of " Complex-valued Neurons Can Learn More but Slower than Real-valued Neurons via Gradient Descent " A Preliminaries
In this section, we first summarize frequently used notations in the following table. Table 4: Frequently used notations.Notation Description C Lemma 7. Let d = 1 . Combining the cases above completes the proof. Subsection B.2 proves several convergence rate lemmas. Subsection B.3 gives some technical We are now ready to prove Theorem 1. Proof of Theorem 1.
- North America > United States (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
Appendix of " Complex-valued Neurons Can Learn More but Slower than Real-valued Neurons via Gradient Descent " A Preliminaries
In this section, we first summarize frequently used notations in the following table. Table 4: Frequently used notations.Notation Description C Lemma 7. Let d = 1 . Combining the cases above completes the proof. Subsection B.2 proves several convergence rate lemmas. Subsection B.3 gives some technical We are now ready to prove Theorem 1. Proof of Theorem 1.
Dimension-Free Bounds for Low-Precision Training
Zheng Li, Christopher M. De Sa
Our methods also generalize naturally to let us prove new convergence bounds on low-precision training with other quantization schemes, such as low-precision floating-point computation and logarithmic quantization.