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Equation 19 · Part 2 · Shrink It, Train It Small, or Search for It: The Main Strategies for Small Models, Compared

Symbol a

a∗=arg⁡min⁡a∈A Lval(w∗(a), a)subject tog(a)≤B,w∗(a)=arg⁡min⁡w Ltrain(w,a)a^* = \arg\min_{a \in \mathcal{A}} \ \mathcal{L}_{\text{val}}\big(w^*(a),\, a\big) \quad \text{subject to} \quad g(a) \le B, \qquad w^*(a) = \arg\min_{w} \ \mathcal{L}_{\text{train}}(w, a)
aa

What this part means

a is part of the quantity the equation computes from the expression on the right.

Its job in the formula

a is part of the quantity the equation computes from the expression on the right.

The passage around this formula

Zoph and Le established the modern form of the idea: a controller network, trained by reinforcement learning, proposes candidate child-network architectures, each of which is trained and evaluated, with the resulting performance used as a reward signal to improve the controller [ 7 ] . Formally, a NAS run of this kind is a constrained, nested…

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Sources cited in the surrounding passage

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