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ztz_t

This entry links every published equation using this exact notation. Its meaning may change between equations.

Meanings in context

Used in 10 equations

Ldistill=(1−λ) LCE(y, σ(zs))+λ T2 DKL(σ(zt/T) ∥ σ(zs/T))\mathcal{L}_{\mathrm{distill}} = (1-\lambda)\,\mathcal{L}_{\mathrm{CE}}(y,\, \sigma(z_s)) + \lambda\, T^2 \, D_{\mathrm{KL}}\big(\sigma(z_t/T) \,\|\, \sigma(z_s/T)\big)

Comparing the Main Approaches to Training Data and Synthetic Data · Equation 6

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

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Ldistill=(1−α) CE(y, σ(zs))  +  α T2 CE(σ(zt/T), σ(zs/T))\mathcal{L}_{\text{distill}} = (1-\alpha)\,\mathrm{CE}\big(y,\ \sigma(z_s)\big) \;+\; \alpha\, T^2\,\mathrm{CE}\big(\sigma(z_t/T),\ \sigma(z_s/T)\big)

Shrink It, Train It Small, or Search for It: The Main Strategies for Small Models, Compared · Equation 1

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

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LKD=α LCE(y,σ(zs))+(1−α) T2 KL(σ(zt/T) ∥ σ(zs/T))\mathcal{L}_{\mathrm{KD}} = \alpha \, \mathcal{L}_{\mathrm{CE}}\left(y, \sigma(z_s)\right) + (1-\alpha)\, T^2 \, \mathrm{KL}\left(\sigma(z_t / T) \,\Vert\, \sigma(z_s / T)\right)

How a Model Actually Gets Small Enough to Run on a Phone · Equation 7

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

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Lseq=−∑t=1Tlog⁡pθ ⁣(zt∣z<t),zt∈{0,1,…,V−1}\mathcal{L}_{\text{seq}} = -\sum_{t=1}^{T} \log p_\theta\!\left(z_t \mid z_{<t}\right), \qquad z_t \in \{0, 1, \dots, V-1\}

Adapters, Native Pretraining, and Unified Tokens: The Main Multimodal Architectures, Compared · Equation 9

This equation states a bound: one expression must stay on the indicated side of the other under the article’s assumptions.

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