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6 Nov 2019: 213.2 Independent Compression. 24. 4.1 Experimental Setup. 254.2 Image Examples from the MNIST Database. ... soft-weight sharing[24], and modifies it with the aim of further improving compression.

18 Nov 2019:  (2.24)where 0 = 01T1 is the zero matrix and > 0 = > 0T1T1 is the non zero matrix.To solve these numerical problems, we have rearranged the positions of zero elements ... 24 Algorithm Design. Algorithm 1 Probabilistic Line Search1: Input:

30 Oct 2019: D, xı. )È6. (2.24). We will discuss the derivation and computation of the PES acquisition function in Chap-ter 3. ... n. (x), vn. (x)) where. µ. n. (x) = „(x)( ‡2I)1y (3.24)v.

15 Nov 2021: 24. 3.3 Flow at 100 Reynolds Number. 31. 3.3.1 Flow at 100 Reynolds Number: Assumptions. ... 41. 3.25 Absolute discrepancy between the reconstruction and ground truth z-velocityfields from Figure 3.24.

17 Nov 2023: 24. 3.4 Evaluation Framework. 25. viii Table of contents. 3.4.1 Evaluating Code Generations for Correctness. ... complex, compositional programs (Dziri et al. [24]). One reason for this limitation is their in-.

11 Feb 2021: Interpretable Policy Learning. Alex J. Chan. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine Intelligence. Wolfson College August 2020. This

23 Nov 2023: 24. 5.1 Replication of RAG results for Task II. 385.2 Shared Task Evaluation for generator-only Chat Completion versions.

30 Oct 2019: T#. ]Ep(f | y) [f#] Ep(f | y). [f T#. ] K#,# Q#,# R#,# (1.24).

17 Nov 2023: 24 Parametrised Teacher. The gradient for policy updates is given by.

18 Nov 2019: 12. 3.1 Cart-pole PILCO environment. 24. 3.2 Pendubot PILCO environment. 24. ... p(|,X̃,x̃,σ 20 ,σ 2n ) = N(|µ,σ. 2). (2.22). whereµ =. 1σ 2n. ϕ(x̃)A1Φ (2.23). σ 2 = σ2n ϕ(x̃). A1ϕ(x̃). (2.24).