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The Battle of the Somme 1916: Developing the Offensive – July to Mid September

為了解決Expensive的問題，作者Cave, Nigel/ Sheldon, Jack 這樣論述：

After the initial anticipation of great results for the Allied offensive that opened on 1 July, the French and the British had to consider their next moves. Haig made the fateful decision to reinforce perceived success at the center and south of the British line (although Joffre, rightly, wished

to continue the pressure at Thiepval). The result was a series of minor (if expensive) operations to provide a suitable base line for the next major British assault along the Bazentin Ridge, running approximately from east of Longueval to west of Bazentin le Petit Wood. Thus Ovillers, Mametz Wood an

d Tr nes Wood became prominent in Britain's military history. The French soon began to appreciate that the great success south of the river on 1 July was not going to achieve much more unless the front was extended southwards (impractical, given pressure at Verdun and limited manpower resources); or

if advances could be made north of the river that would outflank the Germans to the south. Meanwhile Falkenhayn continued to believe in the imminence of British offensive action further north, in French Flanders, despite the fact that he was reassured time and again that there was no evidence for t

his and that in any case such an eventuality could be contained with reduced resources. Eventually the offensive in Verdun was halted, in late August Falkenhayn was removed after he had presided over increasing friction at the highest level on the Somme front amongst senior commanders; Ludendorff an

d Hindenburg took over and the genius of German defensive measures, Lo berg, arrived on the scene. This book covers actions at Ovillers, Pozi res (notably involving the Australians) Mametz, Delville Wood (South Africa's first great war time action in Europe), the bitter fighting at High Wood, all le

ading up to the great attack on the Somme on 15 September. This was the third such major effort by the British army and the first time since 1 July that the Allies had attacked simultaneously in strength. The book then looks at aspects of the fighting associated with this attack, in particular the r

ole of the New Zealand Division and of the Guards Division around Les Boeufs. It then concentrates on the Anglo French boundary area (Guillemont and Combles) before considering French activity at Maurepas, Cl ry, Biaches and La Maisonette and the extension of the French front on 3 September, with fi

ghting at Soy court, Lihons and Vermandovillers. The book ends with a review of the situation both sides found themselves in mid September, before the action continued its relentless grind at extraordinary cost in men and materiel.

Expensive進入發燒排行的影片

貝氏最佳化的小樣本採集函數學習

為了解決Expensive的問題，作者謝秉瑾 這樣論述：

貝氏最佳化 (Bayesian optimization, BO) 通常依賴於手工製作的採集函數 (acqui- sition function, AF) 來決定採集樣本點順序。然而已經廣泛觀察到，在不同類型的黑 盒函數 (black-box function) 下，在後悔 (regret) 方面表現最好的採集函數可能會有很 大差異。 設計一種能夠在各種黑盒函數中獲得最佳性能的採集函數仍然是一個挑戰。 本文目標在通過強化學習與少樣本學習來製作採集函數(few-shot acquisition function, FSAF)來應對這一挑戰。 具體來說，我們首先將採集函數的概念與 Q 函數 (Q

-function) 聯繫起來，並將深度 Q 網路 (DQN) 視為採集函數。 雖然將 DQN 和現有的小樣本 學習方法相結合是一個自然的想法，但我們發現這種直接組合由於嚴重的過度擬合(overfitting) 而表現不佳，這在 BO 中尤其重要，因為我們需要一個通用的採樣策略。 為了解決這個問題，我們提出了一個 DQN 的貝氏變體，它具有以下三個特徵: (i) 它 基於 Kullback-Leibler 正則化 (Kullback-Leibler regularization) 框架學習 Q 網絡的分佈(distribution) 作為採集函數這本質上提供了 BO 採樣所需的不確定性並減輕了

過度擬 合。 (ii) 對於貝氏 DQN 的先驗 (prior)，我們使用由現有被廣泛使用的採集函數誘導 學習的演示策略 (demonstration policy)，以獲得更好的訓練穩定性。 (iii) 在元 (meta) 級別，我們利用貝氏模型不可知元學習 (Bayesian model-agnostic meta-learning) 的元 損失 (meta loss) 作為 FSAF 的損失函數 (loss function)。 此外，通過適當設計 Q 網 路，FSAF 是通用的，因為它與輸入域的維度 (input dimension) 和基數 (cardinality) 無 關。通過廣

泛的實驗，我們驗證 FSAF 在各種合成和現實世界的測試函數上實現了與 最先進的基準相當或更好的表現。

The World’’s Most Expensive Watches

為了解決Expensive的問題，作者Adams, Ariel 這樣論述：

Ariel Adams is the owner and editor of aBlogtoWatch.com - the world’s largest and most popular wrist watch blog, and regularly contributes to other important media such as Forbes, Centurion, Tech Crunch, and more.

以機器學習手法預測保證通過系統級測試之晶片

為了解決Expensive的問題，作者陳玥融 這樣論述：

近年來，如何在維持低百萬次錯誤率(DPPM)的水準下同時降低IC 測試開銷已成為半導體產業重要的研究課題。為了有效降低系統級測試(SLT)的成本，本論文提出一套利用機器學習手法來挑選出保證通過系統級測試之晶片的方法。我們我們首先以神經網路對輸入資料進行特徵空間轉換，並利用在該空間中資料集的分布特性篩選出保證會通過系統級測試的IC。被我們的手法判定為會通過系統級測試的IC 可跳過系統級測試直接進入出貨階段，進而降低整體測試時間。將我們的手法套用在業界資料後，可以成功篩選出1.8%的保證通過系統級測試的IC，且其中不包含測試逃脫(Test Escape)。