標(biāo)題: Titlebook: War Finance, Reconstruction, Hyperinflation and Stabilization in Hungary, 1938–48; Pierre L. Siklos Book 1991 Pierre L. Siklos 1991 financ [打印本頁] 作者: ODE 時(shí)間: 2025-3-21 19:29
書目名稱War Finance, Reconstruction, Hyperinflation and Stabilization in Hungary, 1938–48影響因子(影響力)
書目名稱War Finance, Reconstruction, Hyperinflation and Stabilization in Hungary, 1938–48影響因子(影響力)學(xué)科排名
書目名稱War Finance, Reconstruction, Hyperinflation and Stabilization in Hungary, 1938–48網(wǎng)絡(luò)公開度
書目名稱War Finance, Reconstruction, Hyperinflation and Stabilization in Hungary, 1938–48網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱War Finance, Reconstruction, Hyperinflation and Stabilization in Hungary, 1938–48被引頻次
書目名稱War Finance, Reconstruction, Hyperinflation and Stabilization in Hungary, 1938–48被引頻次學(xué)科排名
書目名稱War Finance, Reconstruction, Hyperinflation and Stabilization in Hungary, 1938–48年度引用
書目名稱War Finance, Reconstruction, Hyperinflation and Stabilization in Hungary, 1938–48年度引用學(xué)科排名
書目名稱War Finance, Reconstruction, Hyperinflation and Stabilization in Hungary, 1938–48讀者反饋
書目名稱War Finance, Reconstruction, Hyperinflation and Stabilization in Hungary, 1938–48讀者反饋學(xué)科排名
作者: Temporal-Lobe 時(shí)間: 2025-3-21 23:31 作者: delusion 時(shí)間: 2025-3-22 00:35 作者: minion 時(shí)間: 2025-3-22 06:50
Pierre L. Siklosode similarity under both network structures and contents. To deal with network structures, most existing works assume a given or enumerable set of meta-paths and then leverage them for the computation of meta-path-based proximities or network embeddings. However, expert knowledge for given meta-pat作者: Musket 時(shí)間: 2025-3-22 11:07
Pierre L. Sikloslso plays a vital role in facilitating the downstream offline data analysis process. The sPHENIX detector, located at the Relativistic Heavy Ion Collider in Brookhaven National Laboratory, is one of the largest nuclear physics experiments on a world scale and is optimized to detect physics processes作者: 歡笑 時(shí)間: 2025-3-22 14:32
Pierre L. Siklostain a cluster structure over the data points generated by the entire network. Usual techniques operate by forwarding and concentrating the entire data in a central server, processing it as a multivariate stream. In this paper, we propose ., a new distributed algorithm which reduces both the dimensi作者: Kernel 時(shí)間: 2025-3-22 18:14
Pierre L. Siklosi) a simple convex term, and (iii) a concave and continuous term. First, by extending randomized CD to nonsmooth nonconvex settings, we develop a coordinate subgradient method that randomly updates block-coordinate variables by using block composite subgradient mapping. This method converges asympto作者: 鉗子 時(shí)間: 2025-3-22 22:18
Pierre L. Siklos (CL) or a lower-bound surrogate of the CL. One training procedure is based on the extended Baum-Welch (EBW) algorithm. Similarly, the remaining two approaches iteratively optimize the parameters (initialized to ML) with a 2-step algorithm. In the first step, either the class posterior probabilities作者: 商店街 時(shí)間: 2025-3-23 03:01
Pierre L. Sikloson method exploits the latent feature space learned through an adversarial autoencoder. The proposed method first generates exemplar images in the latent feature space and learns a decision tree classifier. Then, it selects and decodes exemplars respecting local decision rules. Finally, it visualize作者: 財(cái)主 時(shí)間: 2025-3-23 07:29 作者: GAVEL 時(shí)間: 2025-3-23 10:26 作者: consent 時(shí)間: 2025-3-23 16:50 作者: 世俗 時(shí)間: 2025-3-23 20:41 作者: 北極人 時(shí)間: 2025-3-24 00:24 作者: agglomerate 時(shí)間: 2025-3-24 03:59
Pierre L. Siklosry in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic.?.The 210 full papers presented in these proceedings were carefully reviewed and selected fr作者: CAND 時(shí)間: 2025-3-24 07:37 作者: Admire 時(shí)間: 2025-3-24 11:22
Pierre L. Siklosin the Wikipedia domain. In standard transfer learning like T5, a model is first pre-trained on an unsupervised data task with a language model objective before fine-tuning it on a downstream task. T5 explores several learning objectives, including masked language model (MLM), random span, and deshu作者: neoplasm 時(shí)間: 2025-3-24 15:12
Pierre L. Siklosff available to assist customers which results in increased sales, online stores rely on recommender systems. Proposing an outfit with-respect-to the desired product is one such type of recommendation. This paper describes an outfit generation framework that utilizes a deep-learning sequence classif作者: Allodynia 時(shí)間: 2025-3-24 20:12
Pierre L. Siklosank (LTR) algorithms require relevance judgments on products. In E-Com, getting such judgments poses an immense challenge. In the literature, it is proposed to employ user feedback (such as clicks, add-to-basket (AtB) clicks and orders) to generate relevance judgments. It is done in two steps: first作者: AFFIX 時(shí)間: 2025-3-25 00:52 作者: cocoon 時(shí)間: 2025-3-25 06:48 作者: 鉤針織物 時(shí)間: 2025-3-25 11:33
ted reinforcement learning (FedRL) has emerged, wherein agents collaboratively learn a single policy by aggregating local estimations. However, this aggregation step incurs significant communication costs. In this paper, we propose ., a communication-efficient FedRL approach incorporating both . and作者: 美學(xué) 時(shí)間: 2025-3-25 13:23 作者: JECT 時(shí)間: 2025-3-25 19:07
lity of large language models (LLMs). However, existing research often ignores the adverse effect of “Middle Loss” in lengthy input contexts on answer correctness, and the potential negative impact of unverified citations on the quality of attribution. To address these challenges, we propose a frame作者: FLIP 時(shí)間: 2025-3-25 23:35
Hungary and the German War Economy,s government, though apparently attempting to steer a middle course between a fully independent foreign policy toward Germany and that of a vassal state, nevertheless must have recognized that it had compromised its situation vis-à-vis the issue of independence from Germany, especially after the Sec作者: FLAX 時(shí)間: 2025-3-26 03:59 作者: Oafishness 時(shí)間: 2025-3-26 07:06 作者: Gentry 時(shí)間: 2025-3-26 11:23 作者: 啜泣 時(shí)間: 2025-3-26 14:16
ing model which uses reinforcement learning to propose and select subgoals for a planning model to achieve. This includes a novel action selection mechanism and loss function to allow training around the non-differentiable planner. We demonstrate our algorithms effectiveness on a range of domains, i作者: APNEA 時(shí)間: 2025-3-26 16:59 作者: Anhydrous 時(shí)間: 2025-3-26 23:42 作者: 五行打油詩 時(shí)間: 2025-3-27 04:56
Pierre L. Siklos, the central site has the global multivariate state of the entire network. To avoid monitoring all possible states, which is exponential in the number of sensors, the central site keeps a small list of counters of the most frequent global states. Finally, a simple adaptive partitional clustering al作者: 自負(fù)的人 時(shí)間: 2025-3-27 08:54
Pierre L. Siklosgorithm whereby we solve the subproblem inexactly by accelerated coordinate descent (ACD). Convergence is guaranteed with at most a few number of ACD iterations for each DC subproblem, and convergence complexity is established for identifying certain approximate critical points. Fourth, we further d作者: 爵士樂 時(shí)間: 2025-3-27 13:09 作者: Jubilation 時(shí)間: 2025-3-27 14:37
Pierre L. SiklosWe present the results of an experimental evaluation on three datasets and two black box models. Besides providing the most useful and interpretable explanations, we show that the proposed method outperforms existing explainers in terms of fidelity, relevance, coherence, and stability.作者: 空中 時(shí)間: 2025-3-27 20:14 作者: 反復(fù)拉緊 時(shí)間: 2025-3-28 00:44 作者: 調(diào)情 時(shí)間: 2025-3-28 04:27 作者: –FER 時(shí)間: 2025-3-28 08:20 作者: archenemy 時(shí)間: 2025-3-28 11:48
Pierre L. Siklosognition, name variant matching across languages and writing systems, topic detection and tracking, event scenario template filling, and more. Due to the high number of languages covered, linguistics-poor methods were used for the development of these text mining components. See the site http://lang作者: 抱狗不敢前 時(shí)間: 2025-3-28 15:30
Pierre L. SiklosPart III: .Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics...Applied Data Science Track:..Part IV:. Anomaly detection and malware; spatio-temporal data; e-commerce and finance; health作者: fructose 時(shí)間: 2025-3-28 20:04 作者: orthodox 時(shí)間: 2025-3-28 23:54 作者: 發(fā)源 時(shí)間: 2025-3-29 06:33
Pierre L. Siklosspective embedding vector obtained from the Bayesian Personalised Ranking- Matrix Factorisation (BPR-MF) algorithm which takes user clickstream activity as an input. An outfit is classified as positive or negative depending on its Goodness Score predicted by a Bi-LSTM model. Further, we show that ap作者: overture 時(shí)間: 2025-3-29 07:36
Pierre L. Siklos than 10 million AtB click logs and 1 million order logs from a catalogue of about 3.5 million products associated with 3060 queries. To the best of our knowledge, this is the first work which examines effectiveness of CRM approach in learning ranking model from real-world logged data. Our empirical作者: 解脫 時(shí)間: 2025-3-29 15:13
Pierre L. Siklos achieved without knowing which bandit instance is being faced by . on this period, nor knowing a priori the number of possible bandit instances. We provide numerical illustration that confirm the benefit of . while using less information about the problem compared with existing strategies for simil作者: arthroscopy 時(shí)間: 2025-3-29 18:44
Pierre L. Siklosdel incrementally to avoid the instability of joint optimization. Moreover, we explain the cooperation between ranking and generation tasks. Our experiments on the widely used Math23k and MAWPS datasets show that our method can achieve competitive results under low trainable parameters.(Codes are av作者: 魔鬼在游行 時(shí)間: 2025-3-29 19:57
ct or error-feedback compression. Our bounds indicate improved solution accuracy concerning the number of agents and other federated hyperparameters while simultaneously reducing communication costs. To corroborate our theory, we also conduct in-depth numerical experiments to verify our findings, co作者: 永久 時(shí)間: 2025-3-30 02:57
Pierre L. Siklosensional algorithm. The second one is concerned with the Euclidean space equipped with the Manhattan distance. For this metric space, weakly convex sets form a union of pairwise disjoint axis-aligned hyperrectangles. We show that a weakly convex set that is consistent with a set of examples and cont作者: WAX 時(shí)間: 2025-3-30 05:46
ion quality, we design a verification-based iterative optimization algorithm, which continuously updates candidate statements and citations until it produces a satisfactory output result. Experiments on three public knowledge-intensive datasets demonstrate that the proposed framework significantly i作者: Aviary 時(shí)間: 2025-3-30 08:39
War Finance, Reconstruction, Hyperinflation and Stabilization in Hungary, 1938–48作者: 帶來 時(shí)間: 2025-3-30 12:57 作者: 防止 時(shí)間: 2025-3-30 20:15 作者: 催眠 時(shí)間: 2025-3-30 22:01 作者: ARY 時(shí)間: 2025-3-31 04:00
978-1-349-21327-6Pierre L. Siklos 1991作者: 保全 時(shí)間: 2025-3-31 08:25 作者: 放肆的我 時(shí)間: 2025-3-31 10:13
http://image.papertrans.cn/w/image/1020456.jpg作者: 世俗 時(shí)間: 2025-3-31 14:52
https://doi.org/10.1007/978-1-349-21325-2finance; Hyperinflation; Inflation作者: figurine 時(shí)間: 2025-3-31 20:14 作者: HERTZ 時(shí)間: 2025-3-31 23:02 作者: 身心疲憊 時(shí)間: 2025-4-1 02:47 作者: 技術(shù) 時(shí)間: 2025-4-1 08:51
The Lessons to be Learned from the Hyperinflation and Its Termination,onic or moderate inflations. To what extent can such episodes be treated as a laboratory for the testing of economic theory? Because of the importance of this issue, the present chapter attempts to place the Hungarian experience into its proper context.作者: Interferons 時(shí)間: 2025-4-1 11:09 作者: 背帶 時(shí)間: 2025-4-1 14:25 作者: quiet-sleep 時(shí)間: 2025-4-1 21:59
Introduction and Summary,es defined as 1 only a year earlier (15 July 1945), while the average daily rate of inflation in the final week of the hyperinflation stood at 158 486 per cent!. The following day the Hungarian government introduced the florin or ‘forint’ (Ft) to replace the hyperinflated peng? (P). During the first
