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21#
發(fā)表于 2025-3-25 04:11:50 | 只看該作者
22#
發(fā)表于 2025-3-25 10:34:06 | 只看該作者
23#
發(fā)表于 2025-3-25 14:28:15 | 只看該作者
FFD Augmentor: Towards Few-Shot Oracle Character Recognition from?Scratchof collecting labeled data, recognizing oracle characters is naturally a Few-Shot Learning (FSL) problem, which aims to tackle the learning problem with only one or a few training data. Most current FSL methods assume a disjoint but related big dataset can be utilized such that one can transfer the
24#
發(fā)表于 2025-3-25 19:31:57 | 只看該作者
Genetic Interpretation System for Screening Monogenic Disorders Carrierse rural areas, the monogenic disorder ratio is much more higher because of the consanguineous marriages. According to the OMIM database, there are currently more than 8,000 single-gene diseases identified. Although monogenic diseases are rare, the overall incidence is nearly 1/100. Among various bir
25#
發(fā)表于 2025-3-25 20:42:25 | 只看該作者
26#
發(fā)表于 2025-3-26 03:44:21 | 只看該作者
Vertical Integration in the Electricity Supply Industry: Competition and Investment Issuesetailer (e. g. oil companies and petrol stations) or a manufacturing company and a supplies of parts (e. g. car companies and their suppliers), have always been of interest to competition authorities. The potential for anticompetitive behavior following vertical mergers is, however, deemed to be les
27#
發(fā)表于 2025-3-26 05:16:33 | 只看該作者
28#
發(fā)表于 2025-3-26 10:44:31 | 只看該作者
29#
發(fā)表于 2025-3-26 15:31:03 | 只看該作者
Weiche Erfolgsfaktoren und ihre Me?barkeitschen den Hierarchieebenen. Das Ergebnis der Analyse ist zum einen das sog. BIQ-Portfolio, das den Vergleich und die Einordnung des Entwicklungsstands einer Organisation erlaubt; sie liefert ferner St?rken-/Schw?chenprofile, aus denen detaillierte Ansatzpunkte zur Weiterentwicklung gewonnen werden k?nnen.
30#
發(fā)表于 2025-3-26 18:58:53 | 只看該作者
Melina Heinemann,Karina Ratiner,Eran Elinavels (LLMs) with multi-billion parameters (GPT-Neo, and GPT-J) with the hierarchical framework of MESc and compare them with their standalone performance on legal texts. We also study their intra-domain(legal) transfer learning capability and the impact of combining embeddings from their last layers
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