Results 71 to 80 of about 241,430 (313)
CRF Autoencoder for Unsupervised Dependency Parsing
, 2017 Unsupervised dependency parsing, which tries to discover linguistic dependency structures from unannotated data, is a very challenging task. Almost all previous work on this task focuses on learning generative models.
Cai, Jiong, Jiang, Yong, Tu, Kewei
core +1 more source
Improving the level of training of IT-specialists based on analysis of labor market requirements
Открытое образование (Москва), 2019 Purpose of the study. The aim of the research is to develop new approaches of improvement the level of training of IT-specialists with knowledge and skills that are in demand on the labor market.
A. V. Gavrilov +2 more
doaj +1 more source
Deep Learning‐Assisted Design of Mechanical Metamaterials
Advanced Intelligent Discovery, EarlyView.This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong +5 more
wiley +1 more source
TuLiPA : towards a multi-formalism parsing environment for grammar engineering [PDF]
, 2008 In this paper, we present an open-source parsing environment (Tübingen Linguistic Parsing Architecture, TuLiPA) which uses Range Concatenation Grammar (RCG) as a pivot formalism, thus opening the way to the parsing of several mildly context-sensitive ...
Dellert, Johannes +5 more
core +4 more sources
Journal of Language Modelling, 2019 A recently proposed balanced-bracket encoding (Yli-Jyrä and GómezRodríguez 2017) has given us a way to embed all noncrossing dependency graphs into the string space and to formulate their exact arcfactored inference problem (Kuhlmann and Johnsson 2015 ...
Anssi Mikael Yli-Jyrä
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GINet: Graph Interaction Network for Scene Parsing [PDF]
, 2020 Tianyi Wu +6 more
openalex +1 more source
Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook
Advanced Intelligent Discovery, EarlyView.Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang +4 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Chat computational fluid dynamics (CFD) introduces an large language model (LLM)‐driven agent that automates OpenFOAM simulations end‐to‐end, attaining 82.1% execution success and 68.12% physical fidelity across 315 benchmarks—far surpassing prior systems.
E Fan +8 more
wiley +1 more source
Polyglot Semantic Parsing in APIs
, 2018 Traditional approaches to semantic parsing (SP) work by training individual models for each available parallel dataset of text-meaning pairs. In this paper, we explore the idea of polyglot semantic translation, or learning semantic parsing models that ...
Berant, Jonathan +2 more
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Theorietage der Gesellschaft für Informatik in Speyer 2015—Special Issue
Algorithms, 2016 We briefly report on the national workshops on Formal Languages and Automata Theory as well as on Algorithms and Complexity Theory held in early Autumn, 2015.
Henning Fernau
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