keyword stringlengths 4 52 | weight float64 0 1 | embedding stringlengths 15.8k 16k |
|---|---|---|
graph neural network | 1 | [0.08722702413797379, 0.8745535016059875, -0.3408846855163574, -0.0734473466873169, 0.6662229299545288, 0.009556388482451439, 0.4339209198951721, 0.36693018674850464, 0.17679241299629211, 0.5929588079452515, -0.23569293320178986, -1.1070489883422852, -0.12898148596286774, -1.7027523517608643, -0.7153154611587524, -0.11... |
graph neuralnetworks | 1 | [-0.11062880605459213, 0.9918195605278015, -0.2675487995147705, -0.16344797611236572, 0.5400116443634033, -0.08009643107652664, 0.4921877682209015, 0.3808423578739166, 0.10751718282699585, 0.628934919834137, -0.09424147009849548, -1.1814173460006714, -0.43151986598968506, -1.5148731470108032, -0.7565627694129944, -0.25... |
graph neural networkand | 1 | [0.1356668621301651, 0.8163174986839294, -0.5980242490768433, -0.025542443618178368, 0.5405692458152771, -0.08175227046012878, 0.8464369177818298, 0.43127188086509705, 0.20755882561206818, 0.7934999465942383, -0.22618865966796875, -1.1122263669967651, -0.25321969389915466, -1.7150424718856812, -0.7455411553382874, -0.2... |
graph neural networks based | 1 | [-0.10963953286409378, 0.6333564519882202, -0.41679105162620544, -0.1791401356458664, 0.44391801953315735, 0.16998885571956635, 0.39147520065307617, 0.2665574550628662, 0.22526197135448456, 0.6387982964515686, -0.1389331966638565, -1.081945776939392, -0.662570059299469, -1.7029176950454712, -0.32424113154411316, -0.004... |
graph neural network based | 1 | [-0.029546916484832764, 0.5460565686225891, -0.3735005855560303, -0.11402704566717148, 0.5150271058082581, 0.12608909606933594, 0.2861242890357971, 0.39855721592903137, 0.1951504945755005, 0.647004246711731, -0.09686524420976639, -1.1619526147842407, -0.5512087941169739, -1.7162861824035645, -0.19199423491954803, 0.064... |
neural network | 0.990941 | [-0.02977747470140457, 0.10993075370788574, -0.21777033805847168, -0.6170248985290527, -0.043871812522411346, -0.25199347734451294, 0.35431593656539917, -0.40190786123275757, -0.22014766931533813, 1.1782270669937134, -0.1477624475955963, -0.8176015615463257, -0.21602720022201538, -1.156180739402771, -0.4933942556381225... |
neural network network | 0.990941 | [-0.08472207933664322, 0.15449193120002747, -0.35530537366867065, -0.6574016809463501, -0.019541457295417786, 0.002848076866939664, 0.3572206199169159, -0.4247837960720062, -0.27963730692863464, 1.0127118825912476, -0.16745243966579437, -0.704155683517456, -0.3686750531196594, -1.1254297494888306, -0.40567654371261597,... |
neural network denn | 0.990941 | [0.13430626690387726, 0.08677495270967484, -0.06186086684465408, -0.876826286315918, 0.23916563391685486, -0.007065105251967907, 0.23857584595680237, -0.5607066750526428, -0.518011748790741, 1.1325613260269165, -0.4046156406402588, -0.8065201044082642, -0.1971019208431244, -1.0135905742645264, -0.42981839179992676, -0.... |
non local neural network | 0.990941 | [-0.07845621556043625, -0.13262143731117249, 0.025946689769625664, -0.543731153011322, -0.6083153486251831, -0.3707612454891205, 0.6301832795143127, -0.4701120853424072, -0.14124125242233276, 1.0680018663406372, 0.3229394853115082, -0.30585119128227234, 0.10859904438257217, -1.001286506652832, -0.7751753926277161, -1.1... |
neural learning network | 0.990941 | [-0.057278912514448166, 0.23393464088439941, -0.41464224457740784, -0.6208873987197876, -0.061475496739149094, -0.15842077136039734, 0.47897839546203613, -0.3157622814178467, -0.3063065707683563, 1.2191016674041748, -0.230136439204216, -0.7773691415786743, -0.2551148533821106, -1.2265806198120117, -0.49296465516090393,... |
deeplearning model | 0.989322 | [-1.0290472507476807, 0.31321725249290466, -0.3136841654777527, -0.3311678171157837, -0.19166487455368042, -0.5466514825820923, 0.9878517985343933, -0.5753479599952698, -0.5503822565078735, 0.5756950378417969, 0.13715149462223053, -0.7232561111450195, 0.36430031061172485, -1.217313528060913, -0.7361792922019958, -0.668... |
learning deep model | 0.989322 | [-0.7634826898574829, 0.13746805489063263, -0.14113327860832214, -0.46947646141052246, -0.34734636545181274, -0.17303578555583954, 0.420731782913208, -0.4890424311161041, -0.3811579644680023, 0.45604318380355835, 0.14262089133262634, -0.3791183829307556, 0.3052026629447937, -1.1778945922851562, -0.6743001937866211, -0.... |
AI Dictionary Dataset
Welcome to the AI Dictionary dataset on HuggingFace. This dataset is a comprehensive tool comprised of 16,665 unique key phrases that describe the whole domain of Artificial Intelligence (AI). It serves both the research community and industry domains, aiding in the identification of radical innovations and uncovering applications of AI in new domains.
This dataset is the result of the research paper "The AI Dictionary: The Foundation for a Text-Based Tool to Identify and Measure Technology Innovation". The paper explores the rapidly evolving landscape of AI as a General Purpose Technology and its dual role in driving and sustaining innovation across various domains.
The AI Dictionary is designed to measure technological innovation using text-based methods. We hope to establish a foundational methodology for a new and innovation measurement tool.
Related Repository
The process of creating and validating the AI Dictionary is detailed in a series of Jupyter notebooks and Python scripts in the related GitHub repository. You can access the repository at the following link:
AI Dictionary GitHub Repository
Please refer to the repository for a deeper understanding of the methodology and the process behind the creation of the AI Dictionary.
Usage
You can use this dataset for a variety of AI and Machine Learning tasks such as text classification, named entity recognition, and more. The dataset can also be used for research purposes to identify and measure technological innovation in the field of AI.
License
This dataset is released under the MIT License.
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