keyword stringlengths 4 52 | weight float64 0 1 | embedding stringlengths 15.8k 16k |
|---|---|---|
graph convolutional network based | 0.758025 | [-0.28624317049980164, 0.622128427028656, -0.14166662096977234, -0.11971931904554367, 0.4181678295135498, -0.08375760912895203, 0.3134719431400299, 0.6273069977760315, 0.24743257462978363, 0.33770284056663513, 0.22405235469341278, -1.2245692014694214, -0.35155269503593445, -1.6814926862716675, -0.5000748038291931, -0.0... |
graph convolutional network | 0.758025 | [-0.06163701415061951, 0.9347411394119263, -0.1947157382965088, 0.03995194286108017, 0.5509536862373352, -0.21845367550849915, 0.5197535753250122, 0.6148937940597534, 0.32581251859664917, 0.30659064650535583, 0.15068340301513672, -1.1611665487289429, 0.20278982818126678, -1.6539818048477173, -0.8976256251335144, -0.330... |
deep learning technique | 0.755447 | [-0.7029954195022583, -0.4444587230682373, -0.11241640895605087, 0.050864797085523605, -0.17282769083976746, -0.5690267086029053, 0.6622732281684875, -0.2462683916091919, -0.35011419653892517, 0.8641656637191772, -0.06841881573200226, -0.8992595672607422, 0.4330368936061859, -1.2635910511016846, -0.21483977138996124, -... |
deep learning based technique | 0.755447 | [-0.788273274898529, -0.39721521735191345, -0.12585358321666718, -0.007792862597852945, -0.586738646030426, -0.6057600378990173, 0.5790790915489197, 0.0561167411506176, -0.5336489081382751, 0.684319019317627, -0.034847442060709, -0.8237156271934509, 0.1913156360387802, -1.244388461112976, -0.045979976654052734, -0.8903... |
labeled data | 0.753548 | [-0.2854417562484741, 0.40599924325942993, -1.2477562427520752, -1.63383948802948, -0.9750158786773682, -0.9620333909988403, 0.3203665018081665, 0.34871670603752136, -0.6603615283966064, 1.5069044828414917, 0.390969455242157, -0.7987555265426636, 0.04037756472826004, -0.6620107889175415, -0.3636634647846222, -0.7982330... |
supervised labeled data | 0.753548 | [-0.00147133634891361, 0.6934012174606323, -1.2631747722625732, -1.6450471878051758, -1.155071496963501, -0.8465649485588074, 0.19906878471374512, 0.45354384183883667, -0.4223254323005676, 1.5014657974243164, 0.32366809248924255, -0.6081112027168274, 0.16847939789295197, -0.7801533937454224, -0.6271801590919495, -0.846... |
point cloud description | 0.752982 | [-0.39281895756721497, -0.4323682188987732, 0.15773531794548035, -0.38122251629829407, -0.1394459307193756, -0.6387468576431274, 0.3647284209728241, -0.04767175391316414, 0.40172386169433594, 0.21959197521209717, -1.026794195175171, -0.4980355203151703, 0.3663948178291321, -0.9877071380615234, -0.8093057870864868, -0.6... |
point cloud region | 0.752982 | [-0.4001694321632385, -0.29052069783210754, 0.26461511850357056, -0.16676586866378784, -0.22687546908855438, -0.7855428457260132, 0.2472793608903885, -0.26118704676628113, -0.016128795221447945, 0.24502579867839813, -1.2942203283309937, -0.503423273563385, 0.6662686467170715, -1.0292127132415771, -1.0111712217330933, -... |
pointcloud | 0.752982 | [-0.5642052888870239, -0.40355730056762695, 0.6277588605880737, -0.20544159412384033, -0.17827318608760834, -0.7074372172355652, 0.4191232919692993, -0.040654849261045456, 0.2768482267856598, 0.20694798231124878, -1.0949432849884033, -0.5725470781326294, 0.35793355107307434, -1.1335301399230957, -0.7926024198532104, -0... |
point cloud | 0.752982 | [-0.4256608486175537, -0.39695680141448975, 0.31142038106918335, -0.26613080501556396, -0.1987488567829132, -0.6818041205406189, 0.24859490990638733, -0.17634238302707672, 0.16928434371948242, 0.2712261378765106, -1.3969916105270386, -0.5962041616439819, 0.49646589159965515, -1.1497857570648193, -1.0046254396438599, -0... |
data augmentation technique | 0.751781 | [0.2874191403388977, 0.31338948011398315, -0.21880173683166504, -0.6889923214912415, -0.24104276299476624, -0.015334862284362316, 0.6648456454277039, 0.640384316444397, -0.7712979316711426, 1.0538718700408936, 0.14185862243175507, -0.7341160774230957, 0.5986196398735046, -0.9214202761650085, -0.14618483185768127, -0.87... |
joint convolutional network | 0.751151 | [-0.8257397413253784, -0.014476153068244457, -0.07367898523807526, -0.23962442576885223, -0.7393426895141602, -0.3313392698764801, 0.6058712005615234, -0.21841323375701904, -0.40915927290916443, 0.6609668731689453, 0.30390605330467224, -0.43455344438552856, 0.46762189269065857, -0.8948095440864563, -0.7997451424598694,... |
fully convolutional neural network | 0.751151 | [-0.32976868748664856, 0.10169436782598495, -0.037097763270139694, -0.36517736315727234, -0.37624430656433105, -0.29344502091407776, 0.5373440384864807, -0.5473845601081848, -0.5409329533576965, 0.5769129395484924, 0.2920701503753662, -0.5898646116256714, 0.6945852637290955, -0.9598541855812073, -0.9147069454193115, -0... |
fully convolutional network | 0.751151 | [-0.35740888118743896, 0.12521137297153473, 0.012208032421767712, -0.33937615156173706, -0.26327386498451233, -0.4931025505065918, 0.5055826306343079, -0.4892371594905853, -0.6301491856575012, 0.610319197177887, 0.210012748837471, -0.6063822507858276, 0.5673748254776001, -0.9510604739189148, -0.7901448011398315, -0.694... |
learning algorithm | 0.746075 | [-0.15131911635398865, 0.15354309976100922, -0.6406738758087158, -0.6757664680480957, -0.18050582706928253, -0.429737389087677, 0.13401153683662415, -0.2051943987607956, -0.40527111291885376, 1.240816593170166, -0.42855900526046753, -0.7592020034790039, -0.35336825251579285, -1.3415098190307617, -0.2760445475578308, -0... |
learning learning algorithm | 0.746075 | [-0.14351823925971985, 0.1838580071926117, -0.6766007542610168, -0.8524928092956543, -0.32362234592437744, -0.22056572139263153, 0.18860504031181335, 0.05793447047472, -0.23631355166435242, 1.3436541557312012, -0.3957781195640564, -0.6203694343566895, -0.3403231203556061, -1.3792853355407715, -0.28257519006729126, -0.8... |
language processing task | 0.745114 | [-0.23556821048259735, -0.18068304657936096, 0.6807438135147095, -0.6248365640640259, -0.7969446778297424, -0.7034405469894409, 0.5584850907325745, 0.7567375898361206, -1.1943376064300537, -0.01615532673895359, 0.5360649824142456, -1.475693941116333, 0.310902863740921, -1.000468373298645, -0.06953521072864532, -0.16471... |
stochastic co block model | 0.745104 | [-0.04415753111243248, -0.2888188660144806, -0.6342543959617615, -0.2126738280057907, -0.6713762283325195, -0.10417293757200241, 0.46839118003845215, -0.6128703951835632, 0.3487241268157959, 0.8351508975028992, 0.5561760067939758, -0.9159102439880371, 0.03780919313430786, -0.4675808250904083, -0.9453452229499817, -1.16... |
stochastic block model | 0.745104 | [-0.05300498008728027, -0.40451914072036743, -0.6187065243721008, -0.30883878469467163, -0.5683485269546509, -0.3677281141281128, 0.3670467436313629, -0.4894181787967682, 0.17535680532455444, 0.7257803678512573, 0.5991452932357788, -0.940861701965332, 0.23263315856456757, -0.6083464622497559, -1.0402696132659912, -1.31... |
artificial intelligence | 0.743002 | [0.20731420814990997, 0.45638129115104675, -0.46743661165237427, -0.05331207811832428, 0.22798888385295868, -0.6135532259941101, 0.7753102779388428, -0.3392687439918518, -0.4626792073249817, 1.4820966720581055, -0.2809993326663971, -1.287609338760376, -0.5197232961654663, -1.3903799057006836, -0.24086514115333557, -0.5... |
data augmentation method | 0.741094 | [0.21202664077281952, 0.37477022409439087, -0.20635950565338135, -0.7267473340034485, -0.4166620373725891, 0.04582666605710983, 0.669620931148529, 0.5232324004173279, -0.6303499937057495, 1.0191917419433594, 0.17341198027133942, -0.6016668677330017, 0.6255861520767212, -1.0112214088439941, -0.25030240416526794, -0.9449... |
data augmentation technology | 0.741094 | [0.20155711472034454, 0.3878008723258972, -0.21558396518230438, -0.7005488276481628, -0.25216320157051086, 0.2493773251771927, 0.4939795434474945, 0.3928242027759552, -0.6234696507453918, 0.8684384226799011, 0.17754456400871277, -0.7764402627944946, 0.4732770025730133, -0.7794546484947205, -0.31037914752960205, -0.8098... |
important task | 0.739199 | [0.05997728556394577, 0.3600735664367676, -0.12027455121278763, -0.5434335470199585, -1.233370304107666, -0.4411713480949402, 0.046976640820503235, -0.008429892361164093, -1.1425528526306152, 1.6600130796432495, 0.32346266508102417, 0.10996748507022858, -0.5113039016723633, 0.24997001886367798, -0.2594760060310364, -0.... |
input image | 0.73901 | [1.3300435543060303, 0.17395006120204926, 0.05614462122321129, -0.5540156960487366, -0.01789267733693123, -0.3907631039619446, 0.14170485734939575, -0.8234235644340515, -0.8407077789306641, 0.4998916685581207, -0.3037855923175812, -0.884024441242218, 0.5744407773017883, -1.0905568599700928, -0.5152394771575928, -1.1921... |
language model plm | 0.738163 | [-0.015221166424453259, 0.4751838147640228, -0.8475484848022461, 0.008844886906445026, -0.5340259671211243, -0.17010144889354706, 1.1725926399230957, 0.014402723871171474, -0.19103169441223145, 0.5863131880760193, 0.6420936584472656, -1.0567231178283691, 0.21231453120708466, -0.7193676829338074, -0.5341352820396423, 0.... |
language model | 0.738163 | [-0.6632719039916992, 0.026715369895100594, -0.7022594809532166, -0.2245405614376068, -0.3272770941257477, -0.29640495777130127, 1.1766352653503418, 0.0020149294286966324, -0.6506396532058716, 0.4577265977859497, 0.8159748315811157, -1.3825544118881226, 0.1584729105234146, -0.9152662754058838, -0.2834405303001404, 0.29... |
language model superplm | 0.738163 | [-0.07860549539327621, 0.632583737373352, -0.7195539474487305, -0.21547195315361023, -0.7341857552528381, -0.24282599985599518, 1.1195944547653198, -0.2945418953895569, -0.08695147186517715, 0.7796154618263245, 0.7032341361045837, -0.9503593444824219, 0.2766759693622589, -0.5335187315940857, -0.5743095278739929, 0.2717... |
language model method | 0.738163 | [-0.6493057012557983, -0.07072894275188446, -0.7917701005935669, -0.18045732378959656, -0.614417552947998, -0.24123553931713104, 1.2748075723648071, -0.009252654388546944, -0.6060309410095215, 0.6498360633850098, 0.7001872658729553, -1.2888026237487793, 0.17963619530200958, -0.9118852615356445, -0.19018858671188354, 0.... |
adversarial network | 0.737601 | [0.0713704526424408, 0.620109498500824, 0.4933013916015625, 0.037132810801267624, -0.360562801361084, -0.9475104212760925, 0.38282331824302673, -0.9283106923103333, -0.004992128349840641, 1.16059410572052, -0.33745694160461426, -0.6393963098526001, 0.32568103075027466, -1.1987721920013428, -1.1013858318328857, -0.97868... |
supervised adversarial network | 0.737601 | [0.15458178520202637, 0.6953859925270081, 0.35978230834007263, -0.044247403740882874, -0.6510050892829895, -0.9154375195503235, 0.41460737586021423, -0.8811221122741699, -0.14098228514194489, 1.1224806308746338, -0.3233865201473236, -0.5592339634895325, 0.4800107777118683, -1.19562566280365, -1.0113605260849, -1.110366... |
adversarial network stan | 0.737601 | [0.22326868772506714, 0.6847860217094421, 0.4859691560268402, -0.029439950361847878, -0.555391252040863, -0.8298926949501038, 0.5242617726325989, -0.9980193972587585, -0.1807471364736557, 1.2098884582519531, -0.25281354784965515, -0.5210292935371399, 0.2991524934768677, -1.1271692514419556, -1.0833477973937988, -1.1099... |
adversarial network variant | 0.737601 | [0.2640565037727356, 0.7356548309326172, 0.4574510157108307, -0.06777239590883255, -0.2975212037563324, -0.8954961895942688, 0.3769628703594208, -1.1247280836105347, -0.3413658142089844, 1.2585633993148804, -0.5644662976264954, -0.6404998898506165, 0.10305855423212051, -1.0482791662216187, -0.9051368236541748, -1.13529... |
weak adversarial network | 0.737601 | [-0.06284639984369278, 0.6174922585487366, 0.32382452487945557, 0.06240168213844299, -0.5350868105888367, -0.9999442100524902, 0.48592838644981384, -0.9598875045776367, -0.2757897675037384, 1.3589593172073364, -0.4013155996799469, -0.7093272805213928, 0.4580572843551636, -1.1875971555709839, -1.1238106489181519, -1.062... |
word embeddings feature | 0.73615 | [-0.6264604330062866, -0.5539992451667786, -0.6949318051338196, -0.3880070745944977, -0.3728141486644745, -0.16083569824695587, 0.6638635396957397, 0.5793700218200684, -1.379503607749939, 0.253621906042099, 0.9329148530960083, -0.49350041151046753, -0.42686349153518677, -1.3102750778198242, 0.0999617725610733, -0.40466... |
word embeddings space | 0.73615 | [-0.6183322668075562, -0.402242511510849, -0.5864025950431824, -0.3620818853378296, -0.17568039894104004, -0.19904205203056335, 0.6969253420829773, 0.48171406984329224, -1.3238906860351562, 0.206141397356987, 0.7838666439056396, -0.6937958002090454, -0.22761444747447968, -1.271270990371704, 0.21403999626636505, -0.3997... |
word embeddings | 0.73615 | [-0.6093682646751404, -0.2917075455188751, -0.7436428070068359, -0.35339879989624023, -0.36335358023643494, -0.16281425952911377, 0.5566048622131348, 0.596357524394989, -1.2658649682998657, 0.21530207991600037, 0.8751636743545532, -0.7185512185096741, -0.18791696429252625, -1.2687902450561523, 0.04963099583983421, -0.3... |
word embeddings represent | 0.73615 | [-0.6484118700027466, -0.12678787112236023, -0.6631096601486206, -0.1514005959033966, -0.38018256425857544, -0.05091302841901779, 0.4827772080898285, 0.4681972563266754, -1.089858889579773, 0.2865687608718872, 0.9752601385116577, -0.3618016839027405, -0.15322254598140717, -1.1942005157470703, 0.07918775826692581, -0.33... |
word embeddings learning | 0.73615 | [-0.5697444677352905, -0.2638259828090668, -0.7627736330032349, -0.40550392866134644, -0.460294634103775, -0.10868103802204132, 0.5928907990455627, 0.5731066465377808, -1.3008496761322021, 0.15438714623451233, 0.7345244884490967, -0.6503701210021973, -0.10913791507482529, -1.2071119546890259, -0.03201036900281906, -0.4... |
low dimensional word embeddings | 0.73615 | [-0.6761016845703125, -0.44923701882362366, -0.5950095653533936, -0.20814867317676544, -0.678372323513031, -0.29829534888267517, 0.7436764240264893, 0.3048231303691864, -1.3263869285583496, 0.33411601185798645, 0.7588114142417908, -0.6831744313240051, -0.02713451348245144, -1.2299240827560425, 0.04171007499098778, -0.4... |
deep convolutional artificial | 0.733315 | [-0.32299092411994934, 0.35079264640808105, 0.4840843081474304, -0.34440818428993225, 0.2797534763813019, -0.5393568277359009, 0.7640304565429688, -0.5110512971878052, -0.7209404110908508, 0.7798701524734497, -0.12204079329967499, -0.6172227263450623, 0.8262588381767273, -1.0018975734710693, -0.8385980725288391, -0.948... |
deep convolutional neural | 0.733315 | [-0.42730697989463806, 0.008054119534790516, 0.13934390246868134, -0.5284867286682129, -0.12084688991308212, -0.5034888386726379, 0.6570510864257812, -0.4669817090034485, -0.5248697996139526, 0.47734397649765015, 0.0006213121232576668, -0.653370201587677, 0.6432024836540222, -1.079512357711792, -0.9195430874824524, -0.... |
machine translation system | 0.730057 | [0.18592186272144318, 0.048928581178188324, -0.09920842945575714, -0.3049231171607971, -1.349687933921814, -1.1801025867462158, 1.1837035417556763, -0.3880769908428192, -0.601265013217926, 1.69071364402771, -0.2156858742237091, -1.564314603805542, 0.7264326810836792, -0.6563972234725952, -0.7320101261138916, 0.02088287... |
automatic translation system | 0.730057 | [0.09995412826538086, -0.06792636215686798, -0.04250504821538925, -0.425485223531723, -1.7498283386230469, -1.2791922092437744, 0.8277570605278015, -0.5520685315132141, -0.5083365440368652, 1.6811555624008179, -0.22195780277252197, -1.4174408912658691, 0.725687563419342, -0.5162402391433716, -0.8364566564559937, 0.0149... |
machine translation environment | 0.730057 | [0.21458148956298828, 0.09448904544115067, -0.022691121324896812, -0.27983558177948, -1.3207931518554688, -1.1655348539352417, 1.0899019241333008, -0.4551876485347748, -0.6714603900909424, 1.609114646911621, -0.08416607230901718, -1.5686638355255127, 0.857213020324707, -0.6513915061950684, -0.8614457249641418, 0.139411... |
text classification task | 0.726268 | [0.4567191004753113, 0.13355180621147156, -0.2950020134449005, -0.8167914152145386, -0.42979899048805237, -0.5014467239379883, 0.8571168780326843, 1.0878406763076782, -0.4860968589782715, 0.5808552503585815, 0.3587087094783783, -1.3851321935653687, 0.23168647289276123, -1.1288892030715942, -0.07568652927875519, -0.5770... |
cross lingual text classification | 0.726268 | [-0.20488786697387695, -0.3602640628814697, -0.9296666383743286, -0.773849368095398, -1.4381264448165894, -0.44369372725486755, 1.0857256650924683, 0.6120615601539612, -0.7841544151306152, 0.6280298829078674, 0.45656174421310425, -0.821866512298584, 0.39079999923706055, -0.5363304018974304, -0.5349307656288147, -0.5713... |
test time | 0.725363 | [-0.4512138366699219, -0.28950443863868713, 0.25676262378692627, -0.8882755041122437, -0.5630057454109192, -1.33596932888031, -0.1785925328731537, -0.1810893416404724, -0.6194745302200317, 1.3074314594268799, -1.0940465927124023, -1.152604103088379, -0.8428317308425903, -1.1472601890563965, -0.4164945185184479, -0.3392... |
human pose estimation | 0.72468 | [-0.31395798921585083, 0.23511075973510742, 0.1097434014081955, -0.33692604303359985, -0.6364296674728394, -0.4723823070526123, 0.11823756992816925, -0.6945384740829468, -1.059797763824463, 0.7817376852035522, 0.26114320755004883, 0.33980217576026917, 0.7668945789337158, -0.28560909628868103, -0.5642958879470825, -0.41... |
probabilistic graphical model | 0.722021 | [-0.32549789547920227, -0.09645970165729523, -0.2106652706861496, -0.8389756083488464, -0.5154431462287903, -0.6543660163879395, 0.5882080793380737, -0.020606329664587975, -0.0705539658665657, 0.8364166021347046, 0.42212677001953125, -1.062086820602417, -0.6213129162788391, -1.1625072956085205, -0.40156421065330505, -0... |
dendrograms | 0.722021 | [-0.1819310188293457, -0.2557247281074524, -0.2585729658603668, -0.6066602468490601, 0.164193794131279, -0.7832236886024475, 1.0756467580795288, 0.490387499332428, -0.14876234531402588, 0.7183582782745361, 0.45816412568092346, -1.3695920705795288, -0.7973856329917908, -1.016234278678894, -0.058949027210474014, -0.93283... |
natural language description | 0.719688 | [-0.21119213104248047, 0.4707166254520416, -0.8738569021224976, -0.6384775042533875, -1.0667259693145752, -0.7310511469841003, 1.3214305639266968, 0.5701229572296143, -0.21534569561481476, 1.3528563976287842, 0.8452337980270386, -0.4641103744506836, 0.35783419013023376, -0.798366904258728, -0.18527527153491974, -0.1779... |
human activity recognition | 0.719284 | [0.7952617406845093, -0.07070567458868027, -0.6988430619239807, -0.10777385532855988, -0.2828157842159271, -0.515650749206543, 1.0502440929412842, -0.12626376748085022, -0.619949460029602, 1.3304280042648315, 0.35819321870803833, -0.28135624527931213, 0.26987606287002563, -0.7513867616653442, -0.432034969329834, -1.272... |
human activity recognitionhar | 0.719284 | [0.49574747681617737, -0.07777232676744461, -0.6117948889732361, -0.06455457955598831, -0.458998441696167, -0.42365923523902893, 0.9268223643302917, -0.38328757882118225, -0.7485136985778809, 1.4250659942626953, 0.3022269308567047, -0.09335487335920334, 0.2718105912208557, -0.7090740203857422, -0.46897459030151367, -1.... |
ten of thousand | 0.718623 | [-0.35670575499534607, 0.012291868217289448, 0.014515651389956474, -0.1484917253255844, -0.5093823671340942, -0.8119897842407227, 0.968178927898407, -0.7571002244949341, -0.7112684845924377, 1.6772133111953735, 0.1691547930240631, -1.023467779159546, -0.06057851389050484, -1.1744037866592407, -0.4541662335395813, -0.73... |
fully connected layer | 0.718272 | [-0.48187556862831116, 0.9084353446960449, -0.2766594886779785, -0.3396897315979004, -0.07649976760149002, -0.18442383408546448, 0.28592976927757263, -0.18369585275650024, -0.7842661142349243, 0.6917901039123535, 0.06944285333156586, -0.46971243619918823, 0.5584353804588318, -1.2874281406402588, -0.8330938220024109, -0... |
layer fully connected | 0.718272 | [-0.2100815325975418, 1.1676928997039795, -0.3966725766658783, -0.6462758779525757, -0.29478758573532104, 0.005774980876594782, 0.31863144040107727, -0.09631223231554031, -0.40828561782836914, 0.8936260342597961, 0.24473492801189423, -0.30405062437057495, 0.49274516105651855, -1.1964232921600342, -1.0316197872161865, -... |
explainable artificial intelligent | 0.71797 | [0.28665709495544434, 1.958647608757019, -0.28669604659080505, -0.6567366719245911, 0.5194785594940186, -0.3531343638896942, 0.2517889738082886, -0.7251319885253906, 0.3963318169116974, 1.5430898666381836, -0.41635963320732117, -1.262057900428772, -0.9611022472381592, -0.8788689970970154, -0.9261398315429688, -0.311956... |
explainable artificial intelligence | 0.71797 | [0.2973919212818146, 1.9648466110229492, -0.16571997106075287, -0.5309980511665344, 0.6063165068626404, -0.3654094636440277, 0.35186901688575745, -0.7336504459381104, 0.4302230775356293, 1.4931511878967285, -0.3759118616580963, -1.1733492612838745, -0.9482018351554871, -0.8905513286590576, -0.9725365042686462, -0.34125... |
explainable artificial intelligencemachine | 0.71797 | [-0.0013665386941283941, 2.021034002304077, -0.5611898899078369, -0.45714905858039856, 0.4634323716163635, -0.35691341757774353, 0.7368579506874084, -0.8119651079177856, 0.3798956274986267, 1.679788589477539, -0.4333081543445587, -1.2468700408935547, -0.7127600908279419, -0.5792710185050964, -0.8892428278923035, -0.441... |
human explainable artificial intelligence | 0.71797 | [0.2516229748725891, 1.9024622440338135, 0.07400926202535629, -0.46456319093704224, 0.48495927453041077, -0.40501949191093445, 0.31469959020614624, -0.5644220113754272, 0.20929230749607086, 1.2947723865509033, -0.3906962275505066, -1.175396203994751, -0.7344774007797241, -1.0272780656814575, -0.8792290687561035, -0.153... |
autonomous driving | 0.717935 | [-0.34057891368865967, 0.3866041898727417, -0.1440834403038025, -1.0328363180160522, -0.046827174723148346, -0.6582756638526917, 0.7942872047424316, -0.9640318155288696, -0.5278749465942383, 0.9285421371459961, -0.7821927666664124, -0.9083589315414429, 0.39647892117500305, -0.8987636566162109, -1.081425666809082, -1.02... |
autonomous driving general | 0.717935 | [-0.47749438881874084, 0.3712669610977173, -0.1297570765018463, -0.8037039637565613, -0.15464341640472412, -0.6351497173309326, 0.672015368938446, -0.9322437047958374, -0.24258656799793243, 0.7799041867256165, -0.8663318753242493, -0.7139180898666382, 0.5676583051681519, -0.8768938183784485, -1.0031877756118774, -1.293... |
autonomous driving mission | 0.717935 | [-0.40026435256004333, 0.3349963128566742, -0.032409630715847015, -0.8883775472640991, -0.22696714103221893, -0.6282088756561279, 0.7020643949508667, -0.8964813351631165, -0.5929980874061584, 1.012784481048584, -0.7981148362159729, -0.9599422216415405, 0.042443808168172836, -0.8197928667068481, -0.8981795310974121, -1.... |
autonomously driving | 0.717935 | [-0.3440447449684143, 0.4581829011440277, -0.13892708718776703, -0.8302305936813354, -0.12610861659049988, -0.8101352453231812, 0.6942368149757385, -1.0995805263519287, -0.4705876410007477, 1.0505011081695557, -0.5411739349365234, -0.9264686703681946, 0.6014893054962158, -0.8094877004623413, -1.0819423198699951, -0.980... |
offline reinforcement learning | 0.717474 | [-0.6586729288101196, 0.2804204523563385, -0.22571513056755066, -0.1252206414937973, -0.7250112295150757, -0.7256209254264832, 0.1958657056093216, -0.20907306671142578, -0.967426598072052, 1.6495879888534546, -0.7901618480682373, -0.8934161067008972, -0.18706205487251282, -0.893485426902771, -1.2056710720062256, -0.824... |
offline q learning | 0.717474 | [-0.8420149087905884, 0.3223852515220642, -0.40647658705711365, -0.006125482730567455, -1.120729684829712, -0.5708636045455933, -0.21468372642993927, -0.21709942817687988, -0.7888255715370178, 1.6230100393295288, -0.41186952590942383, -0.6480484008789062, -0.33897167444229126, -0.8252528309822083, -0.8965044021606445, ... |
convolutional neural | 0.716641 | [-0.36754438281059265, 0.005244076251983643, -0.043486565351486206, -0.5356939435005188, -0.2994288206100464, -0.4105386734008789, 0.6994113922119141, -0.39064985513687134, -0.488262802362442, 0.5623714923858643, 0.2473185807466507, -0.6864684820175171, 0.4416956305503845, -1.0831886529922485, -0.8024778962135315, -0.6... |
multi stream convolutional neural | 0.716641 | [0.026668483391404152, -0.6027568578720093, 0.3174181878566742, -0.34164419770240784, -0.8562285304069519, -0.010802916251122952, 0.78645259141922, 0.43357494473457336, -0.7435823082923889, 0.4693574011325836, 0.41817548871040344, -0.24900811910629272, 0.42768430709838867, -1.3230476379394531, -0.9856193661689758, -0.4... |
convolutional learning | 0.716641 | [-0.39370518922805786, 0.09664896130561829, -0.14680004119873047, -0.43228814005851746, -0.44698309898376465, -0.417061448097229, 0.8703982830047607, -0.39189666509628296, -0.44646260142326355, 0.5000560283660889, 0.3710375428199768, -0.623980700969696, 0.29413390159606934, -1.0137183666229248, -0.723393440246582, -0.6... |
convolutional neuralnetworks | 0.716641 | [-0.3997374475002289, 0.2952210605144501, -0.15483534336090088, -0.2984890341758728, 0.0020045239944010973, -0.2794815003871918, 0.6937990784645081, -0.43051162362098694, -0.2883278429508209, 0.5136318206787109, 0.1447969526052475, -0.9757040143013, 0.1159670278429985, -0.9917643070220947, -0.707908570766449, -0.499413... |
hundred of thousand | 0.715467 | [-0.4475506842136383, -0.12874147295951843, 0.005694487597793341, -0.22432191669940948, -0.607387900352478, -0.8374444246292114, 0.9066945314407349, -0.6265177726745605, -0.42693835496902466, 1.6408599615097046, 0.16270677745342255, -0.8056216239929199, -0.004510792903602123, -1.1614547967910767, -0.5081962943077087, -... |
gaussian process regression | 0.714767 | [-0.39082038402557373, -0.06781302392482758, 0.5181370377540588, -0.34754952788352966, -0.9768925905227661, -0.12734614312648773, 0.8858965039253235, -0.4130121171474457, -0.5673829317092896, 0.6127588152885437, -0.10490057617425919, -0.5134507417678833, 0.25415897369384766, -0.40888285636901855, -0.44235047698020935, ... |
based deep network | 0.711785 | [-0.8187773823738098, 0.6100749969482422, 0.3432532846927643, -0.8628911972045898, 0.08982859551906586, -0.20913676917552948, 0.07821815460920334, -0.5225207209587097, -0.7379757165908813, 0.8911186456680298, 0.010523159056901932, -0.6159247159957886, -0.10148868709802628, -1.368776559829712, -0.5039795637130737, -0.75... |
original deep network | 0.711785 | [-0.33612900972366333, 0.3845471441745758, 0.14109712839126587, -0.27667349576950073, 0.3723980486392975, -0.4824327826499939, 0.16167005896568298, -0.730871319770813, -0.4744490683078766, 0.835684597492218, -0.1887330859899521, -0.6548737287521362, 0.2674042582511902, -1.193281650543213, -0.7033222317695618, -0.830729... |
deep network | 0.711785 | [-0.5936920642852783, 0.1966959834098816, 0.12120569497346878, -0.39984092116355896, 0.3353462219238281, -0.6685409545898438, 0.4108867645263672, -0.5362815260887146, -0.5014079809188843, 0.7696742415428162, 0.01041109673678875, -0.698797881603241, 0.05377960205078125, -1.2705129384994507, -0.5311537981033325, -0.73107... |
generative model | 0.710964 | [-0.22734138369560242, 0.4360039234161377, 0.24165700376033783, -0.5399031639099121, -0.5470910668373108, -0.3687130808830261, 1.0745301246643066, -0.931352972984314, 0.5424115061759949, 0.7726858258247375, 0.5504826903343201, -0.4107500910758972, -0.015464911237359047, -1.4596617221832275, -0.8248482942581177, -1.1253... |
combining generative model | 0.710964 | [-0.4837270677089691, 0.3853272795677185, 0.23642030358314514, -0.2740517556667328, -0.7257603406906128, -0.3861173093318939, 1.0655632019042969, -0.9298058748245239, 0.6817872524261475, 0.7447065711021423, 0.5180767774581909, -0.21296977996826172, 0.19442203640937805, -1.4631328582763672, -0.7243856191635132, -0.99761... |
machine learning framework | 0.710555 | [-0.07759741693735123, -0.13053569197654724, -0.12676329910755157, -0.3454296588897705, -0.4955138564109802, -0.8658196330070496, 0.8776152729988098, -0.3157185912132263, -0.04290611669421196, 0.8360778093338013, 0.09101153910160065, -0.9002792239189148, -0.33354097604751587, -1.3501428365707397, -0.46450453996658325, ... |
inverse reinforcement learning | 0.709253 | [-0.2832077443599701, 0.7654593586921692, -0.2844277024269104, -0.11245592683553696, -0.419086754322052, -0.2984580993652344, 0.3310915231704712, -0.7661460041999817, -0.7328296899795532, 1.5586702823638916, -0.396124929189682, -0.7482885122299194, -0.6240371465682983, -0.9243162274360657, -1.323489785194397, -0.718880... |
inverse inverse reinforcement learning | 0.709253 | [-0.2269434928894043, 0.9205736517906189, -0.312611848115921, -0.05159910023212433, -0.5154995918273926, -0.1793380230665207, 0.23211796581745148, -1.003574252128601, -0.7114558815956116, 1.612627625465393, -0.33316871523857117, -0.6377683281898499, -0.5947812795639038, -0.888120174407959, -1.465956687927246, -0.670715... |
diffusionstr diffusion model | 0.708851 | [-0.45489606261253357, -0.14004682004451752, -0.6529170274734497, 0.04065033420920372, 0.5432743430137634, -0.33578017354011536, 1.0646547079086304, 0.04951002076268196, -0.7402429580688477, 0.11895843595266342, 0.4254537522792816, -1.1380951404571533, -0.6140722632408142, -1.0974832773208618, 0.21880066394805908, -0.9... |
blended diffusion | 0.708851 | [-0.46079564094543457, 0.29830795526504517, -1.7075819969177246, -0.28870975971221924, -0.7990801334381104, -0.616732120513916, 0.6242815256118774, 0.36780911684036255, -0.5995185375213623, 0.7324549555778503, 0.7367480397224426, -1.0954687595367432, -0.6324227452278137, -0.918450653553009, -0.6845017671585083, -0.6983... |
text driven diffusion model | 0.708851 | [-0.0388985201716423, 0.15081392228603363, -0.7208514213562012, -0.4208873212337494, -0.009645885787904263, -0.2707132399082184, 1.2712541818618774, 0.26800236105918884, -0.6533529758453369, 0.6048271656036377, 0.8185921311378479, -1.123294711112976, -0.45371437072753906, -1.2089773416519165, -0.04463369771838188, -0.5... |
diffusion model | 0.708851 | [-0.5674161911010742, -0.07388750463724136, -0.9896302819252014, -0.0876932144165039, 0.532163143157959, -0.483862042427063, 0.8549522757530212, 0.17552047967910767, -0.7963379621505737, 0.15228620171546936, 0.3172386884689331, -1.3276275396347046, -0.5989770889282227, -1.2846736907958984, -0.15908999741077423, -0.4699... |
diffusion model advancement | 0.708851 | [-0.4825575351715088, -0.3248249292373657, -1.1874127388000488, -0.009444382973015308, 0.26819247007369995, -0.306976318359375, 0.7587604522705078, 0.3155114948749542, -0.9699678421020508, 0.11290011554956436, 0.2661798596382141, -1.373786211013794, -0.6266067028045654, -1.003258466720581, -0.05344404652714729, -0.4283... |
diffusion model meet | 0.708851 | [-0.5612494945526123, -0.14482082426548004, -0.9201682806015015, -0.16077719628810883, 0.4521530270576477, -0.5199131369590759, 0.8652701377868652, -0.026950335130095482, -0.9045508503913879, 0.11190710216760635, 0.3127809166908264, -1.2923943996429443, -0.6739994287490845, -1.1460886001586914, -0.0012728690635412931, ... |
diffusion model led | 0.708851 | [-0.3349936008453369, 0.08236711472272873, -0.800389289855957, 0.08797906339168549, 0.45550256967544556, -0.5852832794189453, 0.7174656987190247, -0.2118772715330124, -0.9038841128349304, 0.3765915036201477, 0.4584634304046631, -1.471570611000061, -0.7513592839241028, -0.9747630953788757, -0.12432588636875153, -0.53439... |
text guided diffusion model | 0.708851 | [0.06155663728713989, 0.26860740780830383, -0.8896355032920837, -0.24231122434139252, -0.014649822376668453, -0.176039457321167, 1.181622862815857, 0.4429471790790558, -0.5975863933563232, 0.5478213429450989, 0.8405568599700928, -1.3142266273498535, -0.5096319317817688, -1.2373346090316772, 0.027695901691913605, -0.590... |
apply diffusion model | 0.708851 | [-0.47588402032852173, -0.11071576178073883, -0.7070326805114746, -0.02076728455722332, 0.7647767066955566, -0.71222984790802, 0.9628852009773254, 0.2720645070075989, -0.863855242729187, 0.3080151081085205, 0.38277435302734375, -1.1670008897781372, -0.4166557788848877, -1.459363341331482, -0.019622446969151497, -0.2382... |
model parameter input | 0.70872 | [-0.3691594898700714, -0.229842871427536, -0.56043541431427, -0.6034581661224365, -0.288279265165329, -0.7435835599899292, 0.5467139482498169, -0.5173938274383545, -0.24542322754859924, 0.5672107934951782, -0.49935102462768555, -1.0580052137374878, -0.4964037835597992, -0.6575947403907776, -0.3124779760837555, -0.82224... |
model parameter | 0.70872 | [-0.5280344486236572, -0.24282708764076233, -0.6698608994483948, -0.4496689438819885, -0.33487987518310547, -0.80240797996521, 0.4590466022491455, -0.6063947081565857, -0.46826958656311035, 0.48984622955322266, -0.43578827381134033, -0.8309924602508545, -0.5427201986312866, -0.4874042272567749, -0.33940452337265015, -0... |
model perturbation amp | 0.70872 | [0.28624606132507324, 0.24257557094097137, 0.2269304245710373, 0.05398456007242203, -0.4800242781639099, -0.7269178032875061, 0.38439005613327026, -0.4857045114040375, -0.20048296451568604, 1.0483477115631104, -0.21889862418174744, -0.8643717765808105, -0.010386545211076736, -0.05912236124277115, -0.6534743309020996, -... |
time series forecasting feature | 0.708556 | [-0.451912522315979, -0.9184585213661194, 0.29694631695747375, -0.7687563896179199, 0.2585279047489166, -0.4918060302734375, 0.850834846496582, -0.111081562936306, 0.040995512157678604, 0.6451239585876465, 0.15698634088039398, -0.9709535241127014, -0.5372553467750549, -0.9913285374641418, -0.07118875533342361, -0.76712... |
time series forecasting problem | 0.708556 | [-0.017001157626509666, -0.8257916569709778, 0.23050855100154877, -0.7982482314109802, -0.027579424902796745, -0.5621740818023682, 0.661278486251831, -0.04107321798801422, 0.06256420165300369, 0.7863304018974304, -0.06775224208831787, -0.9777761101722717, -0.39821097254753113, -0.835700511932373, -0.21885474026203156, ... |
time series forecasting research | 0.708556 | [-0.4903595447540283, -1.1359955072402954, -0.011188027448952198, -0.615163266658783, 0.354819655418396, -0.43944093585014343, 0.7197766900062561, -0.019786613062024117, -0.13581420481204987, 0.3981436789035797, 0.022625232115387917, -0.9940016865730286, -0.32904016971588135, -0.94175785779953, -0.005836140364408493, -... |
current time series forecasting | 0.708556 | [-0.12461531162261963, -1.0094871520996094, -0.016721516847610474, -0.4952985942363739, 0.13999900221824646, -0.6414883732795715, 0.7012004852294922, -0.2092379927635193, -0.05824136361479759, 0.7590556740760803, 0.22014731168746948, -1.076936960220337, -0.2781822979450226, -1.028551697731018, -0.19175051152706146, -0.... |
time series forecasting model | 0.708556 | [-0.42707327008247375, -0.8445063233375549, 0.1027931347489357, -0.6930047869682312, 0.11636827141046524, -0.41857561469078064, 0.9089028239250183, -0.1256975680589676, 0.04400693252682686, 0.5612362027168274, 0.25301676988601685, -1.1733806133270264, -0.4587112367153168, -0.9713273048400879, -0.1410454362630844, -0.68... |
time series forecasting approach | 0.708556 | [-0.3437235653400421, -0.8793365359306335, 0.20133481919765472, -0.7867913842201233, 0.009182133711874485, -0.6539931297302246, 0.8976569175720215, 0.08453617244958878, 0.00983283668756485, 0.7275962829589844, 0.2169969528913498, -1.221038818359375, -0.5636677145957947, -0.9794687628746033, 0.03787814825773239, -0.7240... |
time series forecasting system | 0.708556 | [-0.3781543970108032, -0.8686911463737488, 0.3271186351776123, -0.8346378207206726, 0.2764628827571869, -0.5219066143035889, 0.739142894744873, -0.039335884153842926, 0.08756348490715027, 0.6744265556335449, -0.059369299560785294, -1.1685818433761597, -0.3308861553668976, -0.9679350852966309, 0.01320159062743187, -0.73... |
time series forecasting | 0.708556 | [-0.21556313335895538, -0.7771633863449097, 0.29363441467285156, -0.6886365413665771, 0.02324867993593216, -0.6287068128585815, 0.8114888072013855, 0.07601696252822876, -0.05708525702357292, 0.7517459988594055, 0.10849186033010483, -1.159407377243042, -0.4942355155944824, -0.9697290658950806, -0.2856970429420471, -0.82... |
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