LEADER 01189nam1 2200361 450 001 990003549310203316 005 20110711150231.0 035 $a000354931 035 $aUSA01000354931 035 $a(ALEPH)000354931USA01 035 $a000354931 100 $a20110711f---0---9km-y0itay50------ba 101 0 $aita 102 $aIT 105 $ay|||||||001yy 200 1 $aOpere di Gaetano Negri 210 $aMilano$cHoepli 215 $avolumi$d18 cm 463 \1$1001990003549330203316$12001 $a<<1>>: Nel presente e nel passato$eprofili e bozzetti storici 463 \1$1001990003549350203316$12001 $a<<2>>: Meditazioni vagabonde$esaggi critici 463 \1$1001990003549370203316$12001 $a<<3>>: Rumori mondani 463 \1$1001990003549380203316$12001 $a<<4>>: Segni dei tempi$eprofili e bozzetti letterari 676 $a085.1 700 1$aNEGRI,$bGaetano$f<1838-1902> |$0300620 801 0$aIT$bsalbc$gISBD 912 $a990003549310203316 951 $aXV.2.A. 170/$cXV.2.A. 959 $aBK 969 $aCUOMO 979 $aDIONIGI$b90$c20110711$lUSA01$h1417 979 $aDIONIGI$b90$c20110711$lUSA01$h1502 996 $aOpere di Gaetano Negri$91116428 997 $aUNISA LEADER 03569nam 2200685 450 001 9910585937403321 005 20230627110803.0 035 $a(CKB)5600000000483108 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/91231 035 $a(NjHacI)995600000000483108 035 $a(EXLCZ)995600000000483108 100 $a20230330d2022 uy 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAAAI Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD) /$fKuan-Chuan Peng, Ziyan Wu 210 1$aBasel :$cMDPI - Multidisciplinary Digital Publishing Institute,$d2022. 215 $a1 electronic resource (186 p.) 311 0\$a3-0365-4681-2 311 0\$a3-0365-4682-0 327 $aAbout the Editors -- Statement of Peer Review -- Electricity Consumption Forecasting for Out-of-Distribution Time-of-Use Tariffs -- Measuring Embedded Human-Like Biases in Face Recognition Models -- Measuring Gender Bias in Contextualized Embeddings -- The Details Matter: Preventing Class Collapsein Supervised Contrastive Learning -- DAP-SDD: Distribution-Aware Pseudo Labeling for Small Defect Detection -- Quantifying Bias in a Face -- Verification System -- Super-Resolution for Brain MR Images from a Significantly Small Amount of Training Data -- Dual Complementary Prototype Learning for Few-Shot Segmentation -- Extracting Salient Facts from Company Reviews with Scarce Labels -- Long-Tail Zero and Few-Shot Learning via Contrastive Pretraining on and for Small Data -- Age Should Not Matter: -- Towards More Accurate Pedestrian Detection via Self-Training. 330 $aThis book is a collection of the accepted papers presented at the Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD) in conjunction with the 36th AAAI Conference on Artificial Intelligence 2022. During AIBSD 2022, the attendees addressed the existing issues of data bias and scarcity in Artificial Intelligence and discussed potential solutions in real-world scenarios. A set of papers presented at AIBSD 2022 is selected for further publication and included in this book. 517 $aAAAI Workshop on Artificial Intelligence with Biased or Scarce Data 606 $aTechnology: general issues$2bicssc 606 $aArtificial intelligence$vCongresses 606 $aHistory of engineering & technology$2bicssc 610 $apermutation equivariance 610 $aoptimization 610 $agender bias 610 $afairness 610 $aface-recognition models 610 $afacial attributes 610 $asocial bias 610 $abias detection 610 $anatural language processing 610 $atemporal bias 610 $aforecasting 610 $acontrastive learning 610 $asupervised contrastive learning 610 $atransfer learning 610 $arobustness 610 $anoisy labels 610 $acoresets 610 $adeep learning 610 $acontextualized embeddings 610 $aout-of-distribution generalization 615 7$aTechnology: general issues. 615 0$aArtificial intelligence 615 7$aHistory of engineering & technology. 676 $a006.3 700 \$aPeng$b Kuan-Chuan$01368114 702 $aWu$b Ziyan 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910585937403321 996 $aAAAI Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD)$93392746 997 $aUNINA