AAAI Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD)

AAAI Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD)

This 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 scarc...

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Detalles Bibliográficos
Otros Autores: Peng, Kuan-Chuan, author (author), Wu, Ziyan, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Basel : MDPI - Multidisciplinary Digital Publishing Institute 2022.
Materias:
Technology: general issues.
Artificial intelligence > Congresses.
History of engineering & technology.
permutation equivariance
optimization
gender bias
fairness
face-recognition models
facial attributes
social bias
bias detection
natural language processing
temporal bias
forecasting
contrastive learning
supervised contrastive learning
transfer learning
robustness
noisy labels
coresets
deep learning
contextualized embeddings
out-of-distribution generalization
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009672611706719
Tabla de Contenidos:
  • About 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.

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