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Anelia Angelova
Also published under:A. Angelova
Affiliation
Google DeepMind
Topic
Object Detection,Flamingo,Position Embedding,Self-supervised Learning,Vision Transformer,Ablation,Base Classifiers,Batch Size,Benchmark,Bounding Box,Contrastive Loss,Detection Task,Fine-tuned,Giraffe,Global Average Pooling,Image Encoder,ImageNet,Language Model,Latent Space,Learning Rate,Object Proposals,Reconstruction Loss,Representation Learning,Text Encoder,Tokenized,Training Set,Weak Supervision,3D Kernel,Adzuki Bean,Area Under Curve,Aspect Ratio,Attention Mechanism,Audio Input,Audio Video,Autoregressive Model,Average Success Rate,Backbone Feature,Binary Classification,Box Regression,CNN Backbone,Camera Frame,Channel Dimension,Collision,Contrast Objective,Crops In Regions,Cross-entropy Loss,Depth Camera,Depth Images,Emergent Properties,FC Layer,
Biography
Anelia Angelova has graduated with a Master of Science degree in Computer Science from the Department of Mathematics and Informatics, Sofia University, Bulgaria and a Master of Science degree in Computer Science from the Department of Computer Science, California Institute of Technology in 2000 and 2004 respectively. She worked as a software engineer for ADA Bulgaria from 1999 until 2001. She is currently a PhD student at California Institute of Technology. Her research interests are in the area of computer vision and machine learning.