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1. The document discusses energy-based models (EBMs) and how they can be applied to classifiers. It introduces noise contrastive estimation and flow contrastive estimation as methods to train EBMs. 2. One paper presented trains energy-based models using flow contrastive estimation by passing data through a flow-based generator. This allows implicit modeling with EBMs. 3. Another paper argues that

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ã€Œ21ä¸–ç´€ã®ç›¸é–¢ã€HSICã®åŽŸè«–æ–‡"Measuring Statistical Dependence with Hilbert-Schmidt Norms" (Gretton et al., Algorithmic Learning Theory, 2005)ãƒ¡ãƒ¢ãƒ©ãƒ³ãƒ€ãƒ - æ¸‹è°·é§…å‰ã§åƒããƒ‡ãƒ¼ã‚¿ã‚µã‚¤ã‚¨ãƒ³ãƒ†ã‚£ã‚¹ãƒˆã®ãƒ–ãƒã‚°

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SAM is a new segmentation model that can segment objects in images using natural language prompts. It was trained on over 1,100 datasets totaling over 10,000 images using a model-in-the-loop approach. SAM uses a transf ormer-based architecture with encoders for images, text, bounding boxes and masks. It achieves state-of-the-art zero-shot segmentation performance without any fine-tuning on target d

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