rejection
Mining GOLD Samples for Conditional GANs
Sangwoo Mo, Chiheon Kim, Sungwoong Kim, Minsu Cho, Jinwoo Shin
Training GANs (including cGANs), however, are known to be often hard and highly unstable [46]. Numerous techniques have thus been proposed to tackle the issue from different angles, e.g., improving architectures [32, 56, 7], losses and regularizers [16, 38, 20] and other training heuristics [46, 51, 8].
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Training Chain-of-Thought via Latent-Variable Inference Du Phan Matthew D. Hoffman
One can also improve LLMs' performance on a specific task by
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On the Calibration of Multiclass Classification with Rejection
Chenri Ni, Nontawat Charoenphakdee, Junya Honda, Masashi Sugiyama
Neural Information Processing Systems http://nips.cc/
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