MNIST
mnist-b117d755·4 events·first seen 1mo agoAliases: MNIST
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Controlled Audit of Human vs. Synthetic Soft-Labels for Calibration and Uncertainty Alignment
This paper presents a controlled study disentangling the effects of human soft-labels from label mode-shift corrections in soft-label learning, using MNIST and a synthetic variant. The authors find that human soft-labels primarily act as a regularizer improving calibration on difficult samples and promoting stable training convergence, rather than simply correcting mislabeled data. Dataset cartography analysis shows models trained on human soft-labels mirror human uncertainty patterns, while those trained on synthetic labels fail to align. The work provides a diagnostic testbed for evaluating human-AI uncertainty alignment.
How to Train Your Model Dynamically Using Adversarial Data
This Hugging Face blog post describes a methodology for dynamically training models using adversarial data, likely in the context of improving robustness against adversarial examples. The post covers techniques for generating and incorporating adversarial inputs during the training loop to improve model resilience. Published in mid-2022, it targets practitioners looking to harden ML models against distribution shift and adversarial attacks.
Dynamics-Level Watermarking of Flow Matching Models with Random Codes
This paper proposes embedding watermarks directly into the velocity field (continuous dynamics) of flow matching generative models, rather than into weights or outputs. The method uses key-dependent perturbations added during training, formulated as random coding over a continuous channel, allowing black-box message recovery at detection time. The perturbation is designed to leave the generated distribution unchanged. Experiments on MNIST and CIFAR-10 demonstrate reliable message recovery, preserved generation quality, and chance-level decoding without the secret key.
Anthropic proposes ambitious federal funding increase for NIST AI measurement and standards
Anthropic published a policy proposal in April 2023 calling for a significant increase in federal funding for the National Institute of Standards and Technology (NIST) to support AI measurement, evaluation, and standards work. The post argues that rigorous AI capability and risk measurement is a prerequisite for effective regulation, and outlines a concrete funding program building on NIST's existing AI Risk Management Framework and related work. Anthropic frames this as a 'shovel-ready' complement to broader AI governance proposals, recommending at minimum a $15 million increase over FY2023 levels.