Data augmentation has emerged asaneffectivedata pre-processing ordata transformation step to mitigateoverfitting [31],toencourage local smoothness [57],andtoimprovegeneralization [6]in machine learning pipelines such as deep neural networks.

Stronger ' s, explicitly increases utilitytrade-of independent demonstraten and =1.

This relationship allows us to design a robust training objective without approximating a non-existing smoothed classifier, e.g., via soft smoothing.

This position paper argues that literary scholars must engage with large language model (LLM) interpretability research. While doing so will involve ideological struggle, if not out-right complicity, the necessity of this engagement is clear: the abiding instrumentality of current approaches to interpretability cannot be the only standard by which we measure interpretation with LLMs. One site at which this struggle could take place, I suggest, is the red team.

There are 1000 iterations between every two checkpoints. SSL algorithms and the FlexMatch achieves the best accuracy. Our toolbox is partially based on [7]. More importantly, in addition to the basic SSL methods and components, we implement several techniques to make the results stable under PyTorch framework. CIFAR-100, SVHN, and STL-10, and report the best error rates in Table 6, 7, 8, and 9, respectively.

Science progresses by iteratively advancing and correcting humanity's understanding of the world. In machine learning (ML) research, rapid advancements have led to an explosion of publications, but have also led to misleading, incorrect, flawed or perhaps even fraudulent studies being accepted and sometimes highlighted at ML conferences due to the fallibility of peer review. While such mistakes are understandable, ML conferences do not offer robust processes to help the field systematically correct when such errors are made. This position paper argues that ML conferences should establish a dedicated "Refutations and Critiques" (R&C) Track. This R&C Track would provide a high-profile, reputable platform to support vital research that critically challenges prior research, thereby fostering a dynamic self-correcting research ecosystem. We discuss key considerations including track design, review principles, potential pitfalls, and provide an illustrative example submission concerning a recent ICLR 2025 Oral. We conclude that ML conferences should create official, reputable mechanisms to help ML research self-correct.