The "black-box" nature of representations, combined with the known susceptibility of networks to learn spurious correlations [

Considering different geographic regions generally have their own native sign languages, it is valuable to establish corresponding SL T datasets to support related communication and research. Auslan, as a sign language specific to Australia, still lacks a dedicated large-scale dataset for SL T.

This work proposes a middle ground in the form of a scalable information-theoretic generalization of CCA, termed max-sliced mutual information (mSMI).

In this section, we expand on our observation that similarity between training samples is not binary. Consider the images shown in Figure 6. As a consequence, any similarity between the anchor image and the so-called'negative' examples is completely ignored. Further, all'positive' examples are considered to be The batch size is set to 16000. We train on 4 A100 GPUs.

The learnt models in the latter domain can then be used for a diverse set of tasks in a zero-shot way, similar to "Contrastive Language-Image

The ocean is a crucial component of the Earth's system.

Large language models (LLMs), typically designed as a function of next-word prediction, have excelled across extensive NLP tasks. Despite the generality, next-word prediction is often not an efficient formulation for many of the tasks, demanding an extreme scale of model parameters (10s or 100s of billions) and sometimes yielding suboptimal performance. In practice, it is often desirable to build more efficient models--despite being less versatile, they still apply to a substantial subset of problems, delivering on par or even superior performance with much smaller model sizes.