Besides textual paragraphs, researchers rely on various modalities to describe research methods. Figures convey information about concepts developed throughout the paper.

It enjoys model-free coverage guarantee regardless of the underlying distribution of the data.

Neighborhood attention reduces the cost of self attention by restricting each token's attention span to its nearest neighbors. This restriction, parameterized by a window size and dilation factor, draws a spectrum of possible attention patterns between linear projection and self attention. Neighborhood attention, and more generally sliding window attention patterns, have long been bounded by infrastructure, particularly in higher-rank spaces (2-D and 3-D), calling for the development of custom kernels, which have been limited in either functionality, or performance, if not both.

Machine learning models are increasingly being deployed in real-world contexts. However, systematic studies on their transferability to specific and critical applications are underrepresented in the research literature. An important example is visual anomaly detection (V AD) for robotic power line inspection.

The framework of formulating the feedback structure as feedback graphs in bandits has a long history (Mannor and Shamir, 2011; Alon et al., 2015, 2017; Lykouris et al.,

We pursue this hypothesis by developing a family of Representation Finetuning (ReFT) methods.

While Differential Privacy (DP) is the gold standard for protecting user privacy, standard DP mechanisms typically significantly impair performance.

However, a pivotal aspect of alignment for honesty involves discerning an LLM's