While performing favourably on the independent and identically distributed (i.i.d.)

We introduce Parsel, a framework enabling automatic implementation and validation of complex algorithms with code LLMs.

Equivariant neural networks encode symmetry as an inductive bias and have achieved strong empirical performance in wide domains. However, their expressive power remains not well understood. Focusing on 2-layer ReLU networks, this paper investigates the impact of equiv-ariance constraints on the expressivity of equivariant and layer-wise equivariant networks. By examining the boundary hyperplanes and the channel vectors of ReLU networks, we construct an example showing that equivariance constraints could strictly limit expressive power. However, we demonstrate that this drawback can be compensated via enlarging the model size. Furthermore, we show that despite a larger model size, the resulting architecture could still correspond to a hypothesis space with lower complexity, implying superior generalizability for equivariant networks.

As described in 3, the MemRecall is the process to extract the key blocks. We also need "strides" as BM25 is a famous TF-IDF-like information retrieval method. Each block is scored based on the common words with query or textual label. However, the semantic relevance are neglected. Glove is a group of pretrained word representation.

In this work, we alleviate this gap.

A wide array of sequence models are built on a framework modeled after Transformers, comprising alternating sequence mixer and channel mixer layers.

We derive the distortion-rate function for this setup as a linear program, and provide an efficient algorithm to compute this fundamental limit via the dual of the linear program.