10 accepted papers
Structured pruning reduces LLM inference cost by removing low-importance architectural components. This can be viewed as learning a multiplicative gate for each component under an $\ell_0$ sparsity constraint. Due to the discreteness of the $\ell_0$ norm, prior work typically adopts stochastic hard-…
The remarkable success of Large Language Models (LLMs) relies heavily on their substantial scale, which poses significant challenges during model deployment in terms of latency and memory consumption. Recently, numerous studies have attempted to compress LLMs using one-shot pruning methods. However,…
Training large transformers is slow, but recent innovations on GPU architecture give us an advantage. NVIDIA Ampere GPUs can execute a fine-grained 2:4 sparse matrix multiplication twice as fast as its dense equivalent. In the light of this property, we comprehensively investigate the feasibility of…
Modern neuroimaging techniques offer disctinct views on brain structure and function. Data acquired using these techniques can be analyzed in terms of its network structure to identify organizing principles at the systems level. Graph representations are flexible frameworks where nodes are related t…
This paper develops new designs for recommender systems inspired by recent advances in graph signal processing. Recommender systems aim to predict unknown ratings by exploiting the information revealed in a subset of user-item observed ratings. Leveraging the notions of graph frequency and graph fil…
This paper examines metric spaces in which the distance between any pair of nodes is given by an interval. The goal is to investigate methods for hierarchical clustering, i.e., a family of nested partitions indexed by a connectivity parameter, deduced from the underlying distance intervals of the me…
This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters recently generalized to irregular graph domains defines brain graph frequencies associated with different levels of spatial smoothness across the brain r…
This paper presents diffusion filtering as a method to smooth signals defined on the nodes of a graph or network. Diffusion filtering considers the given signals as initial temperature distributions in the nodes and diffuses heat through the edges of the graph. The filtered signal is determined by t…
High order networks are weighted complete hypergraphs collecting relationships between elements of tuples. Valid metric distances between high order networks have been defined but they are difficult to compute when the number of nodes is large. We relate high order networks to the filtrations of sim…
This paper presents two families of distances in the space of high order proximity networks. The distances measure differences between networks and are shown to be valid metrics in the space of high order proximity networks modulo permutation isomorphisms. Practical implications are explored by comp…