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Found 6 papers, 1 papers with code
Microscaling Data Formats for Deep Learning
Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications.
With Shared Microexponents, A Little Shifting Goes a Long Way
This paper introduces Block Data Representations (BDR), a framework for exploring and evaluating a wide spectrum of narrow-precision formats for deep learning.
Non-Local Feature Aggregation on Graphs via Latent Fixed Data Structures
The proposed approach is used to design several novel global feature aggregation methods based on the choice of the LFDS.
Online Topology Inference from Streaming Stationary Graph Signals with Partial Connectivity Information
This motivates formulating the topology inference task as an inverse problem, whereby one searches for a sparse GSO that is structurally admissible and approximately commutes with the observations' empirical covariance matrix.
Towards Accelerated Greedy Sampling and Reconstruction of Bandlimited Graph Signals
Then, we consider the Bayesian scenario where we formulate the sampling task as the problem of maximizing a monotone weak submodular function, and propose a randomized-greedy algorithm to find a sub-optimal subset of informative nodes.
Sampling and Reconstruction of Graph Signals via Weak Submodularity and Semidefinite Relaxation
We study the problem of sampling a bandlimited graph signal in the presence of noise, where the objective is to select a node subset of prescribed cardinality that minimizes the signal reconstruction mean squared error (MSE).
