Monica S. Aswani ([email protected]), University of Alabama at Birmingham, Birmingham, Alabama. Lauren A. Do, Boston University, Boston, Massachusetts. Paul R. Shafer, Boston University. The ...

Abstract: Sparsification technology is crucial for deploying convolutional neural networks in resource-constrained environments. However, the efficiency of sparse models is hampered by irregular ...

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Continuing with the success of Phases I and II of its Laconia 3D OBN multi-client seismic program in the US Gulf, Viridien has announced, in collaboration with partner TGS, the start of Laconia Phase ...

Dozens of machine learning algorithms require computing the inverse of a matrix. Computing a matrix inverse is conceptually easy, but implementation is one of the most challenging tasks in numerical ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of computing a matrix inverse using the Newton iteration algorithm. Compared to other algorithms, Newton ...

Pre-trained LLMs require instruction tuning to align with human preferences. Still, the vast data collection and rapid model iteration often lead to oversaturation, making efficient data selection a ...

High sparse Knowledge Graph is a key challenge to solve the Knowledge Graph Completion task. Due to the sparsity of the KGs, there are not enough first-order neighbors to learn the features of ...

Abstract: Structured sparsity has been proposed as an efficient way to prune the complexity of Machine Learning (ML) applications and to simplify the handling of sparse data in hardware. Accelerating ...

The Defense Advanced Research Projects Agency (DARPA) is taking a significant step toward overcoming the challenges of assembling large-scale structures in orbit by testing new in-space manufacturing ...