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Characterizing and Efficiently Accelerating Multimodal Generation Model Inference

Generative artificial intelligence (AI) technology is revolutionizing the computing industry. Not only its applications have broadened to various sectors but also poses new system design and optimization opportunities. The technology is capable of un

Externí odkaz: http://arxiv.org/abs/2410.00215

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Approximate Contraction of Arbitrary Tensor Networks with a Flexible and Efficient Density Matrix Algorithm

Autor: Ma, Linjian, Fishman, Matthew, Stoudenmire, Miles, Solomonik, Edgar

Tensor network contractions are widely used in statistical physics, quantum computing, and computer science. We introduce a method to efficiently approximate tensor network contractions using low-rank approximations, where each intermediate tensor ge

Externí odkaz: http://arxiv.org/abs/2406.09769

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Leveraging the Human Ventral Visual Stream to Improve Neural Network Robustness

Autor: Shao, Zhenan, Ma, Linjian, Li, Bo, Beck, Diane M.

Human object recognition exhibits remarkable resilience in cluttered and dynamic visual environments. In contrast, despite their unparalleled performance across numerous visual tasks, Deep Neural Networks (DNNs) remain far less robust than humans, sh

Externí odkaz: http://arxiv.org/abs/2405.02564

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TongueSAM: An Universal Tongue Segmentation Model Based on SAM with Zero-Shot

Autor: Cao, Shan, Ruan, Qunsheng, Ma, Linjian

Tongue segmentation serves as the primary step in automated TCM tongue diagnosis, which plays a significant role in the diagnostic results. Currently, numerous deep learning based methods have achieved promising results. However, when confronted with

Externí odkaz: http://arxiv.org/abs/2308.06444

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Tensor Rank and Other Multipartite Entanglement Measures of Graph States

Autor: Schatzki, Louis, Ma, Linjian, Solomonik, Edgar, Chitambar, Eric

Graph states play an important role in quantum information theory through their connection to measurement-based computing and error correction. Prior work has revealed elegant connections between the graph structure of these states and their multipar

Externí odkaz: http://arxiv.org/abs/2209.06320

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Cost-efficient Gaussian Tensor Network Embeddings for Tensor-structured Inputs

Autor: Ma, Linjian, Solomonik, Edgar

This work discusses tensor network embeddings, which are random matrices ($S$) with tensor network structure. These embeddings have been used to perform dimensionality reduction of tensor network structured inputs $x$ and accelerate applications such

Externí odkaz: http://arxiv.org/abs/2205.13163

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