Zobrazeno 1 - 10
of 34 635
pro vyhledávání: '"Network Inference"'
Due to the extensive application of machine learning (ML) in a wide range of fields and the necessity of data privacy, privacy-preserving machine learning (PPML) solutions have recently gained significant traction. One group of approaches relies on H
Externí odkaz:
http://arxiv.org/abs/2412.07954
For FPGA-based neural network accelerators, digital signal processing (DSP) blocks have traditionally been the cornerstone for handling multiplications. This paper introduces LUTMUL, which harnesses the potential of look-up tables (LUTs) for performi
Externí odkaz:
http://arxiv.org/abs/2411.11852
Autor:
Puigdemont, Pol, Russo, Enrico, Wassington, Axel, Das, Abhijit, Abadal, Sergi, Palesi, Maurizio
Graph Neural Networks (GNNs) have shown significant promise in various domains, such as recommendation systems, bioinformatics, and network analysis. However, the irregularity of graph data poses unique challenges for efficient computation, leading t
Externí odkaz:
http://arxiv.org/abs/2411.16342
Machine learning has revolutionized data analysis and pattern recognition, but its resource-intensive training has limited accessibility. Machine Learning as a Service (MLaaS) simplifies this by enabling users to delegate their data samples to an MLa
Externí odkaz:
http://arxiv.org/abs/2411.07468
We introduce a precision polarization scheme for DNN inference that utilizes only very low and very high precision levels, assigning low precision to the majority of network weights and activations while reserving high precision paths for targeted er
Externí odkaz:
http://arxiv.org/abs/2411.05845
Large-scale foundation models have demonstrated exceptional performance in language and vision tasks. However, the numerous dense matrix-vector operations involved in these large networks pose significant computational challenges during inference. To
Externí odkaz:
http://arxiv.org/abs/2410.21262
Autor:
Li, Peiwen, Wu, Menghua
Perturbation experiments allow biologists to discover causal relationships between variables of interest, but the sparsity and high dimensionality of these data pose significant challenges for causal structure learning algorithms. Biological knowledg
Externí odkaz:
http://arxiv.org/abs/2410.14436
Gene regulatory networks (GRNs) play a crucial role in the control of cellular functions. Numerous methods have been developed to infer GRNs from gene expression data, including mechanism-based approaches, information-based approaches, and more recen
Externí odkaz:
http://arxiv.org/abs/2410.21295