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pro vyhledávání: '"Song, Xidan"'
We present QNNRepair, the first method in the literature for repairing quantized neural networks (QNNs). QNNRepair aims to improve the accuracy of a neural network model after quantization. It accepts the full-precision and weight-quantized neural ne
Externí odkaz:
http://arxiv.org/abs/2306.13793
We present AIREPAIR, a platform for repairing neural networks. It features the integration of existing network repair tools. Based on AIREPAIR, one can run different repair methods on the same model, thus enabling the fair comparison of different rep
Externí odkaz:
http://arxiv.org/abs/2211.15387
Neural networks are essential components of learning-based software systems. However, their high compute, memory, and power requirements make using them in low resources domains challenging. For this reason, neural networks are often quantized before
Externí odkaz:
http://arxiv.org/abs/2207.04231
Autor:
Song, Xidan, Manino, Edoardo, Sena, Luiz, Alves, Erickson, Filho, Eddie de Lima, Bessa, Iury, Lujan, Mikel, Cordeiro, Lucas
QNNVerifier is the first open-source tool for verifying implementations of neural networks that takes into account the finite word-length (i.e. quantization) of their operands. The novel support for quantization is achieved by employing state-of-the-
Externí odkaz:
http://arxiv.org/abs/2111.13110
Autor:
Sena, Luiz, Song, Xidan, Alves, Erickson, Bessa, Iury, Manino, Edoardo, Cordeiro, Lucas, Filho, Eddie de Lima
Artificial Neural Networks (ANNs) are being deployed for an increasing number of safety-critical applications, including autonomous cars and medical diagnosis. However, concerns about their reliability have been raised due to their black-box nature a
Externí odkaz:
http://arxiv.org/abs/2106.05997
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