Binary Quantization Analysis of Neural Networks Weights on MNIST Dataset

On the VC-dimension of neural networks with binary weights

We investigate the VC-dimension of the perceptron and simple two-layer networks like the committeeand the parity-machine with weights restricted to values ±1. For binary inputs, the VC-dimension is determined by atypical pattern sets, i.e. it cannot be found by replica analysis or numerical Monte Carlo sampling. For small systems, exhaustive enumerations yield exact results. For systems that ar...

rodbar dam slope stability analysis using neural networks

در این تحقیق شبکه عصبی مصنوعی برای پیش بینی مقادیر ضریب اطمینان و فاکتور ایمنی بحرانی سدهای خاکی ناهمگن ضمن در نظر گرفتن تاثیر نیروی اینرسی زلزله ارائه شده است. ورودی های مدل شامل ارتفاع سد و زاویه شیب بالا دست، ضریب زلزله، ارتفاع آب، پارامترهای مقاومتی هسته و پوسته و خروجی های آن شامل ضریب اطمینان می شود. مهمترین پارامتر مورد نظر در تحلیل پایداری شیب، بدست آوردن فاکتور ایمنی است. در این تحقیق ...

Capacity of Two-Layer Feedforward Neural Networks with Binary Weights

The lower and upper bounds for the information capacity of two-layer feedforward neural networks with binary interconnections, integer thresholds for the hidden units, and zero threshold for the output unit is obtained through two steps. First, through a constructive approach based on statistical analysis, it is shown that a specifically constructed (N 2L 1) network with N input units, 2L hidde...

Vapnik-Chervonenkis dimension of neural networks with binary weights

We investigate the Vapnik-Chervonenkis ~VC! dimension of the perceptron and simple two-layer networks such as the committee and the parity machine with weights restricted to values 61. For binary inputs, the VC dimension is determined by atypical pattern sets, i.e., it cannot be found by replica analysis or numerical Monte Carlo sampling. For small systems, exhaustive enumerations yield exact r...

Mapping Boolean Fun tions with Neural Networks having Binary Weights