Filter Pruning Without Damaging Networks Capacity

Structural Compression of Convolutional Neural Networks Based on Greedy Filter Pruning

Convolutional neural networks (CNNs) have state-of-the-art performance on many problems in machine vision. However, networks with superior performance often have millions of weights so that it is difficult or impossible to use CNNs on computationally limited devices or to humanly interpret them. A myriad of CNN compression approaches have been proposed and they involve pruning and compressing t...

Finding Interesting Associations without Support Pruning

Association-rule mining has heretofore relied on the conditionof high support to do its work efficiently. In particular, the well-known a-priori algorithm is only effective when the only rules of interest are relationships that occur very frequently. However, there are a number of applications, such as data mining, identification of similar web documents, clustering, and collaborative filtering...

An Adaptive Self-adjusting Bandwidth Bandpass Filter without IIR Bias

In this paper we introduce a simple, computationally inxepentsive, adaptive recursive structure for enhancing bandpass signals highly corrupted by broad-band noise. This adaptive algorithm, enhancing input signals, enables us to estimate the center frequency and the bandwidth of the input signal. In addition, an important feature of the proposed structure is that the conventional bias existing ...

Tumor Cells without Damaging the Brain Immunotherapy Can Reject Intracranial

An Adaptive Self-adjusting Bandwidth Bandpass Filter without IIR Bias

In this paper we introduce a simple, computationally inxepentsive, adaptive recursive structure for enhancing bandpass signals highly corrupted by broad-band noise. This adaptive algorithm, enhancing input signals, enables us to estimate the center frequency and the bandwidth of the input signal. In addition, an important feature of the proposed structure is that the conventional bias existing ...