In this paper, two new hybrid algorithms are proposed for decoding Low Density Parity Check (LDPC) codes. Original version of the proposed algorithms named Search Based Weighted Multi Bit Flipping (SWMBF). The main idea of these algorithms is flipping variable multi bits in each iteration, change in which leads to the syndrome vector with least hamming weight. To achieve this, the proposed algo...

A new iterative learning controller is proposed for a general unknown discrete time-varying nonlinear non-affine system represented by NARMAX (Nonlinear Autoregressive Moving Average with eXogenous inputs) model. The proposed controller is composed of an iterative learning neural identifier and an iterative learning controller. Iterative learning control and iterative learning identification ar...

Low-bit deep neural networks (DNNs) become critical for embedded applications due to their low storage requirement and computing efficiency. However, they suffer much from the non-negligible accuracy drop. This paper proposes the stochastic quantization (SQ) algorithm for learning accurate low-bit DNNs. The motivation is due to the following observation. Existing training algorithms approximate...

During the last few decades, several papers were published about second-order optimization methods for gradient descent based learning algorithms. Unfortunately, these methods usually have a cost in time close to O(n) per iteration, and O(n) in space, where n is the number of parameters to optimize, which is intractable with large optimization systems usually found in real-life problems. Moreov...

We introduce a new surrogate loss function called orbit loss in the structured prediction framework, which has good theoretical and practical advantages. While the orbit loss is not convex, it has a simple analytical gradient and a simple perceptron-like learning rule. We analyze the new loss theoretically and state a PAC-Bayesian generalization bound. We also prove that the new loss is consist...

The performance of bit-interleaved coded modulation in multiple-input multiple-output (BICM-MIMO) systems using an iterative channel estimator is analysed. In a conventional iterative channel estimator, after initialisation with the training phase, the channel estimator switches to the data phase. However, such a conventional iterative channel estimator does not always improve the performance o...

Classification performance can degrade if data contain missing attribute values. Many methods deal with missing information in a simple way, such as replacing missing values with the global or class-conditional mean/mode. We propose a new iterative algorithm to effectively estimate missing attribute values in both training data and test data. The attributes are selected one by one to be complet...

In a weakly-supervised scenario, object detectors need to be trained using image-level annotation only. Since bounding-box-level ground truth is not available, mostof the solutions proposed so far are based on an iterative approach in which theclassifier, obtained in the previous iteration, is used to predict the objects’ positionswhich are used for training in the current itera...

The time trade-off (TTO) valuation technique is widely used to determine utility values of health outcomes to inform quality-adjusted life-year (QALY) calculations for use in economic evaluation. Protocols for implementing TTO vary in aspects such as the trade-off framework, iteration procedure and its administration model and method, training of respondents and interviewers, and quality contro...