in recent years, researches on reinforcement learning (rl) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. neural network reinforcement learning (nnrl) is among the most popular algorithms in the rl framework. the advantage of using neural networks enables the rl to search for optimal policies more efficiently in several real-life applicat...

Deep learning has become very popular for tasks such as predictive modeling and pattern recognition in handling big data. Deep learning is a powerful machine learning method that extracts lower level features and feeds them forward for the next layer to identify higher level features that improve performance. However, deep neural networks have drawbacks, which include many hyper-parameters and ...

classification of vegetation according to their species composition is one of the most important tasks in the application of remote sensing in precision agriculture. to prepare an algorithm for such a mandate, there is a need for ground truth. field operation is very costly and time consuming. therefore, some other method must be developed, such as extracting information from the satellite imag...

We propose the implementation of transfer learning from natural images to audio-based using self-supervised schemes. Through learning, convolutional neural networks (CNNs) can learn general representation without labels. In this study, a network was pre-trained with (ImageNet) via learning; subsequently, it fine-tuned on target audio samples. Pre-training scheme significantly improved sound cla...

Recently, deep neural network algorithms have emerged as one of the most successful machine learning strategies, obtaining state of the art results for speech recognition, computer vision, and classification of large data sets. Their success is due to advancement in computing power, availability of massive amounts of data and the development of new computational techniques. Some of the drawback...

Several tasks in argumentation mining and debating, question-answering, and natural language inference involve classifying a sequence in the context of another sequence (referred as bisequence classification). For several single sequence classification tasks, the current state-ofthe-art approaches are based on recurrent and convolutional neural networks. On the other hand, for bi-sequence class...

Supervised neural-network learning algorithms have proven very successful at solving a variety of learning problems. However, they suffer from a common problem of requiring explicit output labels. This requirement makes such algorithms implausible as biological models. In this paper, it is shown that pattern classification can be achieved, in a multilayered feedforward neural network, without r...

Deep neural networks (NN) are extensively used for machine learning tasks such as image classification, perception and control of autonomous systems. Increasingly, these deep NNs are also been deployed in high-assurance applications. Thus, there is a pressing need for developing techniques to verify neural networks to check whether certain user-expected properties are satisfied. In this paper, ...

A deep learning approach has been widely applied in sequence modeling problems. In terms of automatic speech recognition (ASR), its performance has significantly been improved by increasing large speech corpus and deeper neural network. Especially, recurrent neural network and deep convolutional neural network have been applied in ASR successfully. Given the arising problem of training speed, w...