Sharif Digital Repository

With the advent of graph neural network networks, a new class of models has emerged that has a high ability to learn powerful representations. Also, there have been popular probabilistic latent variable models for representation learning and solving graph problems. Graph neural networks do not necessarily provide meaningful representation in their hidden layers and also do not have the ability to estimate uncertainty. Learning probabilistic models is usually a slow process and there is no specific way to add general features to these models. Therefore, recently, a combination of neural network models and probabilistic network models have been developed that can partially answer these... 

By the improvement of machine learning methods specially the Deep Learning in the last decade, there were expanding usage of these methods in Language Modeling task. As the essence of a language model is more basic, recently huge networks are trained with language model objective but fine-tuned on target tasks such as Question Answering, Sentiment Analysis and etc. which is a promising sign of its importance and usage in even other NLP tasks. However, this task still has severe problems. The Teacher Forcing based methods, suffer from the so-called exposure bias problem which is due to the train/test procedure discrepancy. Some solutions such as using Reinforcement Learning which has high... 

In recent years, deep generative models have achieved incredible successes in various fields, including graph generation. Due to the advances made in graph generation by deep generative models, these methods have shown numerous applications from drug discovery and molecular graph generation to modeling social and citation network graphs. Graph generation is an approach to discovering and exploring new graph structures and has been attracting growing attention. One of the most challenging applications of deep graph generative models is molecular graph generation since it requires not only generating chemically valid molecular structures but also optimizing their chemical properties in the... 

Flameless combustion is among the newest combustion technologies that is used for reducing the pollutants in recent couple of decades. Within this combustion regime, diminution of super hot points throughout the combustion chamber along with decline in production of pollutant gases occur simultaneously. This kind of combustion often is created by applying two processes; first by preheating the fuel and/or oxydizer flows at the inlet to tempratures over the self-ignition level; and second with diluting the whole amount of fuel and oxidizer in the chamber. These two happen by using hot exhaust gas of the same chamber (via feedback) or of the previous section (in multi-section chambers).... 

Tracking is one of the old and still not thoroughly solved problems in machine vision. Its importance lies on its many applications. These applications vary from security surveillance to examining the motion pattern of atomic particles. There is not a tracker which has acceptable results in all situations, yet. A tracker faces many difficulties such as change in illumination and occlusion. In past, tracking was done by using filters or optical flows. By use of the advances in machine learning and pattern recognition, many models have been proposed to accomplish tracking by using these new learning methods. In this dissertation, we proposed a new tracking method which utilizes sparse... 

Flameless combustion in combustion technology is an innovative method with low nox production that few years provided it passes. In this paper the effects of various parameters on flameless combustion chamber with separate fuel and air jets, has been modeled numerically with the help of Fluent 6.3 software. Differences between traditional combustion and flameless combustion were determined and the flame structure has been studied in different scenarios. In numerical modeling, approach were used to modeling turbulence and eddy dissipation concept (EDC) approach use to modeling combustion and turbulence interaction effects. A global Two-step reaction mechanism for propane and a global... 

In a variety of applications, we deal with event sequences which we require to model the time of those. Event sequences modelling is vital in a variety of applications such as electronic commerce, social networks, health information; For instance, in the context of social networks entrance time, the act of like or comment can be regarded as an event sequence. Point processes are the framework for modelling event sequences, in which designer use prior knowledge and different assumptions (which is not necessarily true) to set the functional form of intensity function. That functional form may not be sufficient enough to model event sequences. In this project, we have used a deep nonlinear... 

Deep learning methods have become more popular in the past years. These methods use complex network architectures to model rich, hierarchical datasets. Although most of the research has been centered around Discriminative models, however, recently a lot of research is focused on Deep Generative Models. Two of the pioneering models in this field are Generative Adversarial Networks and Variational Auto-Encoders. In addition, knowing the structure of data helps models to search in a narrower hypothesis space. Most of the structure in datasets are models using Probabilistic Graphical Models. Using this structural information, one can achieve better parameter estimations. In the case of... 

Trip demand analysis is among the most important parts in comprehensive transportation studies. Developing four stage models including trip generation, trip distribution, mode choice, and assignment are so common in demand analyzing. In this study, trip generation models have been studied and to analyze data, two different methods including OLR and GWR are utilized. This study aims to examine relationship between aggregate zones in order to forecast future trips. Moreover, results of modeling trip generation with two different methods, OLR and GWR, are compared. Comparing different levels of aggregation reveals that as the number of zones decreases, the results get better. Decreasing number... 

Nowadays, there exist many real networks with distinctive features in comparison with random networks. Social networks, collaboration networks, citation networks, protein networks and communication networks are some example of complex network classes. Nowadays these networks are widespread and have many applications and the study of complex networks is an important research area. In many applications, the “synthetic networks generation” is one of the first levels of complex networks analysis. This level has many applications such as simulation and extrapolation. Many generative models are proposed for complex network modeling in recent years. By the use of these models, synthetic networks... 

A social network represents a set of entities and their relationships. Telecommunication networks, online social networks, and paper citation networks are some examples of networks in real world. Nowadays, analysis of social networks is an interesting research area with important applications. Particularly, managers of the social networks and the decision makers often require intelligent decision support for futures study in these social systems. The demanded decision support systems make it possible to define the desired social problem and to analyze the ”what-if scenarios.“ Computer simulation is an appropriate approach toward such decision support systems. In this approach, the desired... 

In this thesis Generative Models in Deep Learning are discussed, especially Generative Models which are based on latent variables. Deep Generative Models have key role in developing Artificial Intelligence, particularly in developments of general cognition and perception in AI. In this thesis, this role for Generative Models and their applications in cognition development, and also the mathematical foundation of generative models are discussed  

Graphs are a language to describe and analyze connections and relations. Recent developments have increased graphs' applications in real-world problems such as social networks, researchers' collaborations, and chemical compounds. Now that we can extract graphs from real life, how can we model and generate graphs similar to a set of known graphs or that are very likely to exist but haven't been discovered yet? Therefore, this research will focus on the problem of graph generation. In graph generation, a set of graphs is a training dataset, and the goal of the thesis is to present an improved deep generative model to learn the training data's distribution, structure, and features.Identifying... 

Drug-target binding affinity prediction is one of the most important and vital part of drug discovery. The computational methods to predict binfing affinity is a standing challenge in drug discovery. State-of-the-art models are usually based on supervised machine learning with known label information. It is expensive and time-consuming to collect labeled data. This thesis proposes a semi-supervised model based on convolutional GAN (Generative adversarial networks). The model consists of two Gans and Two CNN blocks for feature extraction and fully connected layers for prediction. Gan can learn protein and drug features from unlabeled data. We evaluate the performance of our method using four...