Sharif Digital Repository

A predefined assumption in many learning algorithms is that the training and test data must be in thesame feature space and have the same distribution.However, this assumption may not hold in all of these algorithms and in the real world there might be difference between the source and the targer domian, whether in the feature space or the distribution. Moreover, there might be a few number of labled data of the target domain which causes difficulty in learning an accurate classifier. In such cases, transferring knowledge can be useful if can be done successfully and transfer learning was introduced for this purpose. Domain Adaptation is one of the transfer leaning problems that assume some... 

Despite recent advancements in deep learning methods, these methods rely on a huge amount of training data to work. Recently the problem of solving classification and recently semantic segmentation problems with a few training data have gained attention to tackle this issue. In this research, we propose a meta-learning method by combining optimization-based and prototypical approaches in which a small portion of parameters are optimized with task-specific initialization. In addition to this and designing other parts of the method, we propose a new approach to use query data as an unlabeled sample to enhance task-specific learning. Alongside the mentioned method, we propose an approach to use... 

Regression testing, is one of the most effective software testing activities. Adding new test cases during project development to cover different aspects of software performance will increase the test suite’s size and the time and resources required to fully execute the test suite. To face this challenge, test case prioritization is considered as one of the main approaches. The software bug history is one of the useful information sources for improving software analysis solutions, which rarely been used in the field of test case prioritization. The aim of this research is to present algorithms for utilizing the software bug history along with other information sources to improve test case... 

With the popularity of smart devices, a lot of applications have developed and deployed.Developers try to establish continuous interaction with their users by different tools including push notifications. Push notification is a message that is sent from developers to the users and as soon as the user’s device receives that, it appears on the device screen. Sending proper content to users in order to resume their engagement is one of the most important usages of notifications. Users are not interested in receiving irrelevant notifications, and receiving irrelevant notifications make them remove the application, so it’s important to predict users’ interest in different notifications and push... 

Solving the visual symbol grounding problem has long been a goal of modern aritificial intelligence. Due to recent breakthroughs in deep learning methods for natural language processing and visual interpretation tasks‚ the field now seems to be as near to achieving this goal as it ever was. Also recent progress in using recurrent neural netowrks (RNNs) for image description‚ has motivated the exploration of their application for video description tasks. However, while images remain static‚ interpreting videos require modeling complex dynamic temporal sturctures and then properly integrating that information into a natural language description. Recurrent neural networks can be both used to... 

Isoforms are mRNAs that are produced from a same gene site in the phenomenon called Alternative Splicing. Studies have shown that more than 95% of multiexon genes in humans have undergone Alternative Splicing. Although there are few changes in mRNA sequence, They may have a systematic effect on cell function and regulation. It is widely reported that isoforms of a gene have distinct or even contrasting functions. Most studies have shown that alternative splicing plays a significant role in human health and disease. Despite the wide range of gene function studies, there is little information about isoforms’ functionalities. Recently, some computational methods based on Multiple Instance... 

Graphs are widely used for representing structured data and analysis of them is an important area that appears in a broad domain of applications. Graph processing is of great importance in analyzing and predicting social media users' behavior, examining financial markets, detecting malware programs, and designing recombinant drugs. For example, consider a graph in which nodes and edges show the financial institutions and the financial connection between these institutions, respectively. Financial connection refers to the investment of one institute by another. Based on the graph structure, predicting trade stability and balance is extremely significant in macro decisions.In the last few... 

Brand perceptual mapping is a way to display the market structure in a particular industry. In the past, marketing managers relied on questionnaires and focus-groups' data to map the market structure and extract insights from it. These methods are expensive, time-consuming, and not generalizable due to the small sample size. With the expansion of social media, new techniques have been introduced that can map the market structure by analyzing user-generated content on social media. Currently, methods used for this task are automated methods based on the bag-of-word model, which ignores the meaning of words and sentences and needs big datasets. In this research, using language models and... 

Brand perceptual mapping is a way to display the market structure in a particular industry. In the past, marketing managers relied on questionnaires and focus-groups' data to map the market structure and extract insights from it. These methods are expensive, time-consuming, and not generalizable due to the small sample size. With the expansion of social media, new techniques have been introduced that can map the market structure by analyzing user-generated content on social media. Currently, methods used for this task are automated methods based on the bag-of-word model, which ignores the meaning of words and sentences and needs big datasets. In this research, using language models and... 

Sample efficiency and systematic generalization are two long-standing challenges in sequential decision-making problems, especially, in reinforcement learning settings. It is hypothesized that involving natural language in conjunction with other observation modalities in decision-making environments can improve generalization due to its compositional and open-ended nature, and sample efficiency due to the concise information summarized in relatively short linguistic units. By exploiting this information and the compositional structure of the language, one can achieve an abstract and factored understanding of the environment and the task at hand. To do so, it is necessary to find the proper... 

One of the main challenges in the field of Machine Learning, which has come to attention in recent years, is the models' inability to generalize well to datapoints that come from a distribution different from the one that the model has been trained on. This problem is known by Out-of-Distribution Generalization. This problem is of utmost importance as in many real-world applications the training and test data do not come from the same distribution, and thus the model will fail on the test data despite its acceptable performance on samples from the same distribution of the training set. One of the main reasons behind such failure is models' reliance on spurious correlations between some... 

Few-shot learning methods have achieved notable performance in recent years. However, fewshot learning in large-scale settings with hundreds of classes is still challenging. In this dissertation, we tackle the problems of large-scale few-shot learning by taking advantage of pre-trained foundation models. We recast the original problem in two levels with different granularity. At the coarse-grained level, we introduce a novel object recognition approach with robustness to sub-population shifts. At the fine-grained level, generative experts are designed for few-shot learning, specialized for different superclasses. A Bayesian schema is considered to combine coarse-grained information with... 

e study of the brain’s neural activity is an active research area in computational neuroscience aiming to provide insights about the functionality of the brain as well as dysfunctions that underlie disorders. Functional Magnetic Resonance Imaging (fMRI) plays an important role in brain studies by providing non-invasive records of neural activities during a specific task with location sensitivity. Recent advances in statistics and machine learning offer powerful tools for paern recognition and processing of fMRI data. In this thesis, we decode information recorded via fMRI from the visual cortex to reconstruct images presented to subjects. Current reconstruction methods face numerous...