Domain Robustness in Multi-modality Learning and Visual Question Answering

Humans perceive the world via multiple modalities, as information from a single modality is usually partial and incomplete. This observation motivates the development of machine learning algorithms capable of handling multi-modal data and performing intelligent reasoning. The recent resurgence of deep learning brings both opportunities and challenges to multi-modal reasoning. On the one hand, its strong representation learning capability provides a unified approach to represent information across multiple modalities. On the other hand, properly training such models typically requires enormous data, which is not always feasible especially for the multi-modal setting. One promising direction to mitigate the lack of data for deep learning models is to transfer knowledge (e.g., gained from solving related problems) to low-resource domains. This procedure is known as transfer learning or domain adaptation, and it has demonstrated great success in various visual and linguistic applications. However, how to effectively transfer knowledge in a multi-modality setting remains a research question. In this thesis, we choose multi-modal reasoning as our target task and aim at improving the performance of deep neural networks on low-resource domains via domain adaptation. We first briefly discuss our prior work about advertisement understanding (as a typical multi-modal reasoning problem) and share our experience from addressing the data-availability challenge. Next, we turn to visual question answering, a more general problem that involves more complicated reasoning. We evaluate mainstream VQA models and classic single-modal domain adaptation strategies and show that existing methods usually suffer significant performance degradation when directly apply to the multi-modal setting. We measure the domain gaps in different modalities and design an effective strategy to manually control domain shifts on individual modalities, which helps better understand the problem. Lastly, we present a systematic study across real datasets to answer a few fundamental questions regarding knowledge transfer in VQA, such as the sensitivity of various models towards different types of supervisions (i.e. unsupervised, self-supervised, semi-supervised, and fully supervised). We conclude by sharing the limitations and our vision for future research directions

Pacific Symposium on Biocomputing 2023

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field

Analysis and Application of Language Models to Human-Generated Textual Content

Social networks are enormous sources of human-generated content. Users continuously create information, useful but hard to detect, extract, and categorize. Language Models (LMs) have always been among the most useful and used approaches to process textual data. Firstly designed as simple unigram models, they improved through the years until the recent release of BERT, a pre-trained Transformer-based model reaching state-of-the-art performances in many heterogeneous benchmark tasks, such as text classification and tagging. In this thesis, I apply LMs to textual content publicly shared on social media. I selected Twitter as the principal source of data for the performed experiments since its users mainly share short and noisy texts. My goal is to build models that generate meaningful representations of users encoding their syntactic and semantic features. Once appropriate embeddings are defined, I compute similarities between users to perform higher-level analyses. Tested tasks include the extraction of emerging knowledge, represented by users similar to a given set of well-known accounts, controversy detection, obtaining controversy scores for topics discussed online, community detection and characterization, clustering similar users and detecting outliers, and stance classification of users and tweets (e.g., political inclination, COVID-19 vaccines position). The obtained results suggest that publicly available data contains delicate information about users, and Language Models can now extract it, threatening users' privacy