Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

Visual Feature Learning

Categorization is a fundamental problem of many computer vision applications, e.g., image classification, pedestrian detection and face recognition. The robustness of a categorization system heavily relies on the quality of features, by which data are represented. The prior arts of feature extraction can be concluded in different levels, which, in a bottom up order, are low level features (e.g., pixels and gradients) and middle/high-level features (e.g., the BoW model and sparse coding). Low level features can be directly extracted from images or videos, while middle/high-level features are constructed upon low-level features, and are designed to enhance the capability of categorization systems based on different considerations (e.g., guaranteeing the domain-invariance and improving the discriminative power). This thesis focuses on the study of visual feature learning. Challenges that remain in designing visual features lie in intra-class variation, occlusions, illumination and view-point changes and insufficient prior knowledge. To address these challenges, I present several visual feature learning methods, where these methods cover the following sub-topics: (i) I start by introducing a segmentation-based object recognition system. (ii) When training data are insufficient, I seek data from other resources, which include images or videos in a different domain, actions captured from a different viewpoint and information in a different media form. In order to appropriately transfer such resources into the target categorization system, four transfer learning-based feature learning methods are presented in this section, where both cross-view, cross-domain and cross-modality scenarios are addressed accordingly. (iii) Finally, I present a random-forest based feature fusion method for multi-view action recognition

Learning the Language of Chemical Reactions – Atom by Atom. Linguistics-Inspired Machine Learning Methods for Chemical Reaction Tasks

Over the last hundred years, not much has changed how organic chemistry is conducted. In most laboratories, the current state is still trial-and-error experiments guided by human expertise acquired over decades. What if, given all the knowledge published, we could develop an artificial intelligence-based assistant to accelerate the discovery of novel molecules? Although many approaches were recently developed to generate novel molecules in silico, only a few studies complete the full design-make-test cycle, including the synthesis and the experimental assessment. One reason is that the synthesis part can be tedious, time-consuming, and requires years of experience to perform successfully. Hence, the synthesis is one of the critical limiting factors in molecular discovery. In this thesis, I take advantage of similarities between human language and organic chemistry to apply linguistic methods to chemical reactions, and develop artificial intelligence-based tools for accelerating chemical synthesis. First, I investigate reaction prediction models focusing on small data sets of challenging stereo- and regioselective carbohydrate reactions. Second, I develop a multi-step synthesis planning tool predicting reactants and suitable reagents (e.g. catalysts and solvents). Both forward prediction and retrosynthesis approaches use black-box models. Hence, I then study methods to provide more information about the models’ predictions. I develop a reaction classification model that labels chemical reaction and facilitates the communication of reaction concepts. As a side product of the classification models, I obtain reaction fingerprints that enable efficient similarity searches in chemical reaction space. Moreover, I study approaches for predicting reaction yields. Lastly, after I approached all chemical reaction tasks with atom-mapping independent models, I demonstrate the generation of accurate atom-mapping from the patterns my models have learned while being trained self-supervised on chemical reactions. My PhD thesis’s leitmotif is the use of the attention-based Transformer architecture to molecules and reactions represented with a text notation. It is like atoms are my letters, molecules my words, and reactions my sentences. With this analogy, I teach my neural network models the language of chemical reactions - atom by atom. While exploring the link between organic chemistry and language, I make an essential step towards the automation of chemical synthesis, which could significantly reduce the costs and time required to discover and create new molecules and materials