Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks

In the realm of machine and deep learning regression tasks, the role of effective feature engineering (FE) is pivotal in enhancing model performance. Traditional approaches of FE often rely on domain expertise to manually design features for machine learning models. In the context of deep learning models, the FE is embedded in the neural network's architecture, making it hard for interpretation. In this study, we propose to integrate symbolic regression (SR) as an FE process before a machine learning model to improve its performance. We show, through extensive experimentation on synthetic and real-world physics-related datasets, that the incorporation of SR-derived features significantly enhances the predictive capabilities of both machine and deep learning regression models with 34-86% root mean square error (RMSE) improvement in synthetic datasets and 4-11.5% improvement in real-world datasets. In addition, as a realistic use-case, we show the proposed method improves the machine learning performance in predicting superconducting critical temperatures based on Eliashberg theory by more than 20% in terms of RMSE. These results outline the potential of SR as an FE component in data-driven models

A review of the effects of colour and light on non-image function in humans

This paper reviews current knowledge on non-image-forming aspects of vision. Developments in the last 20 years have included the discovery of a fifth class of human visual pigment (melanopsin), in addition to the three classes of photopsin to be found in the cones and rhodopsin in the rods in the human retina. Melanopsin is found in a small number of retinal ganglion cells which then, in addition to receiving input from rods and cones, are intrinsically photosensitive. These retinal ganglion cells send their input primarily to the hypothalamus, where they help to regulate the circadian system (daily rhythms of sleep patterns, body temperature, heart rate, etc.). The discovery of the anatomical basis of non-image-forming vision has led to a great deal of research into the effects of light on sleep, depression and mood, retinal photodamage and well-being, amongst other factors. Given that recent technological innovations in LED lighting now give us greater control over environmental lighting, it is timely to review the non-visual effects of light in humans in order to inform lighting design in the future

Examining machine learning for adaptable end-to-end information extraction systems

All components of a typical IE system have been the object of some machine learning research, motivated by the need to improve time taken to transfer to new domains. In this paper we survey such methods and assess to what extent they can help create a complete IE system that can be easily adapted to new domains. We also lay out a general prescription for an IE system in a new domain, employing existing components and technologies where possible. The goal is a system that can be adapted to a new domain with minimal human intervention (say by someone who may be a domain expert but need not be a computational linguist). We propose research directions for automating the process further, reducing the need for hand-tagged training data by relying on biases intrinsic to the information extraction task, and employing boot-strapping and active learning

This work was carried out under the supervision of Dr. Ido Dagan

ii This thesis introduces the applied notion of textual entailment as a generic empiri-cal task that captures major semantic inferences across many applications. Textual entailment addresses semantic inference as a direct mapping between language expres-sions and abstracts the common semantic inferences as needed for text based Natural Language Processing applications. We define the task and describe the creation of a benchmark dataset for textual entailment along with proposed evaluation measures. This dataset was the basis for the PASCAL Recognising Textual Entailment (RTE) Challenge. We further describe how textual entailment can be approximated and modeled at the lexical level and propose a lexical reference subtask and a correspond-ingly derived dataset. The thesis further proposes a general probabilistic setting that casts the applied notion of textual entailment in probabilistic terms. We suggest that the proposed setting may provide a unifying framework for modeling uncertain semantic inferences from texts. In addition, we describe two lexical models demonstrating the applicabil-ity of the probabilistic setting. Although our proposed models are relatively simple, as they do not rely on syntactic or other deeper analysis, they nevertheless achieved competitive results on the pascal rte challenge. Finally, the thesis presents a novel acquisition algorithm to identify lexical entail-ment relations from a single corpus focusing on the extraction of verb paraphrases. Most previous approaches detect individual paraphrase instances within a pair (or iii set) of comparable corpora, each of them containing roughly the same information, and rely on the given substantial level of correspondence of such corpora. We present a novel method that successfully detects isolated paraphrase instances within a single corpus without relying on any a-priori structure and information. Our instance based approach seems to address some of the drawbacks of distributional similarity based methods, in particular by providing a consistent scoring scale across different words. i