Unsupervised Learning of Individuals and Categories from Images

Motivated by the existence of highly selective, sparsely firing cells observed in the human medial temporal lobe (MTL), we present an unsupervised method for learning and recognizing object categories from unlabeled images. In our model, a network of nonlinear neurons learns a sparse representation of its inputs through an unsupervised expectation-maximization process. We show that the application of this strategy to an invariant feature-based description of natural images leads to the development of units displaying sparse, invariant selectivity for particular individuals or image categories much like those observed in the MTL data

Active Learning in Physics: From 101, to Progress, and Perspective

Active Learning (AL) is a family of machine learning (ML) algorithms that predates the current era of artificial intelligence. Unlike traditional approaches that require labeled samples for training, AL iteratively selects unlabeled samples to be annotated by an expert. This protocol aims to prioritize the most informative samples, leading to improved model performance compared to training with all labeled samples. In recent years, AL has gained increasing attention, particularly in the field of physics. This paper presents a comprehensive and accessible introduction to the theory of AL reviewing the latest advancements across various domains. Additionally, we explore the potential integration of AL with quantum ML, envisioning a synergistic fusion of these two fields rather than viewing AL as a mere extension of classical ML into the quantum realm.Comment: 15 page

Artificial Intelligence in Process Engineering

In recent years, the field of Artificial Intelligence (AI) is experiencing a boom, caused by recent breakthroughs in computing power, AI techniques, and software architectures. Among the many fields being impacted by this paradigm shift, process engineering has experienced the benefits caused by AI. However, the published methods and applications in process engineering are diverse, and there is still much unexploited potential. Herein, the goal of providing a systematic overview of the current state of AI and its applications in process engineering is discussed. Current applications are described and classified according to a broader systematic. Current techniques, types of AI as well as pre- and postprocessing will be examined similarly and assigned to the previously discussed applications. Given the importance of mechanistic models in process engineering as opposed to the pure black box nature of most of AI, reverse engineering strategies as well as hybrid modeling will be highlighted. Furthermore, a holistic strategy will be formulated for the application of the current state of AI in process engineering

A survey on the semi supervised learning paradigm in the context of speech emotion recognition

The area of Automatic Speech Emotion Recognition has been a hot topic for researchers for quite some time now. The recent breakthroughs on technology in the field of Machine Learning open up doors for multiple approaches of many kinds. However, some concerns have been persistent throughout the years where we highlight the design and collection of data. Proper annotation of data can be quite expensive and sometimes not even viable, as specialists are often needed for such a complex task as emotion recognition. The evolution of the semi supervised learning paradigm tries to drag down the high dependency on labelled data, potentially facilitating the design of a proper pipeline of tasks, single or multi modal, towards the final objective of the recognition of the human emotional mental state. In this paper, a review of the current single modal (audio) Semi Supervised Learning state of art is explored as a possible solution to the bottlenecking issues mentioned, as a way of helping and guiding future researchers when getting to the planning phase of such task, where many positive aspects from each piece of work can be drawn and combined.This work has been supported by FCT - Fundação para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/202