Continual Unsupervised Representation Learning

Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially. Prior art in the field has largely considered supervised or reinforcement learning tasks, and often assumes full knowledge of task labels and boundaries. In this work, we propose an approach (CURL) to tackle a more general problem that we will refer to as unsupervised continual learning. The focus is on learning representations without any knowledge about task identity, and we explore scenarios when there are abrupt changes between tasks, smooth transitions from one task to another, or even when the data is shuffled. The proposed approach performs task inference directly within the model, is able to dynamically expand to capture new concepts over its lifetime, and incorporates additional rehearsal-based techniques to deal with catastrophic forgetting. We demonstrate the efficacy of CURL in an unsupervised learning setting with MNIST and Omniglot, where the lack of labels ensures no information is leaked about the task. Further, we demonstrate strong performance compared to prior art in an i.i.d setting, or when adapting the technique to supervised tasks such as incremental class learning.Comment: NeurIPS 201

Model of models -- Part 1

This paper proposes a new cognitive model, acting as the main component of an AGI agent. The model is introduced in its mature intelligence state, and as an extension of previous models, DENN, and especially AKREM, by including operational models (frames/classes) and will. This model's core assumption is that cognition is about operating on accumulated knowledge, with the guidance of an appropriate will. Also, we assume that the actions, part of knowledge, are learning to be aligned with will, during the evolution phase that precedes the mature intelligence state. In addition, this model is mainly based on the duality principle in every known intelligent aspect, such as exhibiting both top-down and bottom-up model learning, generalization verse specialization, and more. Furthermore, a holistic approach is advocated for AGI designing, and cognition under constraints or efficiency is proposed, in the form of reusability and simplicity. Finally, reaching this mature state is described via a cognitive evolution from infancy to adulthood, utilizing a consolidation principle. The final product of this cognitive model is a dynamic operational memory of models and instances. Lastly, some examples and preliminary ideas for the evolution phase to reach the mature state are presented.Comment: arXiv admin note: text overlap with arXiv:2301.1355

Semi-Supervised and Unsupervised Deep Visual Learning: A Survey

State-of-the-art deep learning models are often trained with a large amountof costly labeled training data. However, requiring exhaustive manualannotations may degrade the model's generalizability in the limited-labelregime. Semi-supervised learning and unsupervised learning offer promisingparadigms to learn from an abundance of unlabeled visual data. Recent progressin these paradigms has indicated the strong benefits of leveraging unlabeleddata to improve model generalization and provide better model initialization.In this survey, we review the recent advanced deep learning algorithms onsemi-supervised learning (SSL) and unsupervised learning (UL) for visualrecognition from a unified perspective. To offer a holistic understanding ofthe state-of-the-art in these areas, we propose a unified taxonomy. Wecategorize existing representative SSL and UL with comprehensive and insightfulanalysis to highlight their design rationales in different learning scenariosand applications in different computer vision tasks. Lastly, we discuss theemerging trends and open challenges in SSL and UL to shed light on futurecritical research directions.<br

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Using Hierarchical Temporal Memory for Detecting Anomalous Network Activity

This thesis explores the nature of cyberspace and forms an argument for it as an intangible world. This research is motivated by the notion of creating intelligently autonomous cybercraft to reside in that environment and maintain domain superiority. Specifically, this paper offers 7 challenges associated with development of intelligent, autonomous cybercraft. The primary focus is an analysis of the claims of a machine learning language called Hierarchical Temporal Memory (HTM). In particular, HTM theory claims to facilitate intelligence in machines via accurate predictions. It further claims to be able to make accurate predictions of unusual worlds, like cyberspace. The research thrust of this thesis is then two fold. The primary objective is to provide supporting evidence for the conjecture that HTM implementations facilitate accurate predictions of unusual worlds. The second objective is to then lend evidence that prediction is a good indication of intelligence. A commercial implementation of HTM theory is tested as an anomaly detection system and its ability to characterize network traffic (a major component of cyberspace) as benign or malicious is evaluated. Through the course of testing the poor performance of this implementation is revealed and an independent algorithm is developed from a variant understanding of HTM theory. This alternate algorithm is independent of the realm of cyberspace and developed solely (but also in a contrived abstract world) to lend credibility to concept of using prediction as a method of testing intelligence

LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning

Abstract-We describe a cognitive architecture (LIDA) that affords attention, action selection and human-like learning intended for use in controlling cognitive agents that replicate human experiments as well as performing real-world tasks. LIDA combines sophisticated action selection, motivation via emotions, a centrally important attention mechanism, and multimodal instructionalist and selectionist learning. Empirically grounded in cognitive science and cognitive neuroscience, the LIDA architecture employs a variety of modules and processes, each with its own effective representations and algorithms. LIDA has much to say about motivation, emotion, attention, and autonomous learning in cognitive agents. In this paper we summarize the LIDA model together with its resulting agent architecture, describe its computational implementation, and discuss results of simulations that replicate known experimental data. We also discuss some of LIDA&apos;s conceptual modules, propose non-linear dynamics as a bridge between LIDA&apos;s modules and processes and the underlying neuroscience, and point out some of the differences between LIDA and other cognitive architectures. Finally, we discuss how LIDA addresses some of the open issues in cognitive architecture research