Despite their ability to represent highly expressive functions, deep learning models seem to find simple solutions that generalize surprisingly well. Spectral bias -- the tendency of neural networks to prioritize learning low frequency functions -- is one possible explanation for this phenomenon, but so far spectral bias has primarily been observed in theoretical models and simplified experiments. In this work, we propose methodologies for measuring spectral bias in modern image classification networks on CIFAR-10 and ImageNet. We find that these networks indeed exhibit spectral bias, and that interventions that improve test accuracy on CIFAR-10 tend to produce learned functions that have higher frequencies overall but lower frequencies in the vicinity of examples from each class. This trend holds across variation in training time, model architecture, number of training examples, data augmentation, and self-distillation. We also explore the connections between function frequency and image frequency and find that spectral bias is sensitive to the low frequencies prevalent in natural images. On ImageNet, we find that learned function frequency also varies with internal class diversity, with higher frequencies on more diverse classes. Our work enables measuring and ultimately influencing the spectral behavior of neural networks used for image classification, and is a step towards understanding why deep models generalize well.

Eq. (3), Eq. (5) and its model details) is consistent with the target task. The reward and constraints are reasonably designed. The experimental setting is remarkable (especially the Online Evaluation by simulator and the four proposed evaluation metrics) and the results are positive. However, this paper still has the following minor issues.

SUMMARY: The paper studies the problem of testing whether a graph is epsilon-far from a kNN graph, where epsilon-far means that at least epsilon-fraction of the edges need to be changed in order to make the graph a kNN graph. The paper presents an algorithm with an upper bound of O(\sqrt{n}*k 2/\epsilon 2) number of edge/vertex queries and a lower bound of \Omega(\sqrt{n}). I guess "\omega" should be "p" 2. The result of Proposition 12 is interesting which bounds the number of points that can be in the kNN set of a particular point. The bound is k times the known bound for 1-NN. I wonder if this could be tightened somehow.

In 2022 the covid-19 pandemic will continue to impact our lives in many ways. This means that we will continue to see an accelerated rate of digitization and virtualization of business and society. However, as we move into a new year, the need for sustainability, ever-increasing data volumes, and increasing compute and network speeds will begin to regain their status as the most important drivers of digital transformation. For many individuals and organizations, the most important lesson of the last two years or so has been that truly transformative change isn't as difficult to implement as might have once been thought, if the motivation is there! As a society, we will undoubtedly continue to harness this newfound openness to flexibility, agility, and innovative thinking, as the focus shifts from merely attempting to survive in a changing world to thriving in it. With that in mind, here are my predictions for the specific trends that are likely to have the biggest impact in 2022.

In 2022 the covid-19 pandemic will continue to impact our lives in many ways. This means that we will continue to see an accelerated rate of digitization and virtualization of business and society. However, as we move into a new year, the need for sustainability, ever-increasing data volumes, and increasing compute and network speeds will begin to regain their status as the most important drivers of digital transformation. For many individuals and organizations, the most important lesson of the last two years or so has been that truly transformative change isn't as difficult to implement as might have once been thought, if the motivation is there! As a society, we will undoubtedly continue to harness this newfound openness to flexibility, agility, and innovative thinking, as the focus shifts from merely attempting to survive in a changing world to thriving in it.

In 2022 the covid-19 pandemic will continue to impact our lives in many ways. This means that we will continue to see an accelerated rate of digitization and virtualization of business and society. However, as we move into a new year, the need for sustainability, ever-increasing data volumes, and increasing compute and network speeds will begin to regain their status as the most important drivers of digital transformation. For many individuals and organizations, the most important lesson of the last two years or so has been that truly transformative change isn't as difficult to implement as might have once been thought, if the motivation is there! As a society, we will undoubtedly continue to harness this newfound openness to flexibility, agility, and innovative thinking, as the focus shifts from merely attempting to survive in a changing world to thriving in it. With that in mind, here are my predictions for the specific trends that are likely to have the biggest impact in 2022.

In this guide, we'll take a practical, concise tour through modern machine learning algorithms. While other such lists exist, they don't really explain the practical tradeoffs of each algorithm, which we hope to do here. We'll discuss the advantages and disadvantages of each algorithm based on our experience. Categorizing machine learning algorithms is tricky, and there are several reasonable approaches; they can be grouped into generative/discriminative, parametric/non-parametric, supervised/unsupervised, and so on. However, from our experience, this isn't always the most practical way to group algorithms.

Toward thi: end, knowledge acquisition is sometimes considered a nccessary burden, carried out under protest so that one can gel on with t,he study of cognitive processes in problem solving In this article we argue that, t,he two activities-knowledge acqisitSion and cognitive modeling-are necessarily interwoven, and provide interesting opportunities when t,akcr Logether. Knowledge acquisition shapes cognit,ive modeling because operatzonnl knowledge cont,ains assurnpt,ions nnc directions for its use, t,hat, is, a.11 implicit, processing model In return, problem solving models can profoundly shape knowledge acquisition by providing a framework for the articulation and creation of domain expertise This int,rodllces the theme of this article, t,hat, one can engzneer bodie: of knowledge for various purposes, such as learnability, To the knowledge engineering slogan "knowledge is power," we add "knowledge is an artifact,, worthy of design " The organization of this article is as follows: We first consider the pract,ice of "cognitive advantage " III the t,hird section we suggest some A Shift in Viewpoint from Experts to Clans Over the past decade t,hcrc have been trcmcndous advances in the fabrication of integrated circuits (Robinson, 1980a). Circuits have become smaller and manufacturing costs have dropped dramatically. Design is becoming the dominant cost (Robinson, 19801) with the current round of miniaturization, which goes by the name of VLSI for very large scale integration. This is leading to a substantial int,crest in undcrst,anding design processes.

Empirical evidence suggests that searching deeper in game trees using the minimax propagation rule usually improves the quality of decisions significantly. However, despite many recent theoretical analyses of the effects of minimax look-ahead, however, this phenomenon has still not been convincingly explained. Instead, much attention has been given to socalled pathological behavior, which occurs under certain assumptions. This article supports the view that pathology is a direct result of these underlying theoretical assumptions. Pathology does not occur in practice, because these assumptions do not apply in realistic domains.

Enabling organizations to capture, share, and apply the collective experience and know-how of their people is seen as fundamental to competing in the knowledge economy. As a result, there has been a wave of enthusiasm and activity centered on knowledge management. To make progress in this area, issues of technology, process, people, and content must be addressed. In this article, we develop a road map for knowledge management. It begins with an assessment of the current state of the practice, using examples drawn from our experience at Schlumberger.