The performance of learning-based object detection algorithms, which attempt to both classify and locate objects within images, is determined largely by the quality are prev of alent: the annotated objects that dataset are incorrectly used for training.

Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Four experts spoke about the importance of investing in early childhood education in a TIME100 Talk moderated by TIME executive editor Nikhil Kumar at the TIME100 Summit in New York City on April 22. The panel featured Amanda Devercelli, senior education specialist at the World Bank; Emily Gustafsson-Wright, senior fellow in the global economy and development program at the Brookings Institution; Maria Adelaida Lopez, an artist and educator; and Dr. Jack Shonkoff, a professor of child health and development and founding director of the Center on the Developing Child at Harvard University.

Regularization is typically understood as improving generalization by altering the landscape of local extrema to which the model eventually converges. Deep neural networks (DNNs), however, challenge this view: We show that removing regularization after an initial transient period has little effect on generalization, even if the final loss landscape is the same as if there had been no regularization. In some cases, generalization even improves after interrupting regularization. Conversely, if regularization is applied only after the initial transient, it has no effect on the final solution, whose generalization gap is as bad as if regularization never happened. This suggests that what matters for training deep networks is not just whether or how, but when to regularize. The phenomena we observe are manifest in different datasets (CIFAR-10, CIFAR-100, SVHN, ImageNet), different architectures (ResNet-18, All-CNN), different regularization methods (weight decay, data augmentation, mixup), different learning rate schedules (exponential, piece-wise constant). They collectively suggest that there is a critical period'' for regularizing deep networks that is decisive of the final performance. More analysis should, therefore, focus on the transient rather than asymptotic behavior of learning.

Regularization is typically understood as improving generalization by altering the landscape of local extrema to which the model eventually converges.

This study investigates the aesthetic experience and educational value of collaborative artmaking with generative artificial intelligence (AI) among young learners and art students. Based on a survey of 112 participants, we examine how human creators renegotiate their roles, how conventional notions of originality are challenged, how the creative process is transformed, and how aesthetic judgment is formed in human-AI co-creation. Empirically, participants generally view AI as a partner that stimulates ideation and expands creative boundaries rather than a passive tool, while simultaneously voicing concerns about stylistic homogenization and the erosion of traditional authorship. Theoretically, we synthesize Dewey's aesthetics of experience, Ihde's postphenomenology, and actor-network theory (ANT) into a single analytical framework to unpack the dynamics between human creators and AI as a non-human actant. Findings indicate (i) a fluid subjectivity in which creators shift across multiple stances (director, dialogic partner, discoverer); (ii) an iterative, dialogic workflow (intent-generate-select-refine) that centers critical interpretation; and (iii) an educational value shift from technical skill training toward higher-order competencies such as critical judgment, cross-modal ideation, and reflexivity. We argue that arts education should cultivate a critical co-creation stance toward technology, guiding learners to collaborate with AI while preserving human distinctiveness in concept formation, judgment, and meaning-making.

Weaknesses: I have many reservation against the claims of the paper. I would appreciate it if the authors can clarify some of these issues during their rebuttal. First, the proof of their main theorem about logistic regression has many issues. One key issue is that the authors make assumptions within the proof that are not clearly stated or justified upfront. For example, in Line 440 in the supplementary materials, the proof assumes that theta Tv .1.

The paper studies the following interesting phenomenon (observed in the previous literature): when trained on the dataset with incorrectly labeled points (i.e. "label noise"), DNNs first learn the benign ("correctly labeled") points and once this is done they start "memorizing" the noisy points. It was previously shown in the literature (empirically) that the second "memorization" phase hurts the generalization. The authors make 2 Contributions: (Contribution 1) They demonstrate (empirically and theoretically) that similar phenomenon can be observed in the simpler setting of the over-parametrized (dimensionality number of points) linear two-class logistic regression, when the class distributions are isotropic Gaussian with fixed means \pm mu and vanishing variance (see Theorem 1 and Figure A.1). (Contribution 2) Motivated by the theory of contribution 1, the authors propose a novel regularizer. When used in the vanilla DNN training with the cross-entropy loss, this regularizer successfully prevents the networks from falling to the "memorization phase" (as evidenced by Figure 1). All the reviewers agree that the topic and the focus of this paper is very timely.

The paper is well-written and the authors are clear about their claims. The idea of critical periods during training with reference to regularization is interesting. If true, this would give a different way to think about generalization. The authors have performed a number of experiments with different configurations. Although, there are deficiencies mentioned below.

The paper describes how regularization matters in different ways during different parts of the training processs, i.e., the timing is important for the regularization to be effective. Reviewers have several suggestions, which should be incorporated to the extent possible, but the ideas/results shoule be of interest to members of the community.

We propose a novel framework to perform classification via deep learning in the presence of noisy annotations. When trained on noisy labels, deep neural networks have been observed to first fit the training data with clean labels during an "early learning" phase, before eventually memorizing the examples with false labels. We prove that early learning and memorization are fundamental phenomena in high-dimensional classification tasks, even in simple linear models, and give a theoretical explanation in this setting. Motivated by these findings, we develop a new technique for noisy classification tasks, which exploits the progress of the early learning phase. In contrast with existing approaches, which use the model output during early learning to detect the examples with clean labels, and either ignore or attempt to correct the false labels, we take a different route and instead capitalize on early learning via regularization. There are two key elements to our approach.