The paper proposes a continual learning approach for CNN models. This is achieved through spatial and channel-wise calibration modules, one for each new task. These calibration modules are introduced between each pair of consecutive layers in the original base model. The base model is learnt on the first task, and training data from the subsequent tasks is used to learn the calibration modules. Extensive experiments show the superiority of the proposed method in terms of accuracies, with minimal computation and storage overhead. It is important to emphasize that the proposed approach requires task labels in the test phase.

The paper makes conceptual, algorithmic and theoretical contributions to optimization in multi armed bandits. It introduces a new problem motivated by settings where there is a testing phase with a cost structure proportional to the utility derived from playing each alternative, and gives a novel algorithm with a sample complexity bound for identifying a near-best arm. All the reviewers agree that the paper's contributions as above are significant. The only concern expressed was about the potentially narrow scope of the problem formulation, but the author feedback has helped in clarifying this aspect. It appears that the learning problem studied here, i.e., its cost structure specifically, could emerge in settings beyond e-commerce and advertising, and is likely to be of broader interest.

This paper provides a nice and clean characterization of a decentralized learning problem. The result is perhaps unsurprising in its form, but the analysis is far from trivial. There are some nontrivial assumptions for their results to hold which perhaps limit the scope of this result but do suggest interesting avenues for future research in this increasingly important area. Overall, this is a solid contribution and should be of interest to NeurIPS attendees who work in optimization and distributed systems.

While this algorithm is specifically designed for detectors, Miech et al 2019 used unsupervised NCE losses (much like the ones in this paper) in order to understand the natural language descriptions associated with videos; the algorithm presented here seems like the most straightforward extension of this idea to bounding boxes. Little attention is given to demonstrating that the use of bounding boxes fundamentally changes the problem. Update The rebuttal addresses the following point regarding the accuracy of the evaluation. I had misunderstood the annotations that are available with epic kitchens, and therefore I am changing my review. I would encourage the authors to clarify the writing regarding what's available with epic kitchens.

Post-rebuttal: I am satisfied with the rebuttal. I am interested to know more about "One reason why such rates are common in online learning is the connection of the sequential Rademacher complexity with uniform convergence of martingale difference sequences in the corresponding Banach space (see [2] for details)." If the paper is accepted and space is allowed, I suggest to elaborate on this more thoroughly.

One of the main issues of this paper is clarity, however. We trust that you will work very hard to improve clarity for the final submission, as suggested in the reviews.

Summary and Contributions: Post-rebuttal comments Thank you for the comments. I am happy with the response and would recommend including the paragraph (stabilization vs ERM) from the rebuttal into the final version of the paper. It might be interesting as an open problem for future work. Operator valued kernels provide a theoretical framework for modelling learning problems that map functions to functions. A potential shortcoming of this framework is the fact that kernels need to be positive definite.

Design educators are finding ways to support students in skillfully using Generative Artificial Intelligence (GenAI) tools in their practices while encouraging the critical scrutiny of ethical and social issues around these technologies. However, the problem of environmental sustainability remains largely unaddressed. There is a lack of both resources to grasp the environmental costs of genAI in education and a lack of shared practices around the issue. This work contributes filling this gap by counting the energy costs of using genAI in design education and critically reflecting on the impact of these costs. We leverage the image data collected during a genAI workshop for designers held in 2023 with 49 students, to calculate the energy costs of these types of activities. The results reveal that a genAI workshop for designers can easily double the energy costs associated with students' use of computers, countering the efforts of educational institutions to minimize their energy expenditure. We critically reflect on this finding to distill a set of five alternative stances, with related actions, that can support a conscious use of genAI in design education, while respecting individual positions. The work contributes to the field of design pedagogy, and education more broadly, by bringing together ways for educators to reflect on their practices and informing the future development of educational programs around genAI.

This research assesses the effectiveness of state-of-the-art large language models (LLMs), including ChatGPT, Llama, Aya, Jais, and ACEGPT, in the task of Arabic automated essay scoring (AES) using the AR-AES dataset. It explores various evaluation methodologies, including zero-shot, few-shot in-context learning, and fine-tuning, and examines the influence of instruction-following capabilities through the inclusion of marking guidelines within the prompts. A mixed-language prompting strategy, integrating English prompts with Arabic content, was implemented to improve model comprehension and performance. Among the models tested, ACEGPT demonstrated the strongest performance across the dataset, achieving a Quadratic Weighted Kappa (QWK) of 0.67, but was outperformed by a smaller BERT-based model with a QWK of 0.88. The study identifies challenges faced by LLMs in processing Arabic, including tokenization complexities and higher computational demands. Performance variation across different courses underscores the need for adaptive models capable of handling diverse assessment formats and highlights the positive impact of effective prompt engineering on improving LLM outputs. To the best of our knowledge, this study is the first to empirically evaluate the performance of multiple generative Large Language Models (LLMs) on Arabic essays using authentic student data.

This paper presents a survey based on Kasunic's survey research methodology to identify the criteria used by Machine Learning (ML) experts to evaluate Named Entity Recognition (NER) tools and frameworks. Comparison and selection of NER tools and frameworks is a critical step in leveraging NER for Information Retrieval to support the development of Clinical Practice Guidelines. In addition, this study examines the main challenges faced by ML experts when choosing suitable NER tools and frameworks. Using Nunamaker's methodology, the article begins with an introduction to the topic, contextualizes the research, reviews the state-of-the-art in science and technology, and identifies challenges for an expert survey on NER tools and frameworks. This is followed by a description of the survey's design and implementation. The paper concludes with an evaluation of the survey results and the insights gained, ending with a summary and conclusions.