We thank all reviewers for their constructive and helpful comments. Reviewer 1: Regarding runtime evaluation, what we called the "wall clock time" is the sum of the GPU time and the CPU time, and the reported time to "run the neural net on its own" is the GPU time. We will revise our paper to include this discussion. We have filled in this gap in the literature for flow models. ANS for autoregressive models, which are slow for decoding.

Below, we present the pseudo-code for DQN Pro. Pro is minimal (highlighted in gray). Sticky actions True Optimizer Adam Kingma & Ba (2015) Network architecture Nature DQN network Mnih et al. (2015) Random seeds { 0, 1, 2, 3, 4 } Rainbow hyper-parameters (shared) Batch size 64 Other Config file rainbow_aaai.gin Theorem 2. Consider the PMPI algorithm specified by: We make two assumptions: 1. we assume error in policy evaluation step, as already stated in equation (4). All results are averaged over 5 independent seeds.

Breakthroughs, discoveries, and DIY tips sent every weekday. A few years ago I upgraded from gas to induction. This sentence might confuse you. Gas stoves have a reputation as being the best, mostly because of marketing, so you might think I'm only saying I "upgraded" to induction because of environmental conviction. And I'll admit using less energy motivated the switch (I like saving money) but efficiency alone is not why I'm saying that induction is better.

Breakthroughs, discoveries, and DIY tips sent every weekday. There are plenty of negative stereotypes about games and gamers. And it's true that focusing on gaming to the detriment of all else will have negative effects--there's a reason that the World Health Organization recognizes video game addiction as a mental health condition. In the 50 years since Atari unleashed Pong on the world, there's been plenty of research on the effects of video games on our brains, and it's not all bad. Here are a few of the potential benefits of gaming, according to research. A research review published in American Psychologist in 2013 by Isabela Granic, Adam Lobel, and Rutger C. M. E. Engels at Radboud University in Nijmegen, the Netherlands, looked at decades of research and highlighted the various benefits found in gaming.

In federated learning (FL), model aggregation is a critical step by which multiple clients share their knowledge with one another. However, it is also widely recognized that the aggregated model, when sent back to each client, performs poorly on local data until after several rounds of local training. This temporary performance drop can potentially slow down the convergence of the FL model. Most research in FL regards this performance drop as an inherent cost of knowledge sharing among clients and does not give it special attention. While some studies directly focus on designing techniques to alleviate the issue, an in-depth investigation of the reasons behind this performance drop has yet to be conducted.To address this gap, we conduct a layer-peeled analysis of model aggregation across various datasets and model architectures. Our findings reveal that the performance drop can be attributed to two major consequences of the aggregation process: (1) it disrupts feature variability suppression in deep neural networks (DNNs), and (2) it weakens the coupling between features and subsequent parameters.Based on these findings, we propose several simple yet effective strategies to mitigate the negative impacts of model aggregation while still enjoying the benefit it brings. To the best of our knowledge, our work is the first to conduct a layer-peeled analysis of model aggregation, potentially paving the way for the development of more effective FL algorithms.

Canon has unveiled its most important camera in years -- the EOS R1 mirrorless. Launched alongside the 45-megapixel R5 II, it's the company's new flagship designed to replace the 1DX Mark III DSLR and help Canon maintain its leadership in the pro sports photography field. The R1 is all about speed, with the stacked sensor allowing 40 fps RAW bursts with continuous autofocus. Other features are designed to help nail crucial shots, including pre-capture, eye-tracking AF and sports-specific settings. At the same time, it should be great for video, thanks to its support for 6K RAW capture.

A parent asked a question in a private Facebook group in April 2024: Does anyone with a child who is both gifted and disabled have any experience with New York City public schools? The parent received a seemingly helpful answer that laid out some characteristics of a specific school, beginning with the context that "I have a child who is also 2e," meaning twice exceptional. On a Facebook group for swapping unwanted items near Boston, a user looking for specific items received an offer of a "gently used" Canon camera and an "almost-new portable air conditioning unit that I never ended up using." Both of these responses were lies. That child does not exist and neither do the camera or air conditioner.

Deceptive reviews are becoming increasingly common, especially given the increase in performance and the prevalence of LLMs. While work to date has addressed the development of models to differentiate between truthful and deceptive human reviews, much less is known about the distinction between real reviews and AI-authored fake reviews. Moreover, most of the research so far has focused primarily on English, with very little work dedicated to other languages. In this paper, we compile and make publicly available the MAiDE-up dataset, consisting of 10,000 real and 10,000 AI-generated fake hotel reviews, balanced across ten languages. Using this dataset, we conduct extensive linguistic analyses to (1) compare the AI fake hotel reviews to real hotel reviews, and (2) identify the factors that influence the deception detection model performance. We explore the effectiveness of several models for deception detection in hotel reviews across three main dimensions: sentiment, location, and language. We find that these dimensions influence how well we can detect AI-generated fake reviews.

Ethical hackers are using artificial intelligence tools to find bugs in computer code and claim rewards worth thousands of dollars. However, others are using the same AI tools to generate realistic but nonsensical bug reports, making it hard to know which reports to trust. Bug bounty schemes offer financial rewards for people who can find flaws in software.