Deep learning algorithms are well-known to have a propensity for fitting the training data very well and often fit even outliers and mislabeled data points. Such fitting requires memorization of training data labels, a phenomenon that has attracted significant research interest but has not been given a compelling explanation so far. A recent work of Feldman (2019) proposes a theoretical explanation for this phenomenon based on a combination of two insights. First, natural image and data distributions are (informally) known to be long-tailed, that is have a significant fraction of rare and atypical examples. Second, in a simple theoretical model such memorization is necessary for achieving close-to-optimal generalization error when the data distribution is long-tailed.

Thank you all for your thoughtful comments; we address your concerns below. The MDL principle formalizes Occam's razor and is a We will add the discussion of such relevant studies to section 1. We will add these results and accompanying visualizations to appendix. Model (solver) MAC DAFT MAC (euler) DAFT MAC (rk4) DAFT MAC (dopri5; used in training)Time (ms) 153. We found that during evaluation, rk4 solves all the dynamics generated from CLEVR dataset.

Empirical risk minimization (ERM) may be used to generalize the result from train to test data.

NBA stars, mobsters, and marks with fat wallets are all part of an alleged ring of rigged poker games. Here's how these games are assembled, who attends, and how the purported cheating happens. Former NBA player and Portland Trail Blazers head coach Chauncey Billups (center) exits the Mark O. Hatfield United States Courthouse after his arraignment on October 23, 2025, in Portland, Oregon. To the uninitiated, the arrests of Chauncey Billups and Damon Jones last week for allegations of involvement in rigged illegal poker games may have appeared like an unusual collision of worlds. How could prosecutors claim that former NBA players (one a current coach), professional gamblers, and even mafia members all ended up rubbing elbows as part of the same high-tech cheating scheme that allegedly began in 2019 and ran for several years?

Empirical risk minimization (ERM) may be used to generalize the result from train to test data.

Thank you all for your thoughtful comments; we address your concerns below. The MDL principle formalizes Occam's razor and is a We will add the discussion of such relevant studies to section 1. We will add these results and accompanying visualizations to appendix. Model (solver) MAC DAFT MAC (euler) DAFT MAC (rk4) DAFT MAC (dopri5; used in training)Time (ms) 153. We found that during evaluation, rk4 solves all the dynamics generated from CLEVR dataset.

Corey Feldman discusses his movie "The Birthday," which wrapped in 2004. "The Goonies" star Corey Feldman is concerned that the rise of artificial intelligence could ruin the "magic" of Hollywood filmmaking. In a new interview with Fox News Digital, the entertainer talked about his decades of being part of the film industry and what he thinks of it today compared to how it was when he was starring in beloved 80s classics like "Goonies," "The Lost Boys" and "The Burbs." When asked if he believes modern Hollywood can still conjure up the same "magic" that led to the creation of these iconic films, he said he wasn't so sure. "Well, I share the opinion that there is a lot of the magic that's been lost because of A.I., because of CGI, because of, you know, these things kind of taking over from the good stories, the great characters that we draw, the great writing," Feldman said.

What is a flower, if you can't smell? The latest generation of artificial intelligence models seem to have a human-level understanding of the world, but it turns out that their lack of sensory information – and a body – places limits on how well they can comprehend concepts like a flower or humour. Qihui Xu at the Ohio State University and her colleagues asked both humans and large language models (LLMs) about their understanding of almost 4500 words – everything from "flower" and "hoof" to "humorous" and "swing." The participants and AI models were asked to rate each word for a variety of aspects, such as the level of emotional arousal they conjure up, or their links to senses and physical interaction with different parts of the body. The goal was to see how LLMs, including OpenAI's GPT-3.5 and GPT-4 and Google's PaLM and Gemini, compared with humans in their rankings.