From veneers to abrasive toothpastes, a perfect smile can hide cavities and cause other problems. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Your teeth probably don't look like a movie star's, and that might be a good thing. Breakthroughs, discoveries, and DIY tips sent six days a week. However, in recent years, critics have pointed out that one thing can immediately dispel historical accuracy: actors' blindingly white, perfect teeth.

Dynamic time warping (DTW) is a fundamental technique in time series analysis for comparing one curve to another using a flexible time-warping function. However, it was designed to compare a single pair of curves. In many applications, such as in metabolomics and image series analysis, alignment is simultaneously needed for multiple pairs. Because the underlying warping functions are often related, independent application of DTW to each pair is a sub-optimal solution. Yet, it is largely unknown how to efficiently conduct a joint alignment with all warping functions simultaneously considered, since any given warping function is constrained by the others and dynamic programming cannot be applied.

We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a parametric baseline hazard, and uses a Gaussian process to model variations away from it nonparametrically, as well as dependence on covariates. As opposed to many other methods in survival analysis, our framework does not impose unnecessary constraints in the hazard rate or in the survival function. Furthermore, our model handles left, right and interval censoring mechanisms common in survival analysis. We propose a MCMC algorithm to perform inference and an approximation scheme based on random Fourier features to make computations faster. We report experimental results on synthetic and real data, showing that our model performs better than competing models such as Cox proportional hazards, ANOVA-DDP and random survival forests.

Plus: An unauthorized group has reportedly accessed Anthropic's Mythos. What actually matters in AI right now? It's getting harder to tell amid the constant launches, hype, and warnings. To cut through the noise, reporters and editors have distilled years of analysis into a new essential guide: the 10 Things That Matter in AI Right Now . The list builds on our annual 10 Breakthrough Technologies, but takes a wider view of the ideas, topics, and research shaping AI, spotlighting the trends and breakthroughs shaping the world. We'll be unpacking one item from the list each day here in The Download, explaining what it means and why it matters.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

We study an online decision making problem where on each round a learner chooses a list of items based on some side information, receives a scalar feedback value for each individual item, and a reward that is linearly related to this feedback. These problems, known as contextual semibandits, arise in crowdsourcing, recommendation, and many other domains. This paper reduces contextual semibandits to supervised learning, allowing us to leverage powerful supervised learning methods in this partial-feedback setting. Our first reduction applies when the mapping from feedback to reward is known and leads to a computationally efficient algorithm with near-optimal regret. We show that this algorithm outperforms state-of-the-art approaches on real-world learning-to-rank datasets, demonstrating the advantage of oracle-based algorithms. Our second reduction applies to the previously unstudied setting when the linear mapping from feedback to reward is unknown. Our regret guarantees are superior to prior techniques that ignore the feedback.

In this work, we propose an infinite restricted Boltzmann machine (RBM), whose maximum likelihood estimation (MLE) corresponds to a constrained convex optimization. We consider the Frank-Wolfe algorithm to solve the program, which provides a sparse solution that can be interpreted as inserting a hidden unit at each iteration, so that the optimization process takes the form of a sequence of finite models of increasing complexity. As a side benefit, this can be used to easily and efficiently identify an appropriate number of hidden units during the optimization. The resulting model can also be used as an initialization for typical state-of-the-art RBM training algorithms such as contrastive divergence, leading to models with consistently higher test likelihood than random initialization.

Probabilistic inference serves as a popular model for neural processing. It is still unclear, however, how approximate probabilistic inference can be accurate and scalable to very high-dimensional continuous latent spaces. Especially as typical posteriors for sensory data can be expected to exhibit complex latent dependencies including multiple modes. Here, we study an approach that can efficiently be scaled while maintaining a richly structured posterior approximation under these conditions. As example model we use spike-and-slab sparse coding for V1 processing, and combine latent subspace selection with Gibbs sampling (selectand-sample).

McDonald's boss on abuse claims: 'I don't want to talk about the past' The boss of McDonald's UK and Ireland has said she doesn't want to talk about the past when asked about allegations of abuse at the fast-food chain. Lauren Schultz told the BBC what had happened in recent years was unacceptable but said we have drawn a line under it. A BBC investigation in 2023 heard from more than 100 McDonald's workers in the UK claiming they faced a toxic culture of sexual assault, harassment, racism, and bullying. Last year, staff said they still faced sexual abuse and harassment. The UK equality watchdog agreed tougher measures with the company to protect staff in November, including new sexual harassment training.