Atpresent, however, there are few flexible and provably exchangeable deep generative models to solve this problem.

We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations. Our model is provably exchangeable, meaning that the joint distribution over observations is invariant under permutation: this property lies at the heart of Bayesian inference. The model does not require variational approximations to train, and new samples can be generated conditional on previous samples, with cost linear in the size of the conditioning set. The advantages of our architecture are demonstrated on learning tasks that require generalisation from short observed sequences while modelling sequence variability, such as conditional image generation, few-shot learning, and anomaly detection.

We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations.

There is no doubt artificial intelligence (AI) has the potential to improve access to mental health care. "One could imagine a world where AI serves as the'front line' for mental health, providing a clearinghouse of resources and available services for individuals seeking help,'' wrote the authors of the 2023 article "The Potential Influence of AI on Population Mental Health." Targeted interventions delivered digitally through chatbots "can help reduce the population burden of mental illness, particularly in hard-to-reach populations and contexts, for example, through stepped care approaches that aim to help populations with the highest risk following natural disasters," the article states. Besides Nomi, there are an increasing number of AI platforms people are using to create chatbots to take on several roles, including that of ad hoc therapist. Yet, while AI can assist in mental health management, it cannot replace human intuition. A trained therapist observes nuances that AI can't, such as body language, tone shifts, and unspoken emotions. Chatbots can be helpful, but mental health experts stress that they should never fully replace the human experience. That said, these mainstream chatbots are frequently being used for therapeutic purposes, as opposed to chatbots designed with mental health management in mind. Industry observers say the reasons are many: They provide emotional support when people are not ready to reach out to a therapist. They are anonymous, easy to use, convenient, available anytime, safe, judgment-free, affordable, and fast. These general-purpose chatbots help by providing comfort, validation, and a safe space for users to express themselves--all without the stigma that sometimes comes with traditional therapy settings. "Talking to a therapist can be intimidating, expensive, or complicated to access, and sometimes you need someone--or something--to listen at that exact moment,'' said Stephanie Lewis, a licensed clinical social worker and executive director of Epiphany Wellness addiction and mental health treatment centers.

Reverse differentiation is an essential operation for automatic differentiation. Cartesian reverse differential categories axiomatize reverse differentiation in a categorical framework, where one of the primary axioms is the reverse chain rule, which is the formula that expresses the reverse derivative of a composition. Here, we present the reverse differential analogue of Faa di Bruno's Formula, which gives a higher-order reverse chain rule in a Cartesian reverse differential category. To properly do so, we also define partial reverse derivatives and higher-order reverse derivatives in a Cartesian reverse differential category.

Network models have been widely used to study diverse systems and analyze their dynamic behaviors. Given the structural variability of networks, an intriguing question arises: Can we infer the type of system represented by a network based on its structure? This classification problem involves extracting relevant features from the network. Existing literature has proposed various methods that combine structural measurements and dynamical processes for feature extraction. In this study, we introduce a novel approach to characterize networks using statistics from random walks, which can be particularly informative about network properties. We present the employed statistical metrics and compare their performance on multiple datasets with other state-of-the-art feature extraction methods. Our results demonstrate that the proposed method is effective in many cases, often outperforming existing approaches, although some limitations are observed across certain datasets.

This paper introduces an unsupervised approach to modeling exchangeable data. The proposed method learns an invertible mapping from a latent representation, distributed as correlated-but-exchangeable multivariate-t RVs, to an implicit data distribution that can be efficiently evaluated via recurrent neural networks. I found the paper interesting and well-written. Justification and evaluation of the method could, however, be much better. In particular, the authors do not provide good motivation for their choice of a multivariate t-distribution beyond the standard properties that 1) the posterior variance is data-dependent 2) it is heavier tailed compared to normal.

Natural revision seems so natural: it changes beliefs as little as possible to incorporate new information. Yet, some counterexamples show it wrong. It is so conservative that it never fully believes. It only believes in the current conditions. This is right in some cases and wrong in others. Which is which? The answer requires extending natural revision from simple formulae expressing universal truths (something holds) to conditionals expressing conditional truth (something holds in certain conditions). The extension is based on the basic principles natural revision follows, identified as minimal change, indifference and naivety: change beliefs as little as possible; equate the likeliness of scenarios by default; believe all until contradicted. The extension says that natural revision restricts changes to the current conditions. A comparison with an unrestricting revision shows what exactly the current conditions are. It is not what currently considered true if it contradicts the new information. It includes something more and more unlikely until the new information is at least possible.

It is one of the most forgotten and painful chores – taking out the trash. Technology has come to the rescue with a smart garbage can that can be pre-programmed with a specific schedule and park itself curbside for pick up. Paired with a companion app, SmartCan rolls out to a docking station by the road and autonomously returns once the garbage has been picked up. Taking out the trash has become one of the most dreaded chores. One must make their way outside in rain, sun, sleet or snow, and then haul a heavy trash can to the curb for pick up.

Columbia neuroscientists have revealed that a simple brain region, known for processing basic sensory information, can also guide complex feats of mental activity. The new study involving mice demonstrated that cells in the somatosensory cortex, the brain area responsible for touch, also play a key role in reward learning, the sophisticated type of learning that allows the brain to associate an action with a pleasurable outcome. It is the basis for how we connect our work in the office to that paycheck, or that A to the studying we did in preparation for the test. The new research, published today in Cell Reports, provides evidence that learning and memory are not relegated to a few select regions, but instead may permeate the brain. "Our brains are masterful at making connections, or associations, between seemingly disparate pieces of information, but where those associations are stored has remained an unresolved question," said Randy Bruno, PhD, a principal investigator at Columbia's Mortimer B. Zuckerman Mind Brain Behavior Institute and the paper's senior author.