We investigate efficient exploration strategies of environments with unknown stochastic dynamics and sparse rewards. Specifically, we analyze first the impact of parallel simulations on the probability of reaching rare states within a finite time budget. Using simplified models based on random walks and L\'evy processes, we provide analytical results that demonstrate a phase transition in reaching probabilities as a function of the number of parallel simulations. We identify an optimal number of parallel simulations that balances exploration diversity and time allocation. Additionally, we analyze a restarting mechanism that exponentially enhances the probability of success by redirecting efforts toward more promising regions of the state space. Our findings contribute to a more qualitative and quantitative theory of some exploration schemes in reinforcement learning, offering insights into developing more efficient strategies for environments characterized by rare events.

The adoption of deep neural networks (DNNs) in safety-critical contexts is often prevented by the lack of effective means to explain their results, especially when they are erroneous. In our previous work, we proposed a white-box approach (HUDD) and a black-box approach (SAFE) to automatically characterize DNN failures. They both identify clusters of similar images from a potentially large set of images leading to DNN failures. However, the analysis pipelines for HUDD and SAFE were instantiated in specific ways according to common practices, deferring the analysis of other pipelines to future work. In this paper, we report on an empirical evaluation of 99 different pipelines for root cause analysis of DNN failures. They combine transfer learning, autoencoders, heatmaps of neuron relevance, dimensionality reduction techniques, and different clustering algorithms. Our results show that the best pipeline combines transfer learning, DBSCAN, and UMAP. It leads to clusters almost exclusively capturing images of the same failure scenario, thus facilitating root cause analysis. Further, it generates distinct clusters for each root cause of failure, thus enabling engineers to detect all the unsafe scenarios. Interestingly, these results hold even for failure scenarios that are only observed in a small percentage of the failing images.

Let's set the record straight and dive into the world of ChatGPT prompts, exploring their intricacies and unlocking the secrets to crafting exceptional queries! There is no specific number of prompts that you need to learn to get the best response from ChatGPT. The key to obtaining the best responses lies in understanding how to craft effective prompts, rather than memorizing a set number of them. By only focusing on clarity and specificity, you can guide ChatGPT toward delivering accurate, relevant, and informative answers. The most important thing is to tailor your prompts to the topic or question you want to explore.

ChatGPT helps hackers write malicious codes to steal your personal data — Business Insider India, 1/15/2023 Very interesting article on a very hot toptic. ChatGPT is an AI-driven program that assists…

"Our work was meant as a demonstration project to show how disease registries could be leveraged to yield the same sorts of insights about personalizing the longitudinal sequence of treatments as one might get from a prospective, multistage randomized trial," Krakow said in emailed comments about the work, which was based on the transplant complication known as graft-vs.-host When a cancer patient has a complex medical history, a little-studied disease and many potential treatment options, published guidelines and studies rarely indicate one clear treatment choice. So in everyday clinical practice, physicians recommend treatments to their patients based on an amalgam of published and anecdotal evidence, and patient-specific characteristics and preferences. Then, they adjust treatment as needed as time goes on. In what Krakow calls "algorithm-informed treatment," a computer would generate a treatment recommendation for a specific time point in an individual patient's therapeutic course.

If you missed that thread and there's something you want to mention, post it and I'll put it up. Lots of categories didn't have an entry. You can also make a category yourself. "and we all realized what a pain in the ass Tensorflow was and how it didn't need to be that way. In the academic community, it certainly to me feels like pytorch has become the dominant framework (probably not backed up by actual stats...

Stochastic processes have many applications, including in finance and physics. It is an interesting model to represent many phenomena. Unfortunately the theory behind it is very difficult, making it accessible to a few'elite' data scientists, and not popular in business contexts. One of the most simple examples is a random walk, and indeed easy to understand with no mathematical background. However, time-continuous stochastic processes are always defined and studied using advanced and abstract mathematical tools such as measure theory, martingales, and filtration.

Stochastic processes have many applications, including in finance and physics. It is an interesting model to represent many phenomena. Unfortunately the theory behind it is very difficult, making it accessible to a few'elite' data scientists, and not popular in business contexts. One of the most simple examples is a random walk, and indeed easy to understand with no mathematical background. However, time-continuous stochastic processes are always defined and studied using advanced and abstract mathematical tools such as measure theory, martingales, and filtration.

Cognitive Systems: Toward Human-Level Functionality

Stochastic processes have many applications, including in finance and physics. It is an interesting model to represent many phenomena. Unfortunately the theory behind it is very difficult, making it accessible to a few'elite' data scientists, and not popular in business contexts. One of the most simple examples is a random walk, and indeed easy to understand with no mathematical background. However, time-continuous stochastic processes are always defined and studied using advanced and abstract mathematical tools such as measure theory, martingales, and filtration.