The Bayesian behavior framework synergizes habits and goals through variational Bayesian methods, offering new insights on sensorimotor behavior and comprehension of actions. https://msft.it/6043Yvx4Z
Microsoft Research’s Post
More Relevant Posts
-
VP, Chief Data Officer & Assoc Prof Radiation Oncology & Radiology, MD Anderson Cancer Center * Director, Data Ecosystem - Institute of Data Science in Oncology * Member at CHIEF * Chair, Women-in-Cancer/All-in-Cancer
We make decisions weighing out uncertainties and risks all the time… we need models to present their outputs with uncertainties to make fully informed decisions as we use them
"Without quantifying the model’s uncertainty, an accurate prediction and a wild guess look the same." Jonte Dancker introduces the benefits of conformal prediction, an approach that "can turn any point prediction into statistically valid prediction regions."
-
Leadership training and building complex IT solutions | Technical Program Management | Cybersecurity Management | AI development Management
Stochastic Gradient Descent (SGD) stands out as the go-to method for tackling optimization problems that involve the summation of individual functions. Its key strength lies in its ability to swiftly converge towards an optimal solution, contingent upon the chosen step size. This approach offers a rapid yet effective means of obtaining estimations that prove sufficiently accurate for practical predictive purposes. Furthermore, SGD's efficiency shines through in its ability to significantly reduce the computational workload required for convergence. As a result, it finds extensive utility in the development of prediction models for vast datasets, where 'n' extends into the millions, and the dimensionality 'd' may also reach the millions, as is often the case with high-definition images. In this video, the knowledgeable professor Suvrit Sra elucidates the remarkable power of SGD in an engaging and instructive manner.
25. Stochastic Gradient Descent
https://www.youtube.com/
-
enabling digital services for Student Loan related activities while maintaining the highest security standard, the most compliant personal data protection and customer-centric data-driven innovation.
Exciting developments in Bayesian linear models! Our latest blog post explores mean-field variational inference with the TAP free energy and its impact on geometric and statistical properties. In high-dimensional scenarios, traditional approaches may underestimate posterior uncertainty. However, our study shows that minimizing the TAP free energy provides a consistent estimate of posterior marginals and allows for correctly calibrated posterior inference. Discover more about this groundbreaking research here: https://bit.ly/3R0alwD #BayesianInference #StatisticalModeling #ResearchUpdate
Technoscience - Digital Brain | Cognitive AI | gen AI | agentic AI | Math AI | Science of Artificial Intelligence
3wScientific Understanding of Learning and to model the high-level structure to build systems that interact with their environment, with people, and with other agents in the real world. This vision requires combining perception with reasoning and decision-making. It poses hard challenges: from generalizing effectively from few or no samples (data) to adapting to new domains to communicating (cognition and symbolic communication) in ways that are natural to people.