Anthropic research reveals AI models perform worse with extended reasoning time, challenging industry assumptions about test-time compute scaling in enterprise deployments.

Two AI models have achieved gold medal standard for the first time in a prestigious competition for young mathematicians – ...

Accurate and efficient traffic speed prediction is crucial for improving road safety and efficiency. With the emerging deep ...

Here, the parameters of the primitive indirectly define the shape of a reference trajectory. We propose an alternative MP representation based on probabilistic inference in learned graphical models ...

In conclusion, Neural Graphical Models (NGMs) significantly advance probabilistic graphical modeling. By combining the flexibility and expressiveness of deep neural networks with the structural ...

Learning, inference, and sampling are operations that make graphical models useful for domain exploration. In a broad sense, learning involves fitting the distribution function parameters from data, ...

Grounded in probabilistic graphical models, causal models allow us to ask both predictive and counterfactual queries. Causality can also model distribution shifts by encoding both observational and ...

Aiming at a comprehensive and concise tutorial survey, recap of variational inference and reinforcement learning with Probabilistic Graphical Models are given with detailed derivations. Reviews and ...

Conclusion and relevance: Probabilistic deep learning models can detect glaucoma from multi-modal data with high accuracy. Our findings suggest that models based on combined visual field and fundus ...

In the new paper Variational Inference for Infinitely Deep Neural Networks, researchers from Columbia University propose the unbounded depth neural network (UDN), an infinitely deep probabilistic ...