The days of tech giants buying up discrete chips are over. AI companies now need GPUs, CPUs, and everything in between. Ask anyone what Nvidia makes, and they're likely to first say "GPUs." For decades, the chipmaker has been defined by advanced parallel computing, and the emergence of generative AI and the resulting surge in demand for GPUs has been a boon for the company . But Nvidia's recent moves signal that it's looking to lock in more customers at the less compute-intensive end of the AI market--customers who don't necessarily need the beefiest, most powerful GPUs to train AI models, but instead are looking for the most efficient ways to run agentic AI software.

Bayesian brain theory suggests that the brain employs generative models to understand the external world.

Knockoffs are a popular statistical tool for conditional variable selection in high dimension.

Our framework may be thought of as a generalization of the synthetic control and synthetic interventions frameworks, where data is collected via an adaptive intervention assignment policy.

Appendix A provides derivations supporting Section 3 in the main paper. In this section we provide detailed derivations of the ST -DGMRF joint distribution, for both first-order transition models (Section A.1) and higher-order transition models (Section A.2). A.1 Joint distribution The LDS (see Section 2.2 and 3.1 in the main paper) defines a joint distribution over system states First, note that Eq. (1) can be written as a set of linear equations x We make use of this property in the DGMRF formulation and in the conjugate gradient method. Eq. 11 is converted into a discrete-time dynamical system by approximating ρ We consider two ST -DGMRF variants that capture different amounts of prior knowledge. DGMRF transition matrices can be parameterized accordingly. The air quality dataset is based on hourly PM2.5 measurements obtained from [ The raw PM2.5 measurements are log-transformed and standardized to zero mean and unit Ca. 50% of the nodes are masked out (purple nodes within We use a simple MLP with one hidden layer of width 16 with ReLU activations and no output non-linearity. The DGMRF parameters are not shared across time, allowing for dynamically changing spatial covariance patterns.

Appendix for "Episodic Multi-T ask Learning with Heterogeneous Neural Processes" In this section, we list frequently asked questions from researchers who help proofread this manuscript. As shown in Table 1, we use "Heterogeneous tasks" to distinguish the different branches of multi-task Meanwhile, "Episodic training" is used to describe the data-feeding strategy. Thus, "Heterogeneous tasks" is not available here (-). In episodic multi-task learning, we restrict the scope of the problem to the case where tasks in the same episode are related and share the same target space. This also implies that tasks with the same target space are related.