JIT Compilation
Just in time compilation that generates optimized machine code at runtime from high level graphs, accelerating ML workloads and smart contract VMs.
Just in time compilation that generates optimized machine code at runtime from high level graphs, accelerating ML workloads and smart contract VMs.
Training a model on several related tasks at once to share representations and improve generalization, balancing losses with schedulers or task weighting.
Probability model over multiple random variables, capturing their dependence structure, foundation for graphical models and Bayesian inference.
Representation space learned for multiple modalities or objects so semantically related items lie close together, used in retrieval and cross modal search.
Symmetrized and smoothed version of KL divergence bounded between 0 and 1; used to compare probability distributions in GANs and topic models.
Matrix of first order partial derivatives of a vector valued function; central to backpropagation, sensitivity analysis, and normalizing flows.
Metric for set overlap defined as intersection over union; used for clustering, deduplication, and evaluating retrieval results.
High performance numerical computing library with composable transformations like JIT compilation, vectorization, and automatic differentiation, widely used in research and large model training.
Timeline for when locked tokens become transferable, typically after cliffs and linear vesting, important for liquidity and sell‑pressure analysis.
Trusted setup where participants can add new randomness later, so as long as one contribution is honest the toxic waste remains unknown even if others are compromised.
Trusted setup that works for any circuit within certain size bounds, so one ceremony can support many applications, unlike per‑circuit setups.