Results for "real-time"

Performance drop when moving from simulation to reality.

Combining simulation and real-world data.

Time from request to response; critical for real-time inference and UX.

Learning where data arrives sequentially and the model updates continuously, often under changing distributions.

Sequential data indexed by time.

Randomizing simulation parameters to improve real-world transfer.

Differences between simulated and real physics.

Observing model inputs/outputs, latency, cost, and quality over time to catch regressions and drift.

Letting an LLM call external functions/APIs to fetch data, compute, or take actions, improving reliability.

Low-latency prediction per request.

Enables external computation or lookup.

Control using real-time sensor feedback.

High-fidelity virtual model of a physical system.

Persistent directional movement over time.

CNNs applied to time series.

Guaranteed response times.

The relationship between inputs and outputs changes over time, requiring monitoring and model updates.

Models time evolution via hidden states.

Networks with recurrent connections for sequences; largely supplanted by Transformers for many tasks.

Equations governing how system states change over time.

Separates planning from execution in agent architectures.

Cost to run models in production.

Detects trigger phrases in audio streams.

Optimal estimator for linear dynamic systems.

A dataset + metric suite for comparing models; can be gamed or misaligned with real-world goals.

Artificial environment for training/testing agents.

Artificially created data used to train/test models; helpful for privacy and coverage, risky if unrealistic.

Artificial sensor data generated in simulation.

Two-network setup where generator fools a discriminator.

How many requests or tokens can be processed per unit time; affects scalability and cost.