Results for "retraining signal"

Using production outcomes to improve models.

Embedding signals to prove model ownership.

Detects trigger phrases in audio streams.

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

Practices for operationalizing ML: versioning, CI/CD, monitoring, retraining, and reliable production management.

Joint vision-language model aligning images and text.

Shift in feature distribution over time.

Shift in model outputs.

Learning without catastrophic forgetting.

Learning a function from input-output pairs (labeled data), optimizing performance on predicting outputs for unseen inputs.

Designing input features to expose useful structure (e.g., ratios, lags, aggregations), often crucial outside deep learning.

Methods to set starting weights to preserve signal/gradient scales across layers.

Converting audio speech into text, often using encoder-decoder or transducer architectures.

Maps audio signals to linguistic units.

Aligns transcripts with audio timestamps.

Identifying speakers in audio.

Models time evolution via hidden states.

Monte Carlo method for state estimation.

Optimization under uncertainty.

Maximizing reward without fulfilling real goal.

Devices measuring physical quantities (vision, lidar, force, IMU, etc.).

Software pipeline converting raw sensor data into structured representations.

External sensing of surroundings (vision, audio, lidar).

Control without feedback after execution begins.

Learning physical parameters from data.

Modifying reward to accelerate learning.

Signals indicating dangerous behavior.

Using markers to isolate context segments.