mini-batch online learning,machine learning
Update with small batches of streaming data.
9,967 technical terms and definitions
Update with small batches of streaming data.
Vision-language model aligned with GPT-4.
MiniGPT connects vision encoder to LLM. Open source multimodal. Research project.
Minimum batch requirements specify smallest lot size for processing.
Minor nonconformances are isolated deviations not significantly impacting quality.
Minor stoppages are brief interruptions under several minutes often not formally recorded.
Time before recombination.
Mip-NeRF anti-aliases NeRF by integrating over conical frustums rather than points.
Perplexity-controlled sampling.
Mirostat dynamically adjusts temperature maintaining target perplexity.
Dislocations from lattice mismatch.
Smooth activation x*tanh(softplus(x)).
Identify false or misleading information.
Quantify crystal orientation differences.
Multiple Input Signature Register compresses test responses into compact signatures for comparison with expected values in BIST.
Handle incomplete multimodal data.
Handle missing values: impute mean, median, or model-based.
Prevent errors in processes.
Mistake-proofing designs processes and equipment to prevent or detect errors immediately.
Efficient open-source language model with sliding window attention.
Combine chiplets from different sources/technologies.
Encode network as MILP for verification.
Mixed model production manufactures multiple products on same line enabling variety without dedicated resources.
Use lower precision (FP16) for some operations to speed up and save memory.
Automatic Mixed Precision (AMP) uses FP16/BF16 where safe. 2x memory savings, faster compute. Loss scaling prevents underflow.
Mixed precision uses FP16/BF16 for speed and memory, FP32 for stability. AMP automates this. 2x speedup on modern GPUs.
Mixed-precision training uses different numeric precisions for different operations balancing speed and accuracy.
MixMatch unifies consistency regularization entropy minimization and MixUp for semi-supervised learning with unlabeled data.
Combine consistency augmentation and pseudo-labeling.
Mixture of Experts version of Mistral.
Optimize formulations where components sum to constant.
Route queries to different specialized agents based on task type.
Dynamic computation allocation across layers based on input complexity.
Dynamically allocate computation across transformer layers based on token importance.
Mixture of depths dynamically allocates computation across layers per token.
Route each input to a few specialized expert networks instead of all parameters.
Route tasks to experts.
Blend training examples together to improve robustness.
Blend entire images and labels.
MixUp for text combines embeddings of two examples and interpolates their labels for training with continuous semantic augmentation.
Linearly interpolate training examples.
Mixup blends training examples. Regularization, smoother predictions.
MLC LLM provides universal LLM deployment. Compile to any device.
MLflow Tracking logs experiments: params, metrics, artifacts. Compare runs. Model registry.
Platform for ML lifecycle management.
Compiler infrastructure for ML.
MLIR is multi-level IR for ML compilers. Connects frameworks to hardware. Apache TVM, IREE use MLIR.
Practices for deploying monitoring and maintaining ML systems in production.
I can outline MLOps flows: versioning models, registries, canary deploys, rollback, and monitoring for drift.
Token-mixing and channel-mixing MLPs.