15,418 technical terms and definitions
k8s, kubernetes for ml, gpu scheduling, container orchestration, kubeflow
kubernetes is a distributed control system that schedules, scales, connects, and repairs containerized workloads across clusters. Kubernetes coordinates GPU training, batch…
kubernetes orchestration platform, kubernetes orchestration, orchestration platform
Kubernetes Orchestration Platform Introduction & Motivation Kubernetes Orchestration Platform addresses a central problem in cloud process control and pod-deployment-latency…
kubernetes resource management, kubernetes resource, resource management
Kubernetes Resource Management Introduction & Motivation Kubernetes Resource Management addresses a central problem in ml-ops process control and cpu-request-accuracy prediction…
kustomize declarative customization, kustomize declarative, declarative customization
Kustomize Declarative Customization Introduction & Motivation Kustomize Declarative Customization addresses a central problem in cloud process control…
llm architecture
KV cache stores computed key-value pairs to accelerate autoregressive LLM inference. How it works: During generation, each token attends to all previous tokens. Rather than…
kv cache, key-value cache, llm inference cache, paged attention, gqa, mqa, kv quantization
Key-value cache stores attention key and value tensors already computed for earlier tokens during autoregressive inference. It prevents every decode step from recomputing all…
kv cache quantization, kv cache eviction, attention memory, gqa, mqa, streamingllm, h2o, low rank kv cache
KV cache compression reduces the memory footprint or bandwidth of stored attention keys and values used during autoregressive inference. Long contexts and many concurrent requests…
L-BFGS Optimization Method Introduction & Motivation L-BFGS Optimization Method addresses a central problem in optimization algorithm tuning and improvement: how to Optimize…
L-BFGS Limited-Memory Quasi-Newton Optimization Introduction & Motivation L-BFGS Limited-Memory Quasi-Newton Optimization addresses a central problem in optimization algorithm…
training techniques
L-Diversity is privacy enhancement that requires diverse sensitive attribute values within each anonymity group - It is a core method in modern semiconductor AI serving…
lái, 来字, lái meaning, come
来 拼音：lái 意思：come / come here（来） 例词： 来了 — has come 进来 — come in 出来 — come out 回来 — come back 例句： 你来了！Nǐ lái le! — You are here! 请进来。Qǐng jìnlái. — Please come in. 跟读：lái lái lái
ai safety
Attacks are adversarial attacks that perturb every input feature by at most — constrained within a hypercube , making small, imperceptible changes to all features simultaneously.…
l0, ai safety
Attacks are adversarial attacks that modify the fewest number of input features (pixels) — constrained by , changing at most features but potentially by a large amount, creating…
l1 cache hit rate prediction, l1 cache hit, cache hit rate, hit rate prediction, l1 cache, cache hit, hit rate, rate prediction
L1 Cache Hit Rate Prediction with Machine Learning Introduction & Motivation L1 Cache Hit Rate Prediction with Machine Learning addresses a central problem in L1 cache hit rate…
l1-l2 cache bandwidth hierarchy and blocking, l1-l2 cache bandwidth, cache bandwidth hierarchy, bandwidth hierarchy blocking, l1-l2 cache, cache bandwidth, bandwidth hierarchy
L1-L2 Cache Bandwidth Hierarchy and Blocking Introduction & Motivation L1-L2 Cache Bandwidth Hierarchy and Blocking addresses a central problem in memory process control…
l2, ai safety
Attacks are adversarial attacks that constrain the total Euclidean magnitude of the perturbation — , allowing larger changes in a few features while keeping the overall…
l2 cache miss latency prediction, l2 cache miss, cache miss latency, miss latency prediction, l2 cache, cache miss, miss latency, latency prediction
L2 Cache Miss Latency Prediction with Machine Learning Introduction & Motivation L2 Cache Miss Latency Prediction with Machine Learning addresses a central problem in L2 cache…
l3 cache partitioning and quality-of-service, l3 cache partitioning, cache partitioning quality-of-service, l3 cache, cache partitioning, partitioning quality-of-service
L3 Cache Partitioning and Quality-of-Service Introduction & Motivation L3 Cache Partitioning and Quality-of-Service addresses a central problem in memory process control…
lab-on-chip integration
Lab-On-Chip Integration Introduction & Motivation Lab-On-Chip Integration addresses a central problem in microfluidics process control and device-footprint-mm2 prediction: how…
lab on chip reagent dispense accuracy prediction with machine learning, lab chip reagent dispense accuracy prediction machine learning, lab chip, lab-on-chip reagent
Lab-on-Chip Reagent Dispense Accuracy Prediction with Machine Learning Introduction & Motivation Lab-on-Chip Reagent Dispense Accuracy Prediction with Machine Learning addresses…
Label Efficiency Ratio and Area Under Learning Curve AULC Introduction & Motivation Label Efficiency Ratio and Area Under Learning Curve AULC addresses a central problem…
ai safety
Label Flipping is a data poisoning attack that corrupts training data by changing the labels of selected examples — the attacker flips a fraction of training labels (e.g.…
label marking durability environmental prediction, label marking durability, marking durability environmental, durability environmental prediction, label marking
Label Marking Durability Environmental Prediction with Machine Learning Introduction & Motivation Label Marking Durability Environmental Prediction with Machine Learning addresses…
graph neural networks
Label Propagation (LPA) is a semi-supervised graph algorithm that classifies unlabeled nodes by iteratively spreading known labels through the network structure — each node adopts…
Label Propagation on Graphs Introduction & Motivation Label Propagation on Graphs addresses a central problem in semisupervised and graph-construction-strategy architecture: how…
Black Box Shift Estimation BBSE for Label Distribution Shift Introduction & Motivation Black Box Shift Estimation BBSE for Label Distribution Shift addresses a central problem…
label shift detection, label shift, shift detection
Label Shift Detection Introduction & Motivation Label Shift Detection addresses a central problem in ml-ops process control and shift-detection-samples-required prediction: how…
machine learning
Label Smoothing is a regularization technique that softens hard one-hot labels by distributing a small amount of probability to non-target classes — instead of training…
soft labels, label smoothing regularization, label noise training, smoothed targets
Label Smoothing is the regularization technique that replaces hard one-hot target labels with soft labels that distribute a small amount of probability mass to non-target classes…
Label Spreading Algorithm Introduction & Motivation Label Spreading Algorithm addresses a central problem in semisupervised and transition-matrix-normalization architecture: how…
Zhou Label Spreading with Normalized Graph Laplacian Introduction & Motivation Zhou Label Spreading with Normalized Graph Laplacian addresses a central problem in semisupervised…
Label Uncertainty Modeling Introduction & Motivation Label Uncertainty Modeling addresses a central problem in {subject} learning and efficient {target} annotation: how to Achieve…
Ladder Networks with Denoising Lateral Connections Introduction & Motivation Ladder Networks with Denoising Lateral Connections addresses a central problem in semisupervised…
lag feature engineering, lag feature, feature engineering
Lag Feature Engineering Introduction & Motivation Lag Feature Engineering addresses a central problem in timeseries process control and lag-feature-historical-value prediction…
Lag-Llama LLaMA-Style Foundation Probabilistic Model Introduction & Motivation Lag-Llama LLaMA-Style Foundation Probabilistic Model addresses a central problem in timeseries…
analytical lagrangian dynamics, euler lagrange mechanics, generalized coordinate mechanics, variational mechanics, lagrangian mechanics semiconductor, engineering lagrangian modeling
Lagrangian mechanics predicts motion by expressing a system through generalized coordinates, kinetic and potential structure, constraints, and generalized forces. Instead…
scientific ml
Lagrangian Neural Networks (LNNs) are neural networks that learn the Lagrangian function of a physical system — deriving the equations of motion via the Euler-Lagrange equation…
LAMB Layer-Wise Adaptive Moments for Large Batches Introduction & Motivation LAMB Layer-Wise Adaptive Moments for Large Batches addresses a central problem in optimization…
lamb layer-wise adaptive batch normalization and large-batch training, lamb layer-wise adaptive, layer-wise adaptive batch, adaptive batch normalization, lamb layer-wise
LAMB Layer-Wise Adaptive Batch Normalization and Large-Batch Training Introduction & Motivation LAMB Layer-Wise Adaptive Batch Normalization and Large-Batch Training addresses…
LambdaMART Gradient Boosted Decision Trees for Ranking Introduction & Motivation LambdaMART Gradient Boosted Decision Trees for Ranking addresses a central problem in recsys…
lamda, language model for dialogue applications, foundation model
LaMDA (Language Model for Dialogue Applications) is Google's conversational AI model specifically trained for natural, coherent, and informative multi-turn dialogue…
laminate resin cure shrinkage prediction, laminate resin cure, resin cure shrinkage, cure shrinkage prediction, laminate resin, resin cure, cure shrinkage, shrinkage prediction
Laminate Resin Cure Shrinkage Prediction with Machine Learning Introduction & Motivation Laminate Resin Cure Shrinkage Prediction with Machine Learning addresses a central problem…
laminate substrate warpage drift prediction, laminate substrate warpage, substrate warpage drift, warpage drift prediction, laminate substrate, substrate warpage, warpage drift
Laminate Substrate Warpage Drift Prediction with Machine Learning Introduction & Motivation Laminate Substrate Warpage Drift Prediction with Machine Learning addresses a central…
llm architecture
Landmark Attention is the efficient transformer attention mechanism that reduces computational complexity by routing all token attention through a sparse set of landmark (anchor)…
llamaindex, framework
LLM Application Frameworks LangChain Overview Most popular framework for building LLM applications. Provides abstractions for chains, agents, memory, and tools. Key Components…
langgraph, langsmith, langserve, llm framework, runnable, chain, agent, retriever
LangChain is an open-source ecosystem for composing language-model applications from prompts, models, retrievers, tools, structured chains, and agent workflows. It offers broad…
framework
LangChain is the most widely adopted open-source framework for building applications powered by language models — providing modular components for chaining LLM calls with data…
ai agents
LangChain is a development framework for composing LLM applications using chains, agents, tools, and memory components - It is a core method in modern semiconductor AI-agent…
generative models
Langevin Dynamics is a stochastic sampling algorithm that generates samples from a target probability distribution p(x) by simulating a continuous-time stochastic differential…
visual, langchain, python
LangFlow is an open-source visual UI for building LLM-powered applications by dragging and dropping components (Prompts, LLMs, Vector Stores, Agents, Tools) onto a canvas…