29,259 technical terms and definitions
random sampling, selection bias, 1936 Literary Digest poll, point estimate confidence interval, hypothesis testing
Inferential statistics uses a sample of data to draw conclusions about a larger population the sample is meant to represent, explicitly quantifying the uncertainty introduced…
architecture
Infini-attention is the long-context attention approach that combines local attention with compressed memory mechanisms to approximate effectively unbounded context handling - it…
rdma, network, hpc, mellanox, cluster, latency
InfiniBand is a high-bandwidth, low-latency networking technology using RDMA for GPU cluster communication — providing 200-400 Gbps per port with microsecond latencies, InfiniBand…
infrastructure
RDMA (Remote Direct Memory Access) lets one computer's network card place data directly into another computer's memory without involving either machine's CPU or operating-system…
ib verbs programming, infiniband qp connection, infiniband subnet manager, ib transport layer
InfiniBand Architecture is the high-performance networking standard designed for low-latency, high-bandwidth interconnects in HPC and AI clusters — providing hardware-offloaded…
supply chain & logistics
Infinite Capacity Scheduling is scheduling that ignores capacity constraints to prioritize demand and due-date visibility - It provides a quick demand picture before feasibility…
theory
The Infinite-Width Limit is a theoretical idealization in deep learning where the number of neurons in each hidden layer is taken to infinity — revealing that at this limit…
amd infinity fabric, what is amd infinity fabric
Infinity Fabric is AMD’s interconnect technology family for coordinating components in its computing platforms. The name describes a system communication framework rather than one…
interpretability
Influence functions can help identify mislabeled or otherwise genuinely problematic training examples, since a training example exerting an unusually large, outsized measured…
explainable ai
Influence functions are a technique originally developed within robust statistics, later applied directly to machine learning, that measures how much each individual specific…
Influence Functions for Training Data Attribution Introduction & Motivation Influence Functions for Training Data Attribution addresses a central problem in interpretability…
Influence Functions for Training Data Attribution (Extended Study) Introduction & Motivation Influence Functions for Training Data Attribution (Extended Study) addresses a central…
Influence Functions for Training Data Attribution (Extended Study) Introduction & Motivation Influence Functions for Training Data Attribution (Extended Study) addresses a central…
Influence Functions for Explanation Introduction & Motivation Influence Functions for Explanation addresses a central problem in explainability interpretation and model…
recommendation systems
Influence Functions Rec is training-data attribution methods estimating how individual examples affect recommendation outputs. - They trace problematic or beneficial…
Influence Functions for Training Sample Attribution Introduction & Motivation Influence Functions for Training Sample Attribution addresses a central problem in explainability…
Influence Maximization in Graphs Introduction & Motivation Influence Maximization in Graphs addresses a central problem in graph network representation…
recommendation systems
Influence Propagation is modeling how preferences or behaviors spread across user networks over time - It helps predict adoption and recommendation impact beyond isolated…
info fan-out package warpage prediction, info fan-out package, fan-out package warpage, package warpage prediction, info fan-out, fan-out package, package warpage, warpage prediction
InFO Fan-Out Package Warpage Prediction with Machine Learning Introduction & Motivation InFO Fan-Out Package Warpage Prediction with Machine Learning addresses a central problem…
info, integrated fan-out, advanced packaging
Integrated Fan-Out is TSMC's fan-out wafer-level packaging technology that redistributes die I/O to a larger area without a traditional package substrate. First used in Apple's…
disentangled gan, mutual information gan, controllable image generation, unsupervised disentanglement, generative adversarial network
InfoGAN is a generative adversarial network variant that learns disentangled and interpretable latent factors by maximizing mutual information between selected latent codes…
content creation
Infographic generation is the use of AI to automatically create visual information graphics — transforming data, statistics, processes, and concepts into compelling visual…
self-supervised learning
InfoNCE Loss is a contrastive learning objective that estimates mutual information between representations — by training a model to identify the correct "positive" sample from…
Claude Shannon 1948, information theory, entropy, A Mathematical Theory of Communication, uncertainty, data vs information
Information is whatever reduces uncertainty about something: a fact, a signal, or a pattern that lets a receiver know more after receiving it than before. In everyday speech it…
Information Criteria for Model Selection Introduction & Motivation Information Criteria for Model Selection addresses a central problem in timeseries forecasting…
information extraction structured, information extraction, extraction structured
Information Extraction Structured Introduction & Motivation Information Extraction Structured addresses a central problem in nlp process control…
reinforcement learning
Information Gain Exploration is an exploration strategy that rewards actions that maximize the information gained about the environment — the agent seeks states and actions that…
IPL, IPL-V, Newell Shaw Simon 1956, RAND Corporation, Logic Theorist, JOHNNIAC, push-down stack, list structures
The Information Processing Language, or IPL, was a family of programming languages developed by Allen Newell, Cliff Shaw, and Herbert Simon at the RAND Corporation beginning…
information retrieval bm25, information retrieval, retrieval bm25
Information Retrieval BM25 Introduction & Motivation Information Retrieval BM25 addresses a central problem in systems process control and relevance-ranking-effectiveness…
information retrieval ranking, information retrieval, retrieval ranking
Information Retrieval Ranking Introduction & Motivation Information Retrieval Ranking addresses a central problem in nlp process control and information-retrieval-document-ranking…
information retrieval relevance prediction, information retrieval relevance, retrieval relevance prediction, information retrieval, retrieval relevance, relevance prediction
Information Retrieval Relevance Prediction with Machine Learning Introduction & Motivation Information Retrieval Relevance Prediction with Machine Learning addresses a central…
Claude Shannon, Shannon 1948, mathematical theory of communication, channel capacity, mutual information, KL divergence, source coding
Information theory is the mathematical study of quantifying, storing and communicating information, founded by Claude Shannon's 1948 paper "A Mathematical Theory of Communication"…
entropy, mutual information, channel capacity, shannon theory, kl divergence
Information theory quantifies uncertainty, information, coding, and fundamental communication limits. It provides the language for compression, channel coding, storage, networks…
A Mathematical Theory of Communication, Bell System Technical Journal 1948, channel capacity, noisy-channel coding theorem, source coding theorem, Claude Shannon, Shannon-Hartley
"A Mathematical Theory of Communication," by Claude E. Shannon, appeared in the Bell System Technical Journal in two installments, July and October 1948 (volume 27, pages 379–423…
Information Theory: Entropy, Channel Capacity, and Coding Theorems Information theory, founded by Claude Shannon in 1948, provides the mathematical framework for quantifying…
Information Theory, Entropy, Divergence, and Channel Capacity 1. Information as uncertainty reduction Information theory quantifies uncertainty, compression, communication…
Information Theory: Foundations and Machine Learning Applications 1. Introduction to Information Theory Information theory provides mathematical foundations for quantifying…
time series models
Informer is a long-sequence transformer for time-series forecasting using probabilistic sparse attention. - It reduces quadratic attention cost so long-context forecasting becomes…
Informer with ProbSparse Self-Attention Mechanism Introduction & Motivation Informer with ProbSparse Self-Attention Mechanism addresses a central problem in timeseries forecasting…
lithography
Infrared alignment is the alignment technique that uses infrared transmission through silicon to view frontside marks from the backside during lithography registration - it is…
IR ellipsometry, infrared spectroscopic ellipsometry, IR-VASE, FTIR ellipsometry, far-infrared ellipsometry, infrared dielectric function metrology
Infrared ellipsometry extends polarization-resolved optical metrology into energies where lattice vibrations, molecular bonds, free carriers, and low-energy excitations dominate…
ir microscopy, backside thermal imaging, failure analysis infrared, silicon thermal imaging
Infrared Microscopy is a failure-analysis and diagnostic technique that images infrared radiation or infrared transmission through semiconductor devices to locate thermal…
manufacturing equipment
Infrared Sensor is non-contact sensor that infers object temperature from emitted infrared radiation - It is a core method in modern semiconductor AI, manufacturing control…
infrared thermal hotspot detection prediction, infrared thermal hotspot, thermal hotspot detection, hotspot detection prediction, infrared thermal, thermal hotspot
Infrared Thermal Hotspot Detection Prediction with Machine Learning Introduction & Motivation Infrared Thermal Hotspot Detection Prediction with Machine Learning addresses…
infrared thermography hotspot detection prediction, infrared thermography hotspot, thermography hotspot detection, hotspot detection prediction, infrared thermography
Infrared Thermography Hotspot Detection Prediction with Machine Learning Introduction & Motivation Infrared Thermography Hotspot Detection Prediction with Machine Learning…
infrared thermography hotspot localization prediction, infrared thermography hotspot, thermography hotspot localization, hotspot localization prediction
Infrared Thermography Hotspot Localization Prediction with Machine Learning Introduction & Motivation Infrared Thermography Hotspot Localization Prediction with Machine Learning…
semiconductor infrastructure, datacenter infrastructure, power delivery, pue, cooling, liquid cooling, fab facility, ultrapure water, rack power, thermal resistance, vrm, co-packaged optics, grid interconnection
Infrastructure is everything a chip depends on that is not the chip: the power chain that delivers current to it, the thermal chain that carries heat away, the water and air that…
infrastructure as code terraform, infrastructure as code, as code terraform, infrastructure as, as code, code terraform
Infrastructure as Code Terraform Introduction & Motivation Infrastructure as Code Terraform addresses a central problem in cloud process control and iac-declarative-language…
infrastructure testing validation, infrastructure testing, testing validation
Infrastructure Testing Validation Introduction & Motivation Infrastructure Testing Validation addresses a central problem in cloud process control…
infrastructure vulnerability management, infrastructure vulnerability, vulnerability management
Infrastructure Vulnerability Management Introduction & Motivation Infrastructure Vulnerability Management addresses a central problem in cloud process control…