A feature in which most or all values are nonzero, typically a tensor of floating-point values. For example, the following 10-element Tensor is dense because 9 of its values are nonzero:1
8 , 3 , 7 , 5 , 2 , 4 , 0 , 4 , 9 , 6 목차 목차 관련 글 Tensor Logic: The Language of AI “Most importantly, tensor logic makes new directions possible, such as sound reasoning in embedding space.” Precise Zero-Shot Dense Retrieval without Relevance Labels “This second step ground the generated document to the actual corpus, with the encoder's dense bottleneck filter…” Feature crossing “This synthetic feature would have the following 12 possible values:” Minimum marketable feature “Typically an MMF creates market value in one or more of the following ways:” Bucketization “For example, instead of representing temperature as a single continuous floating-point feature, you could chop …” Hypothetical Document Embeddings “This second step grounds the generated document to the actual corpus, with the encoder's dense bottleneck filte…” Wide model “A linear model that typically has many sparse input features.” Clipping (machine learning) “For example, suppose that <0.5% of values for a particular feature fall outside the range 40–60.” Introduction to machine learning problem framing 관련 절: Predictive ML and data Discrete feature “A feature with a finite set of possible values.” Embedding layer ‘Sparse features’에서 함께 참조됨 Memory Layers at Scale “On downstream tasks, language models augmented with our improved memory layer outperform dense models with more…” Numerical data “Representing a feature as numerical data indicates that the feature's values have a mathematical relationship t…” Example (machine learning) “Furthermore, the features in an example can also include synthetic features, such as feature crosses.” ML crash course - Linear regression “Weight is a parameter of the model and is calculated during training.” Permutation feature importance “One key advantage of permutation feature importance is that it is model-agnostic, i.e. it can be applied to any…”