The term robustness is ubiquitous in modern Machine Learning (ML). However, its meaning varies depending on context and community. Researchers either focus on narrow technical definitions, such as adversarial robustness, natural distribution shifts, and performativity, or they simply leave open what ...
knowledge and strategies from past experience to new experiences, is one of the primary desiderata for models of natural language processing (NLP), as well as for models in the wider field of machine learning1,2. For some, generalization is crucial to ensure that models behave robustly, reliabl...
with high probability, a good performance on previously unseen data points. In particular, we provide a precise meaning of "sufficiently large” in terms of properties of the QMLM and the employed training procedure.
Overfitting occurs when a model begins to memorize training data rather than learning to generalize from trend. The more difficult a criterion is to predict (i.e., the higher its uncertainty), the more noise exists in past information that need to be ignored. The problem is determining which ...
stationarity:A property of data in a data set, in which the data distributionstays constantacross one or more dimensions. Most commonly, that dimension istime, meaning that data exhibiting stationarity doesn't change over time. For example, data that exhibits stationaritydoesn't changefrom September...
the lack of ability to generalize across tasks, meaning the AI cannot solve new tasks, and the lack of ability to generalize across environments, meaning the AI can only perform its given task in a specific environment. The ability to solve new problems and in different environments is a fund...
Language learning as uncertainty reduction: The role of prediction error in linguistic generalization and item-learning Maša VujovićMichael RamscarElizabeth Wonnacott Aug 2021 Abstract Discriminative theories frame language learning as a process of reducing uncertainty about the meaning of an utterance ...
Improving Systematic Generalization Through Modularity and Augmentation Systematic generalization is the ability to combine known parts into novel meaning; an important aspect of efficient human learning, but a weakness of neur... L Ruis,B Lake 被引量: 0发表: 2022年 加载更多来源...
in bothaandb) was the most common output for both people and MLC, translating the queries in a one-to-one (1-to-1) and left-to-right manner consistent with iconic concatenation (IC). The rightmost patterns (in bothaandb) are less clearly structured but still generate a unique meaning ...
On account of the meaning of the particle only, we can see that it is also one of the two readings of the statement: In what is alike, there is only the presence [of the reason] where the particle only is inserted into the predicate position. n⋅nāga eliminates this unwanted ...