Maps for Learning Indexable Classes

• URL: http://arxiv.org/abs/2010.09460v1
• Date: Thu, 15 Oct 2020 09:34:07 GMT
• Title: Maps for Learning Indexable Classes
• Authors: Julian Berger, Maximilian B\"other, Vanja Dosko\v{c}, Jonathan Gadea Harder, Nicolas Klodt, Timo K\"otzing, Winfried L\"otzsch, Jannik Peters, Leon Schiller, Lars Seifert, Armin Wells, Simon Wietheger
• Abstract summary: We study learning of indexed families from positive data where a learner can freely choose a hypothesis space. We are interested in various restrictions on learning, such as consistency, conservativeness or set-drivenness.
• Score: 1.2728819383164875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study learning of indexed families from positive data where a learner can freely choose a hypothesis space (with uniformly decidable membership) comprising at least the languages to be learned. This abstracts a very universal learning task which can be found in many areas, for example learning of (subsets of) regular languages or learning of natural languages. We are interested in various restrictions on learning, such as consistency, conservativeness or set-drivenness, exemplifying various natural learning restrictions. Building on previous results from the literature, we provide several maps (depictions of all pairwise relations) of various groups of learning criteria, including a map for monotonicity restrictions and similar criteria and a map for restrictions on data presentation. Furthermore, we consider, for various learning criteria, whether learners can be assumed consistent.

Related papers

• Local Contrastive Feature learning for Tabular Data [8.93957397187611]
  We propose a new local contrastive feature learning framework (LoCL) In order to create a niche for local learning, we use feature correlations to create a maximum-spanning tree, and break the tree into feature subsets. Convolutional learning of the features is used to learn latent feature space, regulated by contrastive and reconstruction losses.
  arXiv  Detail & Related papers  (2022-11-19T00:53:41Z)
• Cross-lingual Lifelong Learning [53.06904052325966]
  We present a principled Cross-lingual Continual Learning (CCL) evaluation paradigm. We provide insights into what makes multilingual sequential learning particularly challenging. The implications of this analysis include a recipe for how to measure and balance different cross-lingual continual learning desiderata.
  arXiv  Detail & Related papers  (2022-05-23T09:25:43Z)
• LISA: Learning Interpretable Skill Abstractions from Language [85.20587800593293]
  We propose a hierarchical imitation learning framework that can learn diverse, interpretable skills from language-conditioned demonstrations. Our method demonstrates a more natural way to condition on language in sequential decision-making problems.
  arXiv  Detail & Related papers  (2022-02-28T19:43:24Z)
• Towards a theory of out-of-distribution learning [23.878004729029644]
  We propose a chronological approach to defining different learning tasks using the provably approximately correct (PAC) learning framework. We will start with in-distribution learning and progress to recently proposed lifelong or continual learning. Our hope is that this work will inspire a universally agreed-upon approach to quantifying different types of learning.
  arXiv  Detail & Related papers  (2021-09-29T15:35:16Z)
• Language learnability in the limit for general metrics: a Gold-Angluin result [91.3755431537592]
  We use Niyogi's extended version of a theorem by Blum and Blum (1975) on the existence of locking data sets to prove a necessary condition for learnability in the limit of any family of languages in any given metric. When the language family is further assumed to contain all finite languages, the same condition also becomes sufficient for learnability in the limit.
  arXiv  Detail & Related papers  (2021-03-24T13:11:09Z)
• Curriculum Learning: A Survey [65.31516318260759]
  Curriculum learning strategies have been successfully employed in all areas of machine learning. We construct a taxonomy of curriculum learning approaches by hand, considering various classification criteria. We build a hierarchical tree of curriculum learning methods using an agglomerative clustering algorithm.
  arXiv  Detail & Related papers  (2021-01-25T20:08:32Z)
• Learning Languages in the Limit from Positive Information with Finitely Many Memory Changes [0.0]
  We show that non-U-shapedness is not restrictive, while conservativeness and (strong) monotonicity are. We also give an example of a non-semantic restriction (strongly non-U-shapedness) where the two settings differ.
  arXiv  Detail & Related papers  (2020-10-09T19:59:49Z)
• Linguistic Typology Features from Text: Inferring the Sparse Features of World Atlas of Language Structures [73.06435180872293]
  We construct a recurrent neural network predictor based on byte embeddings and convolutional layers. We show that some features from various linguistic types can be predicted reliably.
  arXiv  Detail & Related papers  (2020-04-30T21:00:53Z)
• LNMap: Departures from Isomorphic Assumption in Bilingual Lexicon Induction Through Non-Linear Mapping in Latent Space [17.49073364781107]
  We propose a novel semi-supervised method to learn cross-lingual word embeddings for bilingual lexicon induction. Our model is independent of the isomorphic assumption and uses nonlinear mapping in the latent space of two independently trained auto-encoders.
  arXiv  Detail & Related papers  (2020-04-28T23:28:26Z)
• Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity [67.36239720463657]
  Multi-SimLex is a large-scale lexical resource and evaluation benchmark covering datasets for 12 diverse languages. Each language dataset is annotated for the lexical relation of semantic similarity and contains 1,888 semantically aligned concept pairs. Owing to the alignment of concepts across languages, we provide a suite of 66 cross-lingual semantic similarity datasets.
  arXiv  Detail & Related papers  (2020-03-10T17:17:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.