A computer is something that computes, and since modern theories of cognition assume that humans make computations when processing information, humans are computers. What kinds of computations do humans make when they learn languages? Answering this question requires the collaborative efforts of ...
精确的规则指定了,如何根据新的证据来更新概率,以及,如何根据概率与效用来选择行动(详见词条,Bayes’s theorem与normative theories of rational choice: expected utility)。在1980与1990年代,技术和概念的发展使得高效的计算机程序能够在现实情况下实现或近似于贝叶斯推断。随之而来的就是贝叶斯AI的爆发(Thrun, Burgard, ...
suggestion that midbrain dopaminergic neurons encode a signal, known as a 'reward prediction error', used by artificial intelligence algorithms for learning to choose advantageous actions, the study of the neural substrates for reward-based learning has been strongly influenced by computational theories....
computational model and comparing its performance to that of human learners can help to identifying shortcomings of existing theories and suggest areas for future research.•Models of machine learning proposed by either the experimental or theoretical machine learning communities may provide useful insights...
With better theories of how humans carve activities into means and ends, the informational theory of flow will become easier for researchers to falsify and for practitioners to apply. In addition to raising challenging new questions, the present work supports the recent movement to give computational...
languagegrammarkefirphonologycomputational-linguisticstheories UpdatedApr 27, 2021 Python adbar/German-NLP Star444 Code Issues Pull requests Curated list of open-access/open-source/off-the-shelf resources and tools developed with a particular focus on German ...
Each of our theories of mental representation provides some insight into how the mind works. However, these insights often seem incompatible, as the debate... ST Piantadosi 被引量: 0发表: 2020年 Scout: Rapid Exploration of Interface Layout Alternatives through High-Level Design Constraints Although...
Learning is a principled method for distilling predictive and therefore scienti?c theories from the data [Poggio and Smale 2003]. In a supervised learning scenario we are given a collection of say N training examples along with the desired output based on which we learn a concept/model to ...
It proposes a series of innovative theories, models and methods such as the representation t... DF Li - 《Studies in Fuzziness & Soft Computing》 被引量: 94发表: 2014年 Session F2G Multiple-Representation Online Learning System that Incorporates the Game of Monopoly Representation refers to "...
Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguisti