177,408 research outputs found
Guglielmo di Alnwick e gli infinita in actu
No full textIl contributo analizza il concetto di infinto in atto nell'opera di Guglielmo di Alwnin
People's Perceptions of Intellectuals
No full text(Statement of Responsibility) by Francine R. Gerace(Thesis) Thesis (B.A.) -- New College of Florida, 1977(Electronic Access) RESTRICTED TO NCF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE(Bibliography) Includes bibliographical references.(Source of Description) This bibliographic record is available under the Creative Commons CC0 public domain dedication. The New College of Florida, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.(Local) Faculty Sponsor: Rosel, Natali
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
La rivista «Nuovi Argomenti»: critica, letteratura e società. Atti del Convegno di Studi
No full textStatistical Mechanics of Transfer Learning in Fully Connected Networks in the Proportional Limit
No full textTransfer learning (TL) is a well-established machine learning technique to boost the generalization performance on a specific (target) task using information gained from a related (source) task, and it crucially depends on the ability of a network to learn useful features. Leveraging recent analytical progress in the proportional regime of deep learning theory (i.e., the limit where the size of the training set P and the size of the hidden layers N are taken to infinity keeping their ratio alpha = P/N finite), in this Letter we develop a novel single-instance Franz-Parisi formalism that yields an effective theory for TL in fully connected neural networks. Unlike the (lazy-training) infinite-width limit, where TL is ineffective, we demonstrate that in the proportional limit TL occurs due to a renormalized source-target kernel that quantifies their relatedness and determines whether TL is beneficial for generalization
From statistical inference to a differential learning rule for stochastic neural networks
No full textStochastic neural networks are a prototypical computational device able to build a probabilistic representation of an ensemble of external stimuli. Building on the relationship between inference and learning, we derive a synaptic plasticity rule that relies only on delayed activity correlations, and that shows a number of remarkable features. Our delayed-correlations matching (DCM) rule satisfies some basic requirements for biological feasibility: finite and noisy afferent signals, Dale's principle and asymmetry of synaptic connections, locality of the weight update computations. Nevertheless, the DCM rule is capable of storing a large, extensive number of patterns as attractors in a stochastic recurrent neural network, under general scenarios without requiring any modification: it can deal with correlated patterns, a broad range of architectures (with or without hidden neuronal states), one-shot learning with the palimpsest property, all the while avoiding the proliferation of spurious attractors. When hidden units are present, our learning rule can be employed to construct Boltzmann machine-like generative models, exploiting the addition of hidden neurons in feature extraction and classification tasks
A Preconditioned Finite Element Method for the p-Laplacian Parabolic Equation
No full textIn this paper we propose a method for the discretization of the parabolic p-Laplacian equation. In particular we use alternately either the backward Euler scheme or the Crank-Nicholson scheme for the time-discretization and the first order Finite Elements Method for space-discretization. To obtain the numerical solution we have to invert a block Toeplitz with Toeplitz blocks matrix. To this aim we use a Conjugate Gradient (CG) algorithm preconditioned by a block circulant with circulant blocks matrix. A Two-Dimensional Discrete Sine-Cosine Fast Transform is applied to invert the block circulant with circulant blocks matrix. The experimental results show how the application of the preconditioner reduces the iterations of the CG of about the 56%-75%
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