Data and Parameter Scaling Laws for Neural Machine Translation

We observe that the development cross-entropy loss of supervised neural machine translation models scales like a power law with the amount of training data and the number of non-embedding parameters in the model. We discuss some practical implications of these results, such as predicting BLEU achieved by large scale models and predicting the ROI of labeling data in low-resource language pairs.

The Power of Scale for Parameter-Efficient Prompt Tuning

In this work, we explore prompt tuning,'' a simple yet effective mechanism for learning soft prompts'' to condition frozen language models to perform specific downstream tasks. Unlike the discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signals from any number of labeled examples. Our end-to-end learned approach outperforms GPT-3's few-shot learning by a large margin. More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of parameters, our method closes the gap'' and matches the strong performance of model tuning (where all model weights are tuned). This finding is especially relevant because large models are costly to share and serve and the ability to reuse one frozen model for multiple downstream tasks can ease this burden. Our method can be seen as a simplification of the recently proposed prefix tuning'' of Li and Liang (2021) and we provide a comparison to this and other similar approaches. Finally, we show that conditioning a frozen model with soft prompts confers benefits in robustness to domain transfer and enables efficient prompt ensembling.'' We release code and model checkpoints to reproduce our experiments.

Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages

Abstract Most combinations of NLP tasks and language varieties lack in-domain examples for supervised training because of the paucity of annotated data. How can neural models make sample-efficient generalizations from task--language combinations with available data to low-resource ones? In this work, we propose a Bayesian generative model for the space of neural parameters. We assume that this space can be factorized into latent variables for each language and each task. We infer the posteriors over such latent variables based on data from seen task--language combinations through variational inference. This enables zero-shot classification on unseen combinations at prediction time. For instance, given training data for named entity recognition (NER) in Vietnamese and for part-of-speech (POS) tagging in Wolof, our model can perform accurate predictions for NER in Wolof. In particular, we experiment with a typologically diverse sample of 33 languages from 4 continents and 11 families, and show that our model yields comparable or better results than state-of-the-art, zero-shot cross-lingual transfer methods. Our code is available at github.com/cambridgeltl/parameter-factorization.

conformal transformations of Parabolically- Kahlerian flat Spaces

In this paper remembered important expressions and theorems related of paper, After word find conditions to be exist coformal transformation and Affine Transformation in Parabolically- Kahlerian flat Spaces, and limiting the number of motion parameter in this transformations .

Isometric and projective transformations of Parabolically- Kahlerian flat Spaces

In this paper remembered important expressions and theorems related of paper, After word try to find conditions to be exist Isometric transformation and projective Transformation in in Parabolically- Kahlerian flat Spaces, and try to limiting the number of motion parameter in this transformations .

The Range of Correspond Between One Parameter Model and Pair Parameter Model in Psychasthenia Scale Item Fitting

The current research is aimed at scaling Psychasthenia scale of Minnesota Multiphasic Personality Inventory 2 , access to a new form and brief to the test, it,s liberal from the sample and items properties, using one and pair parameter models, and testing the effect of two variables in the results of scaling Psychasthenia scale (the model used, the sample size) with using criteria of accuracy as the Standard Error, Relaibility and Information Function.

A half-numeric method for Calculation the volume resulting from the intersection of a cylinder with a ball as a function of the impact parameter by using the Mathematical program mathCAD

We have developed an analytic expression for the volume of intersection of any cylinder with any sphere in the usual threedimensional space, and give examples of its usefulness for physics. Produced from surface collisions either elastic collision, or inelastic collision, for the incoming energies greater than the binding energy of the nucleons in the nucleus, the reaction occurs and thus the effective size (what the target nucleus see from the nuclear material in the projectle, and What the projectle see from the nuclear material in the target nucleus) is formed and so for the different collisions parameters .

Tow Fractal (Van-Koch)Antenna Design, fabricate and measured work on 1 GHz and 10 GHz

Fractal antenna are considered actually as the most important large band antenna, due to their design parameters that are based on fractal geometry. The long distance between two specific points of antenna and hisself similarity allow to obtain a multiple resonance frequencies (Wide Band), which is used for several application. The using of dipole antenna in form of Van-Koch curve offer multi-resonance antenna, the Nec simulator is used to design this antenna. The proposed antenna had been fabricated and measured in the antenna laboratory at two central frequencies 1GHz and 10 GHz. Both theoretical and experimental results were very closed in spite of imperfect experiment condition. The Van- Koch antenna fabrication and measurement in laboratory gives the possibility to designing and applying this antenna type, additionally we can study the change of antenna parameters ( gain, rayon diagram … ) when the fractal parameters varies.

The Degree of Participation of Cooperating in the Parameter Field Training for Kindergarten Students in the College of Education at Albaath University

the aim of this research is to know the degree of participation of the parameter in the three stages of field training "boot, viewing, sharing, from the viewpoint of the students majoring parameters kindergarten in al-Baath University College of education.

The Use of The Method of Thermodynamics Similarity to Describe some of Thermo Characters Using the Law of Corresponding States

This Research aims to study the heat capacity for some organic liquids depending on the law of corresponding states , using some analytic equations which are related with the reduced temperatures and the parameter of similarity in ranges of temperatures below the critical temperature . Then to conclude the heat capacity with a constant pressure and compare the results with values of the heat capacity in other works which have been obtained using different methods , theoretical or experimental.