scholarly journals Predictive Coding, Variational Autoencoders, and Biological Connections

2021 ◽  
pp. 1-44
Author(s):  
Joseph Marino
Keyword(s):  
Machine Learning ◽  
Lateral Inhibition ◽  
Predictive Coding ◽  
Common Origin ◽  
Mathematical Framework ◽  
New Directions ◽  
Deep Networks ◽  
Theoretical Neuroscience ◽  
Respective Field ◽  
The Common

Abstract We present a review of predictive coding, from theoretical neuroscience, and variational autoencoders, from machine learning, identifying the common origin and mathematical framework underlying both areas. As each area is prominent within its respective field, more firmly connecting these areas could prove useful in the dialogue between neuroscience and machine learning. After reviewing each area, we discuss two possible correspondences implied by this perspective: cortical pyramidal dendrites as analogous to (nonlinear) deep networks and lateral inhibition as analogous to normalizing flows. These connections may provide new directions for further investigations in each field.

scholarly journals III.—Morphological Studies on the Echinoidea Holectypoida and their Allies

1917 ◽  
Vol 4 (6) ◽  
pp. 249-256
Author(s):  
Herbert L. Hawkins
Keyword(s):  
Common Origin ◽  
Normal Type ◽  
The Third ◽  
Morphological Studies ◽  
Apical System ◽  
The Common

The characters of the apical system of a series of Holectypus hemisphæricus from the same horizon at two localities in Dorsetshire are analysed and described. It is found that the average relations of the plates of the system are different at the two localities, although certain numbers of identical forms occur at both. Out of 189 specimens (from both localities), 40 show serious departures from the normal type. These abnormalities are of three classes. One, the most prevalent, consists in the presence of madreporic pores on genital 3, in addition to the normal perforation of genital 2. This is regarded as a “progressive variant” in the direction of Discoides. The second, occurring in three specimens, consists in the interpolation of a supernumerary plate within the system. It is suggested that this may be either a “regressive variant” towards Acrosalenia, or a “progressive variant” towards Nucleolites (as illustrated by N.orlicularis). In neither case would this variation coincide with actual phyletic sequence, so that it is styled “parallel variation”. The third type of variant, seen in one specimen only, combines both the first and second types, and in addition shows an absence of genital 5 and a corresponding increase in the size of the posterior oculars, which meet round the back of the system. The variation in this specimen is interpreted as being “progressive” towards Discoides, “parallel progressive” or “regressive” towards Nucleolites or Acrosalenia respectively, and “progressive” towards Conulus. There are indications of a different series of variants in the Holectypus depressus from the Cornbrash. The high percentage of variation in the composition of the apical system of Holectypus is regarded as an indication of the evolutional activity of the genus, and of its near approximation in time and phylogeny to the common origin of many of the groups of Irregular Echinoids.

scholarly journals Human Induction in Machine Learning

2021 ◽  
Vol 54 (3) ◽  
pp. 1-18
Author(s):  
Petr Spelda ◽  
Vit Stritecky
Keyword(s):  
Machine Learning ◽  
A Posteriori ◽  
Experimental Paradigm ◽  
Risk Minimisation ◽  
Inductive Risk ◽  
Acceptable Accuracy ◽  
Space And Time ◽  
The Common ◽  
Testing Set

As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, then an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target environments. Yet the latter part of the contract depends on human inductive predictions or generalisations, which infer a uniformity between the trained ML model and the targets. The article asks how we justify the contract between human and machine learning. It is argued that the justification becomes a pressing issue when we use ML to reach “elsewhere” in space and time or deploy ML models in non-benign environments. The article argues that the only viable version of the contract can be based on optimality (instead of on reliability, which cannot be justified without circularity) and aligns this position with Schurz's optimality justification. It is shown that when dealing with inaccessible/unstable ground-truths (“elsewhere” and non-benign targets), the optimality justification undergoes a slight change, which should reflect critically on our epistemic ambitions. Therefore, the study of ML robustness should involve not only heuristics that lead to acceptable accuracies on testing sets. The justification of human inductive predictions or generalisations about the uniformity between ML models and targets should be included as well. Without it, the assumptions about inductive risk minimisation in ML are not addressed in full.

Variational Methods for Machine Learning with Applications to Deep Networks

2021 ◽  
Author(s):  
Lucas Pinheiro Cinelli ◽  
Matheus Araújo Marins ◽  
Eduardo Antônio Barros da Silva ◽  
Sérgio Lima Netto
Keyword(s):  
Machine Learning ◽  
Variational Methods ◽  
Deep Networks

PODC 2020 Review

2021 ◽  
Vol 51 (4) ◽  
pp. 75-81
Author(s):  
Ahad Mirza Baig ◽  
Alkida Balliu ◽  
Peter Davies ◽  
Michal Dory
Keyword(s):  
Machine Learning ◽  
Distributed Computing ◽  
Keynote Speaker ◽  
Lively Discussion ◽  
Theoretical Understanding ◽  
New Directions ◽  
New Ideas ◽  
New Challenges ◽  
The Impact ◽  
Distributed Machine Learning

Rachid Guerraoui was the rst keynote speaker, and he got things o to a great start by discussing the broad relevance of the research done in our community relative to both industry and academia. He rst argued that, in some sense, the fact that distributed computing is so pervasive nowadays could end up sti ing progress in our community by inducing people to work on marginal problems, and becoming isolated. His rst suggestion was to try to understand and incorporate new ideas coming from applied elds into our research, and argued that this has been historically very successful. He illustrated this point via the distributed payment problem, which appears in the context of blockchains, in particular Bitcoin, but then turned out to be very theoretically interesting; furthermore, the theoretical understanding of the problem inspired new practical protocols. He then went further to discuss new directions in distributed computing, such as the COVID tracing problem, and new challenges in Byzantine-resilient distributed machine learning. Another source of innovation Rachid suggested was hardware innovations, which he illustrated with work studying the impact of RDMA-based primitives on fundamental problems in distributed computing. The talk concluded with a very lively discussion.

scholarly journals Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Abdulkadir Canatar ◽  
Blake Bordelon ◽  
Cengiz Pehlevan
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Kernel Regression ◽  
Learning Task ◽  
Learning Curves ◽  
Generalization Error ◽  
Theoretical Understanding ◽  
Classical Statistics ◽  
Deep Networks ◽  
Model Alignment

AbstractA theoretical understanding of generalization remains an open problem for many machine learning models, including deep networks where overparameterization leads to better performance, contradicting the conventional wisdom from classical statistics. Here, we investigate generalization error for kernel regression, which, besides being a popular machine learning method, also describes certain infinitely overparameterized neural networks. We use techniques from statistical mechanics to derive an analytical expression for generalization error applicable to any kernel and data distribution. We present applications of our theory to real and synthetic datasets, and for many kernels including those that arise from training deep networks in the infinite-width limit. We elucidate an inductive bias of kernel regression to explain data with simple functions, characterize whether a kernel is compatible with a learning task, and show that more data may impair generalization when noisy or not expressible by the kernel, leading to non-monotonic learning curves with possibly many peaks.

The Possible Common Origin of tRNA and 5S rRNA

1983 ◽  
Vol 38 (5-6) ◽  
pp. 501-504 ◽  
Author(s):  
Mária Ujhelyi
Keyword(s):  
Common Ancestor ◽  
Common Origin ◽  
5S Rrna ◽  
Mitochondrial Trna ◽  
Trna Molecule ◽  
The Common

Seryl tRNA (anticodon GCU) from mammalian mito­chondria shows in comparison to other mitochondrial tRNAs additional special features differing from the generalized tRNA model. When arranged in the tradi­tional cloverleaf form, eight bases fall within the TΨC loop, and the entire dihydrouridine loop is lacking. This seryl tRNA molecule is therefore shorter than other tRNAs. It was originally thought to represent a mito­chondrial analogon of 5 S rRNA and its precise classifica­tion is still disputed. The present studies suggest that this mitochondrial tRNA represents a fossil molecule which is related to the common ancestor of the present tRNA and 5 S rRNA molecules.

scholarly journals Unraveling city-specific signature and identifying sample origin locations for the data from CAMDA MetaSUB challenge

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Runzhi Zhang ◽  
Alejandro R. Walker ◽  
Susmita Datta
Keyword(s):  
Machine Learning ◽  
Error Rates ◽  
Average Error ◽  
Importance Score ◽  
Learning Methods ◽  
Principal Coordinates Analysis ◽  
Principal Coordinates ◽  
Total Variability ◽  
The Common ◽  
Feature Selecting

Abstract Background Composition of microbial communities can be location-specific, and the different abundance of taxon within location could help us to unravel city-specific signature and predict the sample origin locations accurately. In this study, the whole genome shotgun (WGS) metagenomics data from samples across 16 cities around the world and samples from another 8 cities were provided as the main and mystery datasets respectively as the part of the CAMDA 2019 MetaSUB “Forensic Challenge”. The feature selecting, normalization, three methods of machine learning, PCoA (Principal Coordinates Analysis) and ANCOM (Analysis of composition of microbiomes) were conducted for both the main and mystery datasets. Results Features selecting, combined with the machines learning methods, revealed that the combination of the common features was effective for predicting the origin of the samples. The average error rates of 11.93 and 30.37% of three machine learning methods were obtained for main and mystery datasets respectively. Using the samples from main dataset to predict the labels of samples from mystery dataset, nearly 89.98% of the test samples could be correctly labeled as “mystery” samples. PCoA showed that nearly 60% of the total variability of the data could be explained by the first two PCoA axes. Although many cities overlapped, the separation of some cities was found in PCoA. The results of ANCOM, combined with importance score from the Random Forest, indicated that the common “family”, “order” of the main-dataset and the common “order” of the mystery dataset provided the most efficient information for prediction respectively. Conclusions The results of the classification suggested that the composition of the microbiomes was distinctive across the cities, which could be used to identify the sample origins. This was also supported by the results from ANCOM and importance score from the RF. In addition, the accuracy of the prediction could be improved by more samples and better sequencing depth.

1. Sketch of the Recent Progress of Sanskrit Literature

1866 ◽  
Vol 5 ◽  
pp. 90-91
Author(s):  
John Muir
Keyword(s):  
Recent Progress ◽  
Indian Philosophy ◽  
Common Origin ◽  
Indian Literature ◽  
Sanskrit Literature ◽  
The Common

After giving a sketch of the first beginnings of these studies in India, and their further prosecution in Europe, the author adverted to the relations of Sanskrit with the Greek, Latin, and Teutonic languages, and showed how this affinity established the common origin of the nations by which these languages have been spoken. He then proceeded to give an account of Indian literature, commencing with the hymns and other constituent parts of the Vedas, and then proceeding to the principal systems of Indian philosophy, of which he furnished an outline.

scholarly journals Les catastrophes naturelles dans les romans caribéens d’expression française

Il Tolomeo ◽  
2020 ◽  
Author(s):  
Alessia Vignoli
Keyword(s):  
Native Americans ◽  
Natural Hazards ◽  
Caribbean Literature ◽  
Common Origin ◽  
Caribbean Region ◽  
Common Features ◽  
African Slaves ◽  
European Colonization ◽  
The Common ◽  
The Caribbean

The notion of ‘disaster’ pervades the Caribbean thought. The common origin of the Caribbean region, the European colonization, caused two disasters: the extermination of Native Americans and the deportation of African slaves. The union between nature and the oppressed people against the oppressor resulted in the creation of an environmental conscience that the Caribbean literature has often expressed. This essay will investigate the common points shared by some Haitian, Martinican and Guadeloupean authors in the writing of natural hazards. It will show that, despite the diversity that marks the Caribbean, there is a repetition of common features that proves its geopoetic unity.

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