83 research outputs found
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
Stochastic gradient descent (SGD) is widely believed to perform implicit
regularization when used to train deep neural networks, but the precise manner
in which this occurs has thus far been elusive. We prove that SGD minimizes an
average potential over the posterior distribution of weights along with an
entropic regularization term. This potential is however not the original loss
function in general. So SGD does perform variational inference, but for a
different loss than the one used to compute the gradients. Even more
surprisingly, SGD does not even converge in the classical sense: we show that
the most likely trajectories of SGD for deep networks do not behave like
Brownian motion around critical points. Instead, they resemble closed loops
with deterministic components. We prove that such "out-of-equilibrium" behavior
is a consequence of highly non-isotropic gradient noise in SGD; the covariance
matrix of mini-batch gradients for deep networks has a rank as small as 1% of
its dimension. We provide extensive empirical validation of these claims,
proven in the appendix
A geometric interpretation of stochastic gradient descent using diffusion metrics
This paper is a step towards developing a geometric understanding of a popular algorithm for training deep neural networks named stochastic gradient descent (SGD). We built upon a recent result which observed that the noise in SGD while training typical networks is highly non-isotropic. That motivated a deterministic model in which the trajectories of our dynamical systems are described via geodesics of a family of metrics arising from a certain diffusion matrix; namely, the covariance of the stochastic gradients in SGD. Our model is analogous to models in general relativity: the role of the electromagnetic field in the latter is played by the gradient of the loss function of a deep network in the former
Machine Learning Technique Based Fake News Detection
False news has received attention from both the general public and the
scholarly world. Such false information has the ability to affect public
perception, giving nefarious groups the chance to influence the results of
public events like elections. Anyone can share fake news or facts about anyone
or anything for their personal gain or to cause someone trouble. Also,
information varies depending on the part of the world it is shared on. Thus, in
this paper, we have trained a model to classify fake and true news by utilizing
the 1876 news data from our collected dataset. We have preprocessed the data to
get clean and filtered texts by following the Natural Language Processing
approaches. Our research conducts 3 popular Machine Learning (Stochastic
gradient descent, Na\"ive Bayes, Logistic Regression,) and 2 Deep Learning
(Long-Short Term Memory, ASGD Weight-Dropped LSTM, or AWD-LSTM) algorithms.
After we have found our best Naive Bayes classifier with 56% accuracy and an
F1-macro score of an average of 32%
FORECASTING MISSING DATA USING DIFFERENT METHODS FOR ROAD MAINTAINERS
Observations collected from meteorological stations that are available to road maintainers and used for experimental purposes in this paper. Unfortunately, these observations are insufficient to make good forecasting that is needed for road maintainers. Those meteorological stations are located next to the road surface in the territory of the Republic of Latvia. The road maintainers can make forecasting using this data what is needed for the winter months. It is up to the road maintainers in winter months to process decision-making on road surface smudging with anti-slip chemical materials. The missing data in each meteorological station exists from time to time. The paper represents the possibility of using several approaches to fill out these missing data. This process is needed to be more accurate in predicting specific parameters aggregated from meteorological stations. These approaches are compared between the three closest meteorological stations available in the Republic of Latvia. The relevant data are for the winter months of 2017-2018. To conclude which is more accurate with VAS "Latvijas valsts celi" data set
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