556 research outputs found
Deep neural networks in acoustic model
L'estudiant m'ha contactat amb el requeriment d'una oferta per matricular-se i aquesta oferta respon a la seva petició. Després de confirmar amb Secretaria Acadèmica que està acceptat a destinació, deixem títol, descripció, objectius, i tutor extern per determinar quan arribi a destí.Do implementation of a training of a deep neural network acoustic model for speech recognitio
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%
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks
Gradient inversion attacks are an ubiquitous threat in federated learning as
they exploit gradient leakage to reconstruct supposedly private training data.
Recent work has proposed to prevent gradient leakage without loss of model
utility by incorporating a PRivacy EnhanCing mODulE (PRECODE) based on
variational modeling. Without further analysis, it was shown that PRECODE
successfully protects against gradient inversion attacks. In this paper, we
make multiple contributions. First, we investigate the effect of PRECODE on
gradient inversion attacks to reveal its underlying working principle. We show
that variational modeling introduces stochasticity into the gradients of
PRECODE and the subsequent layers in a neural network. The stochastic gradients
of these layers prevent iterative gradient inversion attacks from converging.
Second, we formulate an attack that disables the privacy preserving effect of
PRECODE by purposefully omitting stochastic gradients during attack
optimization. To preserve the privacy preserving effect of PRECODE, our
analysis reveals that variational modeling must be placed early in the network.
However, early placement of PRECODE is typically not feasible due to reduced
model utility and the exploding number of additional model parameters.
Therefore, as a third contribution, we propose a novel privacy module -- the
Convolutional Variational Bottleneck (CVB) -- that can be placed early in a
neural network without suffering from these drawbacks. We conduct an extensive
empirical study on three seminal model architectures and six image
classification datasets. We find that all architectures are susceptible to
gradient leakage attacks, which can be prevented by our proposed CVB. Compared
to PRECODE, we show that our novel privacy module requires fewer trainable
parameters, and thus computational and communication costs, to effectively
preserve privacy.Comment: 14 pages (12 figures 6 tables) + 6 pages supplementary materials (6
tables). Under review. This work has been submitted to the IEEE for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessible. arXiv admin note: substantial text
overlap with arXiv:2208.0476
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