Results 11 to 20 of about 414,531 (51)
What is the Machine Learning? [PDF]
, 2018 Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. To address this concern, we explore a data planing procedure for identifying combinations of variables -
Christian Saligaut (426443) +9 more
core +2 more sources
Adversarial attacks hidden in plain sight
, 2020 Convolutional neural networks have been used to achieve a string of successes during recent years, but their lack of interpretability remains a serious issue.
DLK Yamins +4 more
core +1 more source
, 2018 Universal induction relies on some general search procedure that is doomed to be inefficient. One possibility to achieve both generality and efficiency is to specialize this procedure w.r.t. any given narrow task.
A Graves +7 more
core +1 more source
, 2017 The precise modeling of subatomic particle interactions and propagation through matter is paramount for the advancement of nuclear and particle physics searches and precision measurements.
de Oliveira, Luke +2 more
core +1 more source
Learning proofs for the classification of nilpotent semigroups [PDF]
arXiv, 2021 Machine learning is applied to find proofs, with smaller or smallest numbers of nodes, for the classification of 4-nilpotent semigroups.
arxiv
A Topic Modeling Toolbox Using Belief Propagation [PDF]
, 2012 Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model for probabilistic topic modeling, which attracts worldwide interests and touches on many important applications in text mining, computer vision and computational biology.
Zeng, Jia
core
The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning [PDF]
arXiv, 2023 With the advance of the powerful heterogeneous, parallel and distributed computing systems and ever increasing immense amount of data, machine learning has become an indispensable part of cutting-edge technology, scientific research and consumer products. In this study, we present a review of modern machine and deep learning.
arxiv
The Machine Learning Landscape of Top Taggers
, 2019 Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output.
Butter, A. +26 more
core +1 more source
Mathematical Perspective of Machine Learning [PDF]
arXiv, 2020 We take a closer look at some theoretical challenges of Machine Learning as a function approximation, gradient descent as the default optimization algorithm, limitations of fixed length and width networks and a different approach to RNNs from a mathematical perspective.
arxiv
Discriminative Recurrent Sparse Auto-Encoders [PDF]
, 2013 We present the discriminative recurrent sparse auto-encoder model, comprising a recurrent encoder of rectified linear units, unrolled for a fixed number of iterations, and connected to two linear decoders that reconstruct the input and predict its ...
LeCun, Yann, Rolfe, Jason Tyler
core