89,650 research outputs found
Adversarial learning games with deep learning models
© 2017 IEEE. Deep learning has been found to be vulnerable to changes in the data distribution. This means that inputs that have an imperceptibly and immeasurably small difference from training data correspond to a completely different class label in deep learning. Thus an existing deep learning network like a Convolutional Neural Network (CNN) is vulnerable to adversarial examples. We design an adversarial learning algorithm for supervised learning in general and CNNs in particular. Adversarial examples are generated by a game theoretic formulation on the performance of deep learning. In the game, the interaction between an intelligent adversary and deep learning model is a two-person sequential noncooperative Stackelberg game with stochastic payoff functions. The Stackelberg game is solved by the Nash equilibrium which is a pair of strategies (learner weights and genetic operations) from which there is no incentive for either learner or adversary to deviate. The algorithm performance is evaluated under different strategy spaces on MNIST handwritten digits data. We show that the Nash equilibrium leads to solutions robust to subsequent adversarial data manipulations. Results suggest that game theory and stochastic optimization algorithms can be used to study performance vulnerabilities in deep learning models
High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning
Edge machine learning involves the deployment of learning algorithms at the
wireless network edge so as to leverage massive mobile data for enabling
intelligent applications. The mainstream edge learning approach, federated
learning, has been developed based on distributed gradient descent. Based on
the approach, stochastic gradients are computed at edge devices and then
transmitted to an edge server for updating a global AI model. Since each
stochastic gradient is typically high-dimensional (with millions to billions of
coefficients), communication overhead becomes a bottleneck for edge learning.
To address this issue, we propose in this work a novel framework of
hierarchical stochastic gradient quantization and study its effect on the
learning performance. First, the framework features a practical hierarchical
architecture for decomposing the stochastic gradient into its norm and
normalized block gradients, and efficiently quantizes them using a uniform
quantizer and a low-dimensional codebook on a Grassmann manifold, respectively.
Subsequently, the quantized normalized block gradients are scaled and cascaded
to yield the quantized normalized stochastic gradient using a so-called hinge
vector designed under the criterion of minimum distortion. The hinge vector is
also efficiently compressed using another low-dimensional Grassmannian
quantizer. The other feature of the framework is a bit-allocation scheme for
reducing the quantization error. The scheme determines the resolutions of the
low-dimensional quantizers in the proposed framework. The framework is proved
to guarantee model convergency by analyzing the convergence rate as a function
of the quantization bits. Furthermore, by simulation, our design is shown to
substantially reduce the communication overhead compared with the
state-of-the-art signSGD scheme, while both achieve similar learning
accuracies
PEMODELAN PREDIKSI KUAT TEKAN BETON UMUR MUDA MENGGUNAKAN H2O'S DEEP LEARNING
Artificial Neural Network (ANN) is a Machine Learning (ML) algorithm which learn by itself and organize its thinking to solve problems. Although the learning process involves many hidden layers (Deep Learning) this algorithm still has weaknesses when faced with high noise data. Concrete mixture design data has a high enough noise caused by many unidentified / measurable aspects such as planning, design, manufacture of test specimens, maintenance, testing, diversity of physical and chemical properties, mixed formulas, mixed design errors, environmental conditions, and testing process. Information needs about the compressive strength of early age concrete (under 28 days) are often needed while the construction process is still ongoing. ANN has been tried to predict the compressive strength of concrete, but the results are less than optimal. This study aims to improve the ANN prediction model using an H2O’s Deep Learning based on a multi-layer feedforward artificial neural network that is trained with stochastic gradient descent using backpropagation. The H2O’s Deep Learning best model is achieved by 2 hidden layers- 50 hidden neurons and ReLU activation function with a RMSE value of 6,801. This Machine Learning model can be used as an alternative/ substitute for conventional mix designs, which are environmentally friendly, economical, and accurate. Future work with regard to the concrete industry, this model can be applied to create an intelligent Batching and Mixing Plants
Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions
This paper presents a data-driven approach for multi-robot coordination in
partially-observable domains based on Decentralized Partially Observable Markov
Decision Processes (Dec-POMDPs) and macro-actions (MAs). Dec-POMDPs provide a
general framework for cooperative sequential decision making under uncertainty
and MAs allow temporally extended and asynchronous action execution. To date,
most methods assume the underlying Dec-POMDP model is known a priori or a full
simulator is available during planning time. Previous methods which aim to
address these issues suffer from local optimality and sensitivity to initial
conditions. Additionally, few hardware demonstrations involving a large team of
heterogeneous robots and with long planning horizons exist. This work addresses
these gaps by proposing an iterative sampling based Expectation-Maximization
algorithm (iSEM) to learn polices using only trajectory data containing
observations, MAs, and rewards. Our experiments show the algorithm is able to
achieve better solution quality than the state-of-the-art learning-based
methods. We implement two variants of multi-robot Search and Rescue (SAR)
domains (with and without obstacles) on hardware to demonstrate the learned
policies can effectively control a team of distributed robots to cooperate in a
partially observable stochastic environment.Comment: Accepted to the 2017 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2017
Model-Free Learning of Optimal Beamformers for Passive IRS-Assisted Sumrate Maximization
Although Intelligent Reflective Surfaces (IRSs) are a cost-effective
technology promising high spectral efficiency in future wireless networks,
obtaining optimal IRS beamformers is a challenging problem with several
practical limitations. Assuming fully-passive, sensing-free IRS operation, we
introduce a new data-driven Zeroth-order Stochastic Gradient Ascent (ZoSGA)
algorithm for sumrate optimization in an IRS-aided downlink setting. ZoSGA does
not require access to channel model or network structure information, and
enables learning of optimal long-term IRS beamformers jointly with standard
short-term precoding, based only on conventional effective channel state
information. Supported by state-of-the-art (SOTA) convergence analysis,
detailed simulations confirm that ZoSGA exhibits SOTA empirical behavior as
well, consistently outperforming standard fully model-based baselines, in a
variety of scenarios
Online quantum mixture regression for trajectory learning by demonstration
In this work, we present the online Quantum Mixture Model (oQMM), which combines the merits of quantum mechanics and stochastic optimization. More specifically it allows for quantum effects on the mixture states, which in turn become a superposition of conventional mixture states. We propose an efficient stochastic online learning algorithm based on the online Expectation Maximization (EM), as well as a generation and decay scheme for model components. Our method is suitable for complex robotic applications, where data is abundant or where we wish to iteratively refine our model and conduct predictions during the course of learning. With a synthetic example, we show that the algorithm can achieve higher numerical stability. We also empirically demonstrate the efficacy of our method in well-known regression benchmark datasets. Under a trajectory Learning by Demonstration setting we employ a multi-shot learning application in joint angle space, where we observe higher quality of learning and reproduction. We compare against popular and well-established methods, widely adopted across the robotics community
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