Monotonic function ≈ Monotonic function
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Trust Region Policy Optimization Open
We describe an iterative procedure for optimizing policies, with guaranteed monotonic improvement. By making several approximations to the theoretically-justified procedure, we develop a practical algorithm, called Trust Region Policy Opti…
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An Improved Non-monotonic Transition System for Dependency Parsing Open
Transition-based dependency parsers usually use transition systems that monotonically extend partial parse states until they identify a complete parse tree.Honnibal et al. (2013) showed that greedy onebest parsing accuracy can be improved …
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Mish: A Self Regularized Non-Monotonic Neural Activation Function Open
The concept of non-linearity in a Neural Network is introduced by an
activation function which serves an integral role in the training and
performance evaluation of the network. Over the years of theoretical research,
many activation funct…
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Regularizing and Optimizing LSTM Language Models Open
Recurrent neural networks (RNNs), such as long short-term memory networks (LSTMs), serve as a fundamental building block for many sequence learning tasks, including machine translation, language modeling, and question answering. In this pa…
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Uniqueness of Radial Solutions for the Fractional Laplacian Open
We prove general uniqueness results for radial solutions of linear and nonlinear equations involving the fractional Laplacian (−Δ) s with s ∊ (0,1) for any space dimensions N ≥ 1. By extending a monotonicity formula found by Cabré and Sire…
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Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning Open
In many real-world settings, a team of agents must coordinate its behaviour while acting in a decentralised fashion. At the same time, it is often possible to train the agents in a centralised fashion where global state information is avai…
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Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism Open
The loss function for bounding box regression (BBR) is essential to object detection. Its good definition will bring significant performance improvement to the model. Most existing works assume that the examples in the training data are hi…
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QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning Open
In many real-world settings, a team of agents must coordinate their behaviour while acting in a decentralised way. At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, wh…
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Simulating masonry wall behaviour using a simplified micro-model approach Open
In this paper, a simplified micro-model approach utilising a combination of plasticity-based constitutive models and the extended finite element method (XFEM) is proposed. The approach is shown to be an efficient means of simulating the th…
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Trace-distance measure of coherence Open
We show that trace distance measure of coherence is a strong monotone for all qubit and, so called, $X$ states. An expression for the trace distance coherence for all pure states and a semi definite program for arbitrary states is provided…
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Fidelity and trace-norm distances for quantifying coherence Open
We investigate the coherence measures induced by fidelity and trace norm,\nbased on the recent proposed coherence quantification in [Phys. Rev. Lett. 113,\n140401, 2014]. We show that the fidelity of coherence does not in general\nsatisfy …
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Neural Cognitive Diagnosis for Intelligent Education Systems Open
Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts. Existing approaches usually mine linear interactions of student exercising proces…
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An Optimal Iterative Learning Control Approach for Linear Systems With Nonuniform Trial Lengths Under Input Constraints Open
In practical applications of iterative learning control (ILC), the repetitive process may end up early by accident during the performance improvement along the trial axis, which yields the nonuniform trial length problem. For such practica…
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On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning Open
In this paper, we utilize results from convex analysis and monotone operator theory to derive additional properties of the softmax function that have not yet been covered in the existing literature. In particular, we show that the softmax …
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A Forward-Backward Splitting Method for Monotone Inclusions Without Cocoercivity Open
In this work, we propose a simple modification of the forward-backward splitting method for finding a zero in the sum of two monotone operators. Our method converges under the same assumptions as Tseng's forward-backward-forward method, na…
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Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search Open
Recently, text-to-speech (TTS) models such as FastSpeech and ParaNet have been proposed to generate mel-spectrograms from text in parallel. Despite the advantage, the parallel TTS models cannot be trained without guidance from autoregressi…
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Nonmonotonic Energy Dependence of Net-Proton Number Fluctuations Open
Nonmonotonic variation with collision energy (sqrt[s_{NN}]) of the moments of the net-baryon number distribution in heavy-ion collisions, related to the correlation length and the susceptibilities of the system, is suggested as a signature…
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Online and Linear-Time Attention by Enforcing Monotonic Alignments Open
Recurrent neural network models with an attention mechanism have proven to be extremely effective on a wide variety of sequence-to-sequence problems. However, the fact that soft attention mechanisms perform a pass over the entire input seq…
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The Hypervolume Indicator Open
The hypervolume indicator is one of the most used set-quality indicators for the assessment of stochastic multiobjective optimizers, as well as for selection in evolutionary multiobjective optimization algorithms. Its theoretical propertie…
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Cyclical Learning Rates for Training Neural Networks Open
It is known that the learning rate is the most important hyper-parameter to tune for training deep neural networks. This paper describes a new method for setting the learning rate, named cyclical learning rates, which practically eliminate…
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Quantum-coherence quantifiers based on the Tsallis relativeentropies Open
The concept of coherence is one of cornerstones in physics. The development\nof quantum information science has lead to renewed interest in properly\napproaching the coherence at the quantum level. Various measures could be\nproposed to qu…
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Generalized Singular Value Thresholding Open
This work studies the Generalized Singular Value Thresholding (GSVT) operator associated with a nonconvex function g defined on the singular values of X. We prove that GSVT can be obtained by performing the proximal operator of g on the si…
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Preaggregation Functions: Construction and an Application Open
In this work we introduce the notion of preaggregation
\nfunction. Such a function satisfies the same boundary
\nconditions as an aggregation function, but, instead of requiring
\nmonotonicity, only monotonicity along some fixed direction …
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Construction of the SILAM Eulerian atmospheric dispersion model based on the advection algorithm of Michael Galperin Open
The paper presents the transport module of the System for Integrated modeLling of Atmospheric coMposition SILAM v.5 based on the advection algorithm of Michael Galperin. This advection routine, so far weakly presented in the international …
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Symbolic Discovery of Optimization Algorithms Open
We present a method to formulate algorithm discovery as program search, and apply it to discover optimization algorithms for deep neural network training. We leverage efficient search techniques to explore an infinite and sparse program sp…
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Modelling monotonic effects of ordinal predictors in Bayesian regression models Open
Ordinal predictors are commonly used in regression models. They are often incorrectly treated as either nominal or metric, thus under‐ or overestimating the information contained. Such practices may lead to worse inference and predictions …
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Multi-Topic Misinformation Blocking With Budget Constraint on Online Social Networks Open
Along with the development of Information Technology, Online Social Networks (OSN) are constantly developing and have become popular media in the world. Besides communication enhancement benefits, OSN have such limitations on rapid spread …
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Methods of Nonlinear Analysis: Applications to Differential Equations Open
In this book, fundamental methods of nonlinear analysis are introduced, discussed and illustrated in straightforward examples. Each method considered is motivated and explained in its general form, but presented in an abstract framework as…
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Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement Open
The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network. Our method trains a lightweight deep network, DCE-Net,…
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Dynamical Systems Coupled with Monotone Set-Valued Operators: Formalisms, Applications, Well-Posedness, and Stability Open
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