bluuebunny/crossref_metadata_2025 huggingface dataset:
Ranking Sets of Defeasible Elements in Preferential Approaches to Structured Argumentation: Postulates, Relations, and Characterizations
Authors: Maly Jan , Wallner Johannes P.
Published: September 2022

Preferences play a key role in computational argumentation in AI, as they reflect various notions of argument strength vital for the representation of argumentation. Within central formal approaches to structured argumentation, preferential approaches are applied by lifting preferences over defeasible elements to rankings over sets of defeasible elements, in order to be able to compare the relative strength of two arguments and their respective defeasible constituents. To overcome the current gap in the scientific landscape, we give in this paper a general study of the critical component of lifting operators in structured argumentation. We survey existing lifting operators scattered in the literature of argumentation theory, social choice, and utility theory, and show fundamental relations and properties of these operators. Extending existing works from argumentation and social choice, we propose a list of postulates for lifting operations, and give a complete picture of (non-)satisfaction for the considered operators. Based on our postulates, we present impossibility results, stating for which sets of postulates there is no hope of satisfaction, and for two main lifting operators presented in structured argumentation, Elitist and Democratic, we give a full characterization in terms of our postulates.
GENSYNTH: Synthesizing Datalog Programs without Language Bias
Authors: Mendelson Jonathan , Naik Aaditya , Raghothaman Mukund , Naik Mayur
Published: September 2022

Techniques for learning logic programs from data typically rely on language bias mechanisms to restrict the hypothesis space. These methods are therefore limited by the user's ability to tune them such that the hypothesis space is simultaneously large enough to include the target program but small enough to admit a tractable search. We propose a technique to learn Datalog programs from input-output examples without requiring the user to specify any language bias. It employs an evolutionary search strategy that mutates candidate programs and evaluates their fitness on the examples using an off-the-shelf Datalog interpreter. We have implemented our approach in a tool called GenSynth and evaluate it on diverse tasks from knowledge discovery, program analysis, and relational queries. Our experiments show that GenSynth can learn correct programs from few examples, including for tasks that require recursion and invented predicates, and is robust to noise.
ChronoR: Rotation Based Temporal Knowledge Graph Embedding
Authors: Sadeghian Ali , Armandpour Mohammadreza , Colas Anthony , Wang Daisy Zhe
Published: September 2022

Despite the importance and abundance of temporal knowledge graphs, most of the current research has been focused on reasoning on static graphs. In this paper, we study the challenging problem of inference over temporal knowledge graphs. In particular, the task of temporal link prediction. In general, this is a difficult task due to data non-stationarity, data heterogeneity, and its complex temporal dependencies.
We propose Chronological Rotation embedding (ChronoR), a novel model for learning representations for entities, relations, and time. Learning dense representations is frequently used as an efficient and versatile method to perform reasoning on knowledge graphs. The proposed model learns a k-dimensional rotation transformation parametrized by relation and time, such that after each fact's head entity is transformed using the rotation, it falls near its corresponding tail entity. By using high dimensional rotation as its transformation operator, ChronoR captures rich interaction between the temporal and multi-relational characteristics of a Temporal Knowledge Graph. Experimentally, we show that ChronoR is able to outperform many of the state-of-the-art methods on the benchmark datasets for temporal knowledge graph link prediction.
Quantification of Resource Production Incompleteness
Authors: Salhi Yakoub
Published: September 2022

In a situation where an agent has to produce specific resources using the available ones, it may not be possible to achieve the complete goal, but only obtaining some of its parts.This incompleteness problem calls for reasoning models to make rational decisions. In this paper, we introduce a logic-based framework for measuring resource production incompleteness: the greater the value returned by a measure, the greater is the intensity of incompleteness. After motivating our work by describing situations where the incompleteness measures can be applied, we introduce our framework by using a postulate-based approach. To some extent, the incompleteness measures can be seen as a counterpart of inconsistency measures in resource logics.
Here, intuitionistic affine logic is used for representing and reasoning about resource consummation and production. Besides, we propose different notions that are useful for defining different types of incompleteness measures. We also present several measures to illustrate the introduced concepts and notions.
On-the-fly Synthesis for LTL over Finite Traces
Authors: Xiao Shengping , Li Jianwen , Zhu Shufang , Shi Yingying , Pu Geguang , Vardi Moshe
Published: September 2022

We present a new synthesis framework based on the on-the-fly DFA construction for LTL over finite traces (LTLf ). Extant approaches rely heavily on the construction of the complete DFA w.r.t. the input LTLf formula, whose size can be doubly exponential to the size of the formula in the worst case. Under those approaches, the synthesis cannot be conducted unless the whole DFA is completely constructed, which is not only inefficient but also not scalable in practice. Indeed, the DFA construction is the main bottleneck of LTLf synthesis in prior work. To mitigate this challenge, we follow two steps in this paper: Firstly, we present several light-weight pre-processing techniques such that the synthesis result can be obtained even without DFA construction; Secondly, we propose to achieve the synthesis together with the on-the-fly DFA construction such that the synthesis result can be obtained before constructing the whole DFA. The on-the-fly DFA construction is implemented using the SAT-based techniques for automata generation. We compared our new approach with the traditional ones on extensive LTLf synthesis benchmarks. Experimental results showed that the pre-processing techniques have a significant advantage on the synthesis performance in terms of scalability, and the on-the-fly synthesis is able to complement extant approaches on both realizable and unrealizable cases.
An Enhanced Advising Model in Teacher-Student Framework using State Categorization
Authors: Anand Daksh , Gupta Vaibhav , Paruchuri Praveen , Ravindran Balaraman
Published: September 2022

The teacher-student framework aims to improve the sample efficiency of RL algorithms by deploying an advising mechanism in which a teacher helps a student by guiding its exploration. Prior work in this field has considered an advising mechanism where the teacher advises the student about the optimal action to take in a given state. However, real-world teachers can leverage domain expertise to provide more informative signals. Using this insight, we propose to extend the current advising framework wherein the teacher would provide not only the optimal action but also a qualitative assessment of the state. We introduce a novel architecture, namely Advice Replay Memory (ARM), to effectively reuse the advice provided by the teacher. We demonstrate the robustness of our approach by showcasing our experiments on multiple Atari 2600 games using a fixed set of hyper-parameters. Additionally, we show that a student taking help even from a sub-optimal teacher can achieve significant performance boosts and eventually outperform the teacher. Our approach outperforms the baselines even when provided with comparatively suboptimal teachers and an advising budget, which is smaller by orders of magnitude. The contributions of our paper are 4-fold (a) effectively leveraging a teacher's knowledge by richer advising (b) introduction of ARM to effectively reuse the advice throughout learning (c) ability to achieve significant performance boost even with a coarse state categorization (d) enabling the student to outperform the teacher.
Deep Radial-Basis Value Functions for Continuous Control
Authors: Asadi Kavosh , Parikh Neev , Parr Ronald E. , Konidaris George D. , Littman Michael L.
Published: September 2022

A core operation in reinforcement learning (RL) is finding an action that is optimal with respect to a learned value function. This operation is often challenging when the learned value function takes continuous actions as input. We introduce deep radial-basis value functions (RBVFs): value functions learned using a deep network with a radial-basis function (RBF) output layer. We show that the maximum action-value with respect to a deep RBVF can be approximated easily and accurately. Moreover, deep RBVFs can represent any true value function owing to their support for universal function approximation. We extend the standard DQN algorithm to continuous control by endowing the agent with a deep RBVF. We show that the resultant agent, called RBF-DQN, significantly outperforms value-function-only baselines, and is competitive with state-of-the-art actor-critic algorithms.
Relative Variational Intrinsic Control
Authors: Baumli Kate , Warde-Farley David , Hansen Steven , Mnih Volodymyr
Published: September 2022

In the absence of external rewards, agents can still learn useful behaviors by identifying and mastering a set of diverse skills within their environment. Existing skill learning methods use mutual information objectives to incentivize each skill to be diverse and distinguishable from the rest. However, if care is not taken to constrain the ways in which the skills are diverse, trivially diverse skill sets can arise. To ensure useful skill diversity, we propose a novel skill learning objective, Relative Variational Intrinsic Control (RVIC), which incentivizes learning skills that are distinguishable in how they change the agent's relationship to its environment. The resulting set of skills tiles the space of affordances available to the agent. We qualitatively analyze skill behaviors on multiple environments and show how RVIC skills are more useful than skills discovered by existing methods in hierarchical reinforcement learning.
Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks
Authors: Boo Yoonho , Shin Sungho , Choi Jungwook , Sung Wonyong
Published: September 2022

The quantization of deep neural networks (QDNNs) has been actively studied for deployment in edge devices. Recent studies employ the knowledge distillation (KD) method to improve the performance of quantized networks. In this study, we propose stochastic precision ensemble training for QDNNs (SPEQ). SPEQ is a knowledge distillation training scheme; however, the teacher is formed by sharing the model parameters of the student network. We obtain the soft labels of the teacher by randomly changing the bit precision of the activation stochastically at each layer of the forward-pass computation. The student model is trained with these soft labels to reduce the activation quantization noise. The cosine similarity loss is employed, instead of the KL-divergence, for KD training. As the teacher model changes continuously by random bit-precision assignment, it exploits the effect of stochastic ensemble KD. SPEQ outperforms the existing quantization training methods in various tasks, such as image classification, question-answering, and transfer learning without the need for cumbersome teacher networks.
Fast Training of Provably Robust Neural Networks by SingleProp
Authors: Boopathy Akhilan , Weng Lily , Liu Sijia , Chen Pin-Yu , Zhang Gaoyuan , Daniel Luca
Published: September 2022

Recent works have developed several methods of defending neural networks against adversarial attacks with certified guarantees. However, these techniques can be computationally costly due to the use of certification during training. We develop a new regularizer that is both more efficient than existing certified defenses, requiring only one additional forward propagation through a network, and can be used to train networks with similar certified accuracy. Through experiments on MNIST and CIFAR-10 we demonstrate improvements in training speed and comparable certified accuracy compared to state-of-the-art certified defenses.
Open-Set Recognition with Gaussian Mixture Variational Autoencoders
Authors: Cao Alexander , Luo Yuan , Klabjan Diego
Published: September 2022

In inference, open-set classification is to either classify a sample into a known class from training or reject it as an unknown class. Existing deep open-set classifiers train explicit closed-set classifiers, in some cases disjointly utilizing reconstruction, which we find dilutes the latent representation's ability to distinguish unknown classes. In contrast, we train our model to cooperatively learn reconstruction and perform class-based clustering in the latent space. With this, our Gaussian mixture variational autoencoder (GMVAE) achieves more accurate and robust open-set classification results, with an average F1 increase of 0.26, through extensive experiments aided by analytical results.
HyDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks
Authors: Chen Yuanyuan , Li Boyang , Yu Han , Wu Pengcheng , Miao Chunyan
Published: September 2022

The behaviors of deep neural networks (DNNs) are notoriously resistant to human interpretations. In this paper, we propose Hypergradient Data Relevance Analysis, or HyDRA, which interprets the predictions made by DNNs as effects of their training data. Existing approaches generally estimate data contributions around the final model parameters and ignore how the training data shape the optimization trajectory. By unrolling the hypergradient of test loss w.r.t. the weights of training data, HyDRA assesses the contribution of training data toward test data points throughout the training trajectory. In order to accelerate computation, we remove the Hessian from the calculation and prove that, under moderate conditions, the approximation error is bounded. Corroborating this theoretical claim, empirical results indicate the error is indeed small. In addition, we quantitatively demonstrate that HyDRA outperforms influence functions in accurately estimating data contribution and detecting noisy data labels. The source code is available at https://github.com/cyyever/aaai_hydra.
Self-Progressing Robust Training
Authors: Cheng Minhao , Chen Pin-Yu , Liu Sijia , Chang Shiyu , Hsieh Cho-Jui , Das Payel
Published: September 2022

Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy machine learning systems. Current robust training methods such as adversarial training explicitly uses an ``attack'' (e.g., l_infty-norm bounded perturbation) to generate adversarial examples during model training for improving adversarial robustness. In this paper, we take a different perspective and propose a new framework SPROUT, self-progressing robust training. During model training, SPROUT progressively adjusts training label distribution via our proposed parametrized label smoothing technique, making training free of attack generation and more scalable. We also motivate SPROUT using a general formulation based on vicinity risk minimization, which includes many robust training methods as special cases.
Compared with state-of-the-art adversarial training methods (PGD-l_infty and TRADES) under l_infty-norm bounded attacks and various invariance tests, SPROUT consistently attains superior performance and is more scalable to large neural networks. Our results shed new light on scalable, effective and attack-independent robust training methods.
Generalized Adversarially Learned Inference
Authors: Dandi Yatin , Bharadhwaj Homanga , Kumar Abhishek , Rai Piyush
Published: September 2022

Allowing effective inference of latent vectors while training GANs can greatly increase their applicability in various downstream tasks. Recent approaches, such as ALI and BiGAN frameworks, develop methods of inference of latent variables in GANs by adversarially training an image generator along with an encoder to match two joint distributions of image and latent vector pairs. We generalize these approaches to incorporate multiple layers of feedback on reconstructions, self-supervision, and other forms of supervision based on prior or learned knowledge about the desired solutions. We achieve this by modifying the discriminator's objective to correctly identify more than two joint distributions of tuples of an arbitrary number of random variables consisting of images, latent vectors, and other variables generated through auxiliary tasks, such as reconstruction and inpainting or as outputs of suitable pre-trained models. We design a non-saturating maximization objective for the generator-encoder pair and prove that the resulting adversarial game corresponds to a global optimum that simultaneously matches all the distributions. Within our proposed framework, we introduce a novel set of techniques for providing self-supervised feedback to the model based on properties, such as patch-level correspondence and cycle consistency of reconstructions. Through comprehensive experiments, we demonstrate the efficacy, scalability, and flexibility of the proposed approach for a variety of tasks. The appendix of the paper can be found at the following link: https://drive.google.com/file/d/1i99e682CqYWMEDXlnqkqrctGLVA9viiz/view?usp=sharing
Learning a Gradient-free Riemannian Optimizer on Tangent Spaces
Authors: Fan Xiaomeng , Gao Zhi , Wu Yuwei , Jia Yunde , Harandi Mehrtash
Published: September 2022

A principal way of addressing constrained optimization problems is to model them as problems on Riemannian manifolds. Recently, Riemannian meta-optimization provides a promising way for solving constrained optimization problems by learning optimizers on Riemannian manifolds in a data-driven fashion, making it possible to design task-specific constrained optimizers. A close look at the Riemannian meta-optimization reveals that learning optimizers on Riemannian manifolds needs to differentiate through the nonlinear Riemannian optimization, which is complex and computationally expensive. In this paper, we propose a simple yet efficient Riemannian meta-optimization method that learns to optimize on tangent spaces of manifolds. In doing so, we present a gradient-free optimizer on tangent spaces, which takes parameters of the model along with the training data as inputs, and generates the updated parameters directly. As a result, the constrained optimization is transformed from Riemannian manifolds to tangent spaces where complex Riemannian operations (e.g., retraction operations) are removed from the optimizer, and learning the optimizer does not need to differentiate through the Riemannian optimization. We empirically show that our method brings efficient learning of the optimizer, while enjoying a good optimization trajectory in a data-driven manner.
UAG: Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks
Authors: Feng Boyuan , Wang Yuke , Ding Yufei
Published: September 2022

With the increasing popularity of graph-based learning, graph neural networks (GNNs) emerge as the essential tool for gaining insights from graphs.
However, unlike the conventional CNNs that have been extensively explored and exhaustively tested, people are still worrying about the GNNs' robustness under the critical settings, such as financial services.
The main reason is that existing GNNs usually serve as a black-box in predicting and do not provide the uncertainty on the predictions.
On the other side, the recent advancement of Bayesian deep learning on CNNs has demonstrated its success of quantifying and explaining such uncertainties to fortify CNN models.
Motivated by these observations, we propose UAG, the first systematic solution to defend adversarial attacks on GNNs through identifying and exploiting hierarchical uncertainties in GNNs.
UAG develops a Bayesian uncertainty technique to explicitly capture uncertainties in GNNs and further employs an uncertainty-aware attention technique to defend adversarial attacks on GNNs. Intensive experiments show that our proposed defense approach outperforms the state-of-the-art solutions by a significant margin.
Learning to Augment for Data-scarce Domain BERT Knowledge Distillation
Authors: Feng Lingyun , Qiu Minghui , Li Yaliang , Zheng Hai-Tao , Shen Ying
Published: September 2022

Despite pre-trained language models such as BERT have achieved appealing performance in a wide range of Natural Language Processing (NLP) tasks, they are computationally expensive to be deployed in real-time applications. A typical method is to adopt knowledge distillation to compress these large pre-trained models (teacher models) to small student models. However, for a target domain with scarce training data, the teacher can hardly pass useful knowledge to the student, which yields performance degradation for the student models. To tackle this problem, we propose a method to learn to augment data for BERT Knowledge Distillation in target domains with scarce labeled data, by learning a cross-domain manipulation scheme that automatically augments the target domain with the help of resource-rich source domains. Specifically, the proposed method generates samples acquired from a stationary distribution near the target data and adopts a reinforced controller to automatically refine the augmentation strategy according to the performance of the student. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art baselines on different NLP tasks, and for the data-scarce domains, the compressed student models even perform better than the original large teacher model, with much fewer parameters (only ~13.3%) when only a few labeled examples available.
A Trace-restricted Kronecker-Factored Approximation to Natural Gradient
Authors: Gao Kaixin , Liu Xiaolei , Huang Zhenghai , Wang Min , Wang Zidong , Xu Dachuan , Yu Fan
Published: September 2022

Second-order optimization methods have the ability to accelerate convergence by modifying the gradient through the curvature matrix. There have been many attempts to use second-order optimization methods for training deep neural networks. In this work, inspired by diagonal approximations and factored approximations such as Kronecker-factored Approximate Curvature (KFAC), we propose a new approximation to the Fisher information matrix (FIM) called Trace-restricted Kronecker-factored Approximate Curvature (TKFAC), which can hold the certain trace relationship between the exact and the approximate FIM. In TKFAC, we decompose each block of the approximate FIM as a Kronecker product of two smaller matrices and scaled by a coefficient related to trace. We theoretically analyze TKFAC's approximation error and give an upper bound of it. We also propose a new damping technique for TKFAC on convolutional neural networks to maintain the superiority of second-order optimization methods during training. Experiments show that our method has better performance compared with several state-of-the-art algorithms on some deep network architectures.
Reinforcement Learning Based Multi-Agent Resilient Control: From Deep Neural Networks to an Adaptive Law
Authors: Hou Jian , Wang Fangyuan , Wang Lili , Chen Zhiyong
Published: September 2022

Recent advances in Multi-agent Reinforcement Learning (MARL) have made it possible to implement various tasks in cooperative as well as competitive scenarios through trial and error, and deep neural networks. These successes motivate us to bring the mechanism of MARL into the Multi-agent Resilient Consensus (MARC) problem that studies the consensus problem in a network of agents with faulty ones. Relying on the natural characteristics of the system goal, the key component in MARL, reward function, can thus be directly constructed via the relative distance among agents. Firstly, we apply Deep Deterministic Policy Gradient (DDPG) on each single agent to train and learn adjacent weights of neighboring agents in a distributed manner, that we call Distributed-DDPG (D-DDPG), so as to minimize the weights from suspicious agents and eliminate the corresponding influences. Secondly, to get rid of neural networks and their time-consuming training process, a Q-learning based algorithm, called Q-consensus, is further presented by building a proper reward function and a credibility function for each pair of neighboring agents so that the adjacent weights can update in an adaptive way. The experimental results indicate that both algorithms perform well with appearance of constant and/or random faulty agents, yet the Q-consensus algorithm outperforms the faulty ones running D-DDPG. Compared to the traditional resilient consensus strategies, e.g., Weighted-Mean-Subsequence-Reduced (W-MSR) or trustworthiness analysis, the proposed Q-consensus algorithm has greatly relaxed the topology requirements, as well as reduced the storage and computation loads. Finally, a smart-car hardware platform consisting of six vehicles is used to verify the effectiveness of the Q-consensus algorithm by achieving resilient velocity synchronization.
Dynamic Multi-Context Attention Networks for Citation Forecasting of Scientific Publications
Authors: Ji Taoran , Self Nathan , Fu Kaiqun , Chen Zhiqian , Ramakrishnan Naren , Lu Chang-Tien
Published: September 2022

Forecasting citations of scientific patents and publications is a crucial task for understanding the evolution and development of technological domains and for foresight into emerging technologies. By construing citations as a time series, the task can be cast into the domain of temporal point processes. Most existing work on forecasting with temporal point processes, both conventional and neural network-based, only performs single-step forecasting. In citation forecasting, however, the more salient goal is n-step forecasting: predicting the arrival time and the technology class of the next n citations. In this paper, we propose Dynamic Multi-Context Attention Networks (DMA-Nets), a novel deep learning sequence-to-sequence (Seq2Seq) model with a novel hierarchical dynamic attention mechanism for long-term citation forecasting. Extensive experiments on two real-world datasets demonstrate that the proposed model learns better representations of conditional dependencies over historical sequences compared to state-of-the-art counterparts and thus achieves significant performance for citation predictions. The dataset and code have been made available online.
GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning
Authors: Killamsetty Krishnateja , Sivasubramanian Durga , Ramakrishnan Ganesh , Iyer Rishabh
Published: September 2022

Large scale machine learning and deep models are extremely data-hungry. Unfortunately, obtaining large amounts of labeled data is expensive, and training state-of-the-art models (with hyperparameter tuning) requires significant computing resources and time. Secondly, real-world data is noisy and imbalanced. As a result, several recent papers try to make the training process more efficient and robust. However, most existing work either focuses on robustness or efficiency, but not both. In this work, we introduce GLISTER, a GeneraLIzation based data Subset selecTion for Efficient and Robust learning framework. We formulate GLISTER as a mixed discrete-continuous bi-level optimization problem to select a subset of the training data, which maximizes the log-likelihood on a held-out validation set. We then analyze GLISTER for simple classifiers such as gaussian and multinomial naive-bayes, k-nearest neighbor classifier, and linear regression and show connections to submodularity. Next, we propose an iterative online algorithm GLISTER-ONLINE, which performs data selection iteratively along with the parameter updates, and can be applied to any loss-based learning algorithm. We then show that for a rich class of loss functions including cross-entropy, hinge-loss, squared-loss, and logistic-loss, the inner discrete data selection is an instance of (weakly) submodular optimization, and we analyze conditions for which GLISTER-ONLINE reduces the validation loss and converges. Finally, we propose GLISTER-ACTIVE, an extension to batch active learning, and we empirically demonstrate the performance of GLISTER on a wide range of tasks including, (a) data selection to reduce training time, (b) robust learning under label noise and imbalance settings, and (c) batch-active learning with a number of deep and shallow models. We show that our framework improves upon the state of the art both in efficiency and accuracy (in cases (a) and (c)) and is more efficient compared to other state-of-the-art robust learning algorithms in case (b). The code for GLISTER is at: https://github.com/dssresearch/GLISTER.
Sparsity Aware Normalization for GANs
Authors: Kligvasser Idan , Michaeli Tomer
Published: September 2022

Generative adversarial networks (GANs) are known to benefit from regularization or normalization of their critic (discriminator) network during training. In this paper, we analyze the popular spectral normalization scheme, find a significant drawback and introduce sparsity aware normalization (SAN), a new alternative approach for stabilizing GAN training. As opposed to other normalization methods, our approach explicitly accounts for the sparse nature of the feature maps in convolutional networks with ReLU activations. We illustrate the effectiveness of our method through extensive experiments with a variety of network architectures. As we show, sparsity is particularly dominant in critics used for image-to-image translation settings. In these cases our approach improves upon existing methods, in less training epochs and with smaller capacity networks, while requiring practically no computational overhead.
Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees
Authors: Kungurtsev Vyacheslav , Egan Malcolm , Chatterjee Bapi , Alistarh Dan
Published: September 2022

Asynchronous distributed algorithms are a popular way to reduce synchronization costs in large-scale optimization, and in particular for neural network training. However, for nonsmooth and nonconvex objectives, few convergence guarantees exist beyond cases where closed-form proximal operator solutions are available.
As training most popular deep neural networks corresponds to optimizing nonsmooth and nonconvex objectives, there is a pressing need for such convergence guarantees. In this paper, we analyze for the first time the convergence of stochastic asynchronous optimization for this general class of objectives. In particular, we focus on stochastic subgradient methods allowing for block variable partitioning, where the shared model is asynchronously updated by concurrent processes. To this end, we use a probabilistic model which captures key features of real asynchronous scheduling between concurrent processes. Under this model, we establish convergence with probability one to an invariant set for stochastic subgradient methods with momentum.
From a practical perspective, one issue with the family of algorithms that we consider is that they are not efficiently supported by machine learning frameworks, which mostly focus on distributed data-parallel strategies.
To address this, we propose a new implementation strategy for shared-memory based training of deep neural networks for a partitioned but shared model in single- and multi-GPU settings.
Based on this implementation, we achieve on average1.2x speed-up in comparison to state-of-the-art training methods for popular image classification tasks, without compromising accuracy.
Metrics and Continuity in Reinforcement Learning
Authors: Le Lan Charline , G. Bellemare Marc , Castro Pablo Samuel
Published: September 2022

In most practical applications of reinforcement learning, it is untenable to maintain direct estimates for individual states; in continuous-state systems, it is impossible. Instead, researchers often leverage {\em state similarity} (whether explicitly or implicitly) to build models that can generalize well from a limited set of samples. The notion of state similarity used, and the neighbourhoods and topologies they induce, is thus of crucial importance, as it will directly affect the performance of the algorithms. Indeed, a number of recent works introduce algorithms assuming the existence of "well-behaved" neighbourhoods, but leave the full specification of such topologies for future work. In this paper we introduce a unified formalism for defining these topologies through the lens of metrics. We establish a hierarchy amongst these metrics and demonstrate their theoretical implications on the Markov Decision Process specifying the reinforcement learning problem. We complement our theoretical results with empirical evaluations showcasing the differences between the metrics considered.
Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem
Authors: Li Bingcong , Wang Lingda , Giannakis Georgios B. , Zhao Zhizhen
Published: September 2022

Aiming at convex optimization under structural constraints, this work introduces and analyzes a variant of the Frank Wolfe (FW) algorithm termed ExtraFW. The distinct feature of ExtraFW is the pair of gradients leveraged per iteration, thanks to which the decision variable is updated in a prediction-correction (PC) format. Relying on no problem dependent parameters in the step sizes, the convergence rate of ExtraFW for general convex problems is shown to be ${\cal O}(\frac{1}{k})$, which is optimal in the sense of matching the lower bound on the number of solved FW subproblems. However, the merit of ExtraFW is its faster rate ${\cal O}\big(\frac{1}{k^2} \big)$ on a class of machine learning problems. Compared with other parameter-free FW variants that have faster rates on the same problems, ExtraFW has improved rates and fine-grained analysis thanks to its PC update. Numerical tests on binary classification with different sparsity-promoting constraints demonstrate that the empirical performance of ExtraFW is significantly better than FW, and even faster than Nesterov's accelerated gradient on certain datasets. For matrix completion, ExtraFW enjoys smaller optimality gap, and lower rank than FW.
Unsupervised Active Learning via Subspace Learning
Authors: Li Changsheng , Mao Kaihang , Liang Lingyan , Ren Dongchun , Zhang Wei , Yuan Ye , Wang Guoren
Published: September 2022

Unsupervised active learning has been an active research topic in machine learning community, with the purpose of choosing representative samples to be labelled in an unsupervised manner. Previous works usually take the minimization of data reconstruction loss as the criterion to select representative samples which can better approximate original inputs. However, data are often drawn from low-dimensional subspaces embedded in an arbitrary high-dimensional space in many scenarios, thus it might severely bring in noise if attempting to precisely reconstruct all entries of one observation, leading to a suboptimal solution. In view of this, this paper proposes a novel unsupervised Active Learning model via Subspace Learning, called ALSL. In contrast to previous approaches, ALSL aims to discovery the low-rank structures of data, and then perform sample selection based on learnt low-rank representations. To this end, we devise two different strategies and propose two corresponding formulations to perform unsupervised active learning with and under low-rank sample representations respectively. Since the proposed formulations involve several non-smooth regularization terms, we develop a simple but effective optimization procedure to solve them. Extensive experiments are performed on five publicly available datasets, and experimental results demonstrate the proposed first formulation achieves comparable performance with the state-of-the-arts, while the second formulation significantly outperforms them, achieving a 13\% improvement over the second best baseline at most.
VSQL: Variational Shadow Quantum Learning for Classification
Authors: Li Guangxi , Song Zhixin , Wang Xin
Published: September 2022

Classification of quantum data is essential for quantum machine learning and near-term quantum technologies. In this paper, we propose a new hybrid quantum-classical framework for supervised quantum learning, which we call Variational Shadow Quantum Learning (VSQL). Our method in particular utilizes the classical shadows of quantum data, which fundamentally represent the side information of quantum data with respect to certain physical observables. Specifically, we first use variational shadow quantum circuits to extract classical features in a convolution way and then utilize a fully-connected neural network to complete the classification task. We show that this method could sharply reduce the number of parameters and thus better facilitate quantum circuit training. Simultaneously, less noise will be introduced since fewer quantum gates are employed in such shadow circuits. Moreover, we show that the Barren Plateau issue, a significant gradient vanishing problem in quantum machine learning, could be avoided in VSQL. Finally, we demonstrate the efficiency of VSQL in quantum classification via numerical experiments on the classification of quantum states and the recognition of multi-labeled handwritten digits. In particular, our VSQL approach outperforms existing variational quantum classifiers in the test accuracy in the binary case of handwritten digit recognition and notably requires much fewer parameters.
ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification
Authors: Li Guozhong , Choi Byron , Xu Jianliang , S Bhowmick Sourav , Chun Kwok-Pan , Wong Grace Lai-Hung
Published: September 2022

Time series shapelets are short discriminative subsequences that recently have been found not only to be accurate but also interpretable for the classification problem of univariate time series (UTS). However, existing work on shapelets selection cannot be applied to multivariate time series classification (MTSC) since the candidate shapelets of MTSC may come from different variables of different lengths and thus cannot be directly compared. To address this challenge, in this paper, we propose a novel model called ShapeNet, which embeds shapelet candidates of different lengths into a unified space for shapelet selection. The network is trained using cluster-wise triplet loss, which considers the distance between anchor and multiple positive (negative) samples and the distance between positive (negative) samples, which are important for convergence. We compute representative and diversified final shapelets rather than directly using all the embeddings for model building to avoid a large fraction of non-discriminative shapelet candidates. We have conducted experiments on ShapeNet with competitive state-of-the-art and benchmark methods using UEA MTS datasets. The results show that the accuracy of ShapeNet is the best of all the methods compared. Furthermore, we illustrate the shapelets’ interpretability with two case studies.
SILURIAN INTERMEDIATE DIAMOND COLLECTOR (SOUTH CIS-TIMAN, PERM REGION)
Authors:   Perm State University, Paktovsky Yu. G.
Published: September 2022

The article presents the results of granulometric, mineralogical and lithochemical analyses of rock samples taken from different horizons of terrigenous deposits at the base of Silurian. Their features and indicator signs, which are important in the field diagnostics of rocks, have been established. The geological description of the deposits of the Silurian reservoir is given. Three strata are distinguished in the reservoir formation by lithological features. The productivity of each of the strata is compared using results of large-scale testing. Lithological studies shown that the continental facies of temporary and small watercourses are distinguished by large-block material scattered over the surface of the rock stratification, controlling the trickle and nest distribution of minerals. An important consequence is that rare pebble material scattered in the rock mass can serve as an indicator of productive placers. By analogy, the appearance of ventifacts in the detrital weathering crust can serve as an indication of aeolian diamond placers. Thus, an operational combination of structural-geological and lithological studies makes it possible to compare similar deposits in different geological settings and increase the efficiency of prospecting and exploration.
REFINEMENT OF THE BOUNDARIES OF THE SULFATE KARST AREAS OF THE PERM REGION
Authors:   Perm State University, Maksimovich N. G. , Kadebskaya O. I. ,   Perm State University, Meshcheriakova O. Yu. ,   Perm State University
Published: September 2022

The areas referred to the areas of sulfate karst are related to the areas of exclusive distribution of gypsum and anhydrite karst, as well as joint occurrence with carbonate rocks and salts. Based on the field study data, generalization of archive materials and publications, as well as the results of monitoring of exogenous geological processes carried out by the authors of the article, the data on sulfate karst of the Perm Territory not updated since the 90s of the last century were analyzed. This made it possible to clarify the boundaries of the previously identified areas of sulfate karst in the Perm region
SOLUTION OF THE INVERSE PROBLEM OF GRAVITY EXPLORATION FOR 2D PRISMATIC BODIES BY THE STATISTICAL TESTS METHOD
Authors:   MI UB RAS, Dolgal A. S. ,   Perm State University, Petrosyan R. N. ,   Perm State University,   Antikarst and coastal protection LLC
Published: September 2022

An algorithm for solving a nonlinear inverse problem of gravity exploration for a monogenic anomaly caused by a 2D prism, based on the method of statistical tests (Monte Carlo), is presented. It uses the generation of random multidimensional vectors of rectangular coordinates of the corner points of the model. The difference between the observed and model fields is estimated in the Euclidean and Chebyshev metrics. The algorithm was developed for the purpose of teaching students and implemented in the PODBOR_ST program, which is used in the process of laboratory work. Model and practical examples of the interpretation of gravity anomalies are given. In the latter case, a guaranteed approach is implemented.
LOCALIZATION PECULIARITIES OF GOLD PLACERS IN THE BURIED FILLED KARST OF INTERMOUNTAIN DEPRESSIONS OF THE SOUTHERN URALS
Authors:   Institute of Geology, Ufa Federal Research Center of RAS, Kazakov P. V.
Published: September 2022

Large megablocks of the South Urals are separated by longitudinal suture zones presented by long-lived negative morphostructures, which are inherited by intermountain erosional-karst depressions: Belskaya , Mindyakskaya, and Miasskaya. In intermountain depressions, the main interest for placer gold prospecting is related to the areas of paleovalleys of the Belaya, Mindyak, and Shartymka rivers especially to the karst placer gold traps associated with zones of increased fracturing and permeability at the junctions of submeridian and intersecting neotectonically active transverse faults. In the ore placer clusters of the Mindyakskaya and Miasskaya (southern part) depressions, manifestations of superimposed low-temperature gold-argillisite-jasperoid mineralization associated with one of the stages of the Mesozoic tectonic magmatic activation of the region have been established
MINERALOGICAL AND BIOLOGICAL WITNESSES OF REDIKAR BURIAL OF THE 10TH–11TH CENTURIES
Authors:   Mining Institute of the Ural Branch of the Russian Academy of Sciences, Сhaikovskiy I. I. , Fadeeva T. V. ,   Mining Institute of the Ural Branch of the Russian Academy of Sciences, Chirkova E. P. ,   Mining Institute of the Ural Branch of the Russian Academy of Sciences
Published: September 2022

The study of products of copper alloys of the Lomatov culture of the X-XI centuries from the collection of the Cherdyn Museum revealed two types of patina. The first pseudomorphic might be composed of intergrowths of chlorides formed upon interaction with the sweat of the hands, or a zonal rim of cuprite-malachite-azurite composition, due to interaction with groundwater. The second type, which distorts the shape of the original product, is associated with the leaching of copper and other metals and the crystallization of oxides and salts on the surface in the form of worm-like, kidney-shaped and drusoid aggregates. Fragments of mineralized biological objects (wings, eggs and larvae of flies, mole hair) found on the surface of the patina testify to the rapid oxidation of bronze, which is associated with flooding of the soil of the burial ground immediately after burial.
STAGES OF THE ENGINEERING-GEOLOGICAL PROPERTIES OF ROCK OF THE RED-COLORED TERRIGENOUS FORMATION OF THE KAMA REGION
Authors:   Perm State University, Gainanov Sh. Kh.
Published: September 2022

The paper analyzes the stages of formation of engineering-geological properties of rocks of the red-colored terrigenous formation. Two main stages are distinguished, which determined the features of their engineering-geological characteristics. The first stage – the stage of progressive lithogenesis was characterized by facies sedimentation environments and determined the main genetic types of rocks of the formation. The second stage – the stage of epigenesis (hypergenesis) was defining for the formation of the engineering and geological properties existing at the present time that must be taken into account when solving problems in the course of design and construction work. The role of local tectonic structures and tectonic fracturing within them is emphasized in the activation of hypergenesis processes and changes in rock properties.
RESULTS OF THE RESEARCH ON DEVELOPMENT OF THE HARDWARE-SOFTWARE COMPLEX FOR SURFACE-TO-MINE ELECTROMAGNETIC SOUNDING
Authors:   Perm State University, Kolesnikov V. P. , Karpov S. B. ,   Perm State University, Laskina T. A. ,   Uralgeopole
Published: September 2022

Results of the research on development of the hardware-software complex for surface-to-mine electromagnetic sounding is considered in the paper. It taking into account its effectiveness, informativeness and peculiarities of its practical implementation. On the base of experimental studies and numerical analysis, the hardware and software modules providing synchronization of the transmitter and receiver, visual inspection of the measured signal including its frequency structure and amplitudes during the field observations with specified parameters was developed. Results of the experimental studies using the hardware-software complex showed technological effectiveness and prospects of its practical implementation to control physical state of the water protective strata over the salt mines.