Reviewerly/exHarmony huggingface dataset:
Improving Knowledge Distillation via Category Structure

Most previous knowledge distillation frameworks train the student to mimic the teacher’s output of each sample or transfer cross-sample relations from the teacher to the student. Nevertheless, they neglect the structured relations at a category level. In this paper, a novel Category Structure is proposed to transfer category-level structured relations for knowledge distillation. It models two structured relations, including intra-category structure and inter-category structure, which are intrinsic natures in relations between samples. Intra-category structure penalizes the structured relations in samples from the same category and inter-category structure focuses on cross-category relations at a category level. Transferring category structure from the teacher to the student supplements category-level structured relations for training a better student. Extensive experiments show that our method groups samples from the same category tighter in the embedding space and the superiority of our method in comparison with closely related works are validated in different datasets and models.
A Discrete Variational Recurrent Topic Model without the Reparametrization Trick

We show how to learn a neural topic model with discrete random variables---one that explicitly models each word's assigned topic---using neural variational inference that does not rely on stochastic backpropagation to handle the discrete variables. The model we utilize combines the expressive power of neural methods for representing sequences of text with the topic model's ability to capture global, thematic coherence. Using neural variational inference, we show improved perplexity and document understanding across multiple corpora. We examine the effect of prior parameters both on the model and variational parameters and demonstrate how our approach can compete and surpass a popular topic model implementation on an automatic measure of topic quality.
Ant Lion Optimization Algorithm

Ant lion optimization (ALO) is an innovative optimization technique inspired by nature. The ALO algorithm describes the dangerous hunting technique of ant lion by digging holes in a cone-like shape while AL goes down to the bottom of the hole and places its jaw at the end to ensure proper feasting on prey falling into the trap. Model equation of the ant lion algorithm and solved examples are used to illustrate the approach using MATLAB 2020 version. The ALO algorithm was applied to model heat recovery steam generator and the algorithm showed the possibility of obtaining an outlet steam temperature of 539.413 K (266.413 °C) at Entropy of 58.738 kJ/kg K when the flue gas temperature from the Gas Turbine into the WRPC’s Heat Recovery Steam Generator (HRSG) is 723.15 K (450.15 °C). The temperature of Feed Water is 393 K (120 °C).
Model Agnostic Multilevel Explanations

In recent years, post-hoc local instance-level and global dataset-level explainability of black-box models has received a lot of attention. Much less attention has been given to obtaining insights at intermediate or group levels, which is a need outlined in recent works that study the challenges in realizing the guidelines in the General Data Protection Regulation (GDPR). In this paper, we propose a meta-method that, given a typical local explainability method, can build a multilevel explanation tree. The leaves of this tree correspond to the local explanations, the root corresponds to the global explanation, and intermediate levels correspond to explanations for groups of data points that it automatically clusters. The method can also leverage side information, where users can specify points for which they may want the explanations to be similar. We argue that such a multilevel structure can also be an effective form of communication, where one could obtain few explanations that characterize the entire dataset by considering an appropriate level in our explanation tree. Explanations for novel test points can be cost-efficiently obtained by associating them with the closest training points. When the local explainability technique is generalized additive (viz. LIME, GAMs), we develop a fast approximate algorithm for building the multilevel tree and study its convergence behavior. We validate the effectiveness of the proposed technique based on two human studies -- one with experts and the other with non-expert users -- on real world datasets, and show that we produce high fidelity sparse explanations on several other public datasets.
Woodbury Transformations for Deep Generative Flows

Normalizing flows are deep generative models that allow efficient likelihood calculation and sampling. The core requirement for this advantage is that they are constructed using functions that can be efficiently inverted and for which the determinant of the function's Jacobian can be efficiently computed. Researchers have introduced various such flow operations, but few of these allow rich interactions among variables without incurring significant computational costs. In this paper, we introduce Woodbury transformations, which achieve efficient invertibility via the Woodbury matrix identity and efficient determinant calculation via Sylvester's determinant identity. In contrast with other operations used in state-of-the-art normalizing flows, Woodbury transformations enable (1) high-dimensional interactions, (2) efficient sampling, and (3) efficient likelihood evaluation. Other similar operations, such as 1x1 convolutions, emerging convolutions, or periodic convolutions allow at most two of these three advantages. In our experiments on multiple image datasets, we find that Woodbury transformations allow learning of higher-likelihood models than other flow architectures while still enjoying their efficiency advantages.
Task-agnostic Exploration in Reinforcement Learning

Efficient exploration is one of the main challenges in reinforcement learning (RL). Most existing sample-efficient algorithms assume the existence of a single reward function during exploration. In many practical scenarios, however, there is not a single underlying reward function to guide the exploration, for instance, when an agent needs to learn many skills simultaneously, or multiple conflicting objectives need to be balanced. To address these challenges, we propose the \textit{task-agnostic RL} framework: In the exploration phase, the agent first collects trajectories by exploring the MDP without the guidance of a reward function. After exploration, it aims at finding near-optimal policies for $N$ tasks, given the collected trajectories augmented with \textit{sampled rewards} for each task. We present an efficient task-agnostic RL algorithm, \textsc{UCBZero}, that finds $\epsilon$-optimal policies for $N$ arbitrary tasks after at most $\tilde O(\log(N)H^5SA/\epsilon^2)$ exploration episodes. We also provide an $\Omega(\log (N)H^2SA/\epsilon^2)$ lower bound, showing that the $\log$ dependency on $N$ is unavoidable. Furthermore, we provide an $N$-independent sample complexity bound of \textsc{UCBZero} in the statistically easier setting when the ground truth reward functions are known.
Benign Overfitting in Binary Classification of Gaussian Mixtures

Deep neural networks generalize well despite being exceedingly overparametrized, but understanding the statistical principles behind this so called benign-overfitting phenomenon is not yet well understood. Recently there has been remarkable progress towards understanding benign-overfitting in simpler models, such as linear regression and, even more recently, linear classification. This paper studies benign-overfitting for data generated from a popular binary Gaussian mixtures model (GMM) and classifiers trained by support-vector machines (SVM). Our approach has two steps. First, we leverage an idea introduced in (Muthukumar et al. 2020) to relate the SVM solution to the least-squares (LS) solution. Second, we derive novel non-asymptotic bounds on the classification error of LS solution. Combining the two gives sufficient conditions on the overparameterization ratio and the signal-to-noise ratio that lead to benign overfitting. We corroborate our theoretical findings with numerical simulations.
Natural Graph Networks

A key requirement for graph neural networks is that they must process a graph in a way that does not depend on how the graph is described. Traditionally this has been taken to mean that a graph network must be equivariant to node permutations. Here we show that instead of equivariance, the more general concept of naturality is sufficient for a graph network to be well-defined, opening up a larger class of graph networks. We define global and local natural graph networks, the latter of which are as scalable as conventional message passing graph neural networks while being more flexible. We give one practical instantiation of a natural network on graphs which uses an equivariant message network parameterization, yielding good performance on several benchmarks.
Multi-Valued Neural Networks and their Use in Decision Making on the Management of a Group of Unmanned Vehicles

To make a management decision in the UAV group, a multi-valued associative memory is used. Such a memory is a new concept, generalizing the similar in fuzzy neural networks. In a multi-valued neural network, inputs are elements or subsets of the lattice of linguistic variables. The lattice is assumed to be distributive and defines the implication used to determine the output of a neural network.
Customers’ awareness and knowledge level of fraudulent acts in electronic banking in Ghana: evidence from a universal bank

Purpose This study aims to assess customers’ awareness and level of knowledge on electronic banking fraud. Design/methodology/approach A well-structured interviewer-assisted questionnaire was used to collect data from 400 clients of a case study bank. Data were analysed using descriptive statistics. Kendall’s coefficient of concordance (W) statistic was also estimated to track and rank the fraudulent activities identified by the respondents with respect to electronic banking. Findings This study found that respondents were aware of most of the specific forms of electronic banking fraud. Firstly, automated teller machinfraud is the most common scam for which customers are aware of. Secondly, institutional factors such as lack of monitoring and education of clients are major factors which expose the bank and clients to fraudulent electronic banking acts. Thirdly, the most effective action that can be taken to prevent fraud in the bank is increased security and personal identification number (PIN) protection education. Research limitations/implications This study focusses on a universal bank and uses data from customers of only one branch of the bank to achieve the research objectives. Originality/value One uniqueness of this paper is in the adoption of Kendall’s coefficient of concordance (W) statistic to track and rank fraudulent banking activities. The findings will allow financial institutions to know the forms of current and innovative electronic banking fraudulent activities that customers are aware of. It will also enable the banks to find ways to inform their clients about emerging electronic banking fraudulent activities to prevent them from falling victims.
A URI parsing technique and algorithm for anti-pattern detection in RESTful Web services

Purpose Many REpresentational State Transfer (RESTful) Web services suffered from anti-patterns problem, which may diminish the sustainability of the services. The anti-patterns problem could happen in the code of the programme or the uniform resource identifiers (URIs) of RESTful Web services. This study aims to address the problem by proposing a technique and an algorithm for detecting anti-patterns in RESTful Web services. Specifically, the technique is designed based on URIs parsing process. Design/methodology/approach The study was conducted following the design science research process, which has six activities, namely, identifying problems, identifying solutions, design the solutions, demonstrate the solution, evaluation and communicate the solution. The proposed technique was embedded in an algorithm and evaluated in four phases covering the process of extracting the URIs, implementing the anti-pattern detection algorithm, detecting the anti-patterns and validating the results. Findings The results of the study suggested an acceptable level of accuracy for the anti-patterns detection with 82.30% of precision, 87.86% of recall and 84.93% of F-measure. Practical implications The technique and the algorithm can be used by developers of RESTful Web services to detect possible anti-pattern occurrences in the service-based systems. Originality/value The technique is personalised to detect amorphous URI and ambiguous name anti-patterns in which it scans the Web service URIs using specified rules and compares them with pre-determined syntax and corpus.
A Decentralized Parallel Algorithm for Training Generative Adversarial Nets

Generative Adversarial Networks (GANs) are a powerful class of generative models in the deep learning community. Current practice on large-scale GAN training utilizes large models and distributed large-batch training strategies, and is implemented on deep learning frameworks (e.g., TensorFlow, PyTorch, etc.) designed in a centralized manner. In the centralized network topology, every worker needs to either directly communicate with the central node or indirectly communicate with all other workers in every iteration. However, when the network bandwidth is low or network latency is high, the performance would be significantly degraded. Despite recent progress on decentralized algorithms for training deep neural networks, it remains unclear whether it is possible to train GANs in a decentralized manner. The main difficulty lies at handling the nonconvex-nonconcave min-max optimization and the decentralized communication simultaneously. In this paper, we address this difficulty by designing the \textbf{first gradient-based decentralized parallel algorithm} which allows workers to have multiple rounds of communications in one iteration and to update the discriminator and generator simultaneously, and this design makes it amenable for the convergence analysis of the proposed decentralized algorithm. Theoretically, our proposed decentralized algorithm is able to solve a class of non-convex non-concave min-max problems with provable non-asymptotic convergence to first-order stationary point. Experimental results on GANs demonstrate the effectiveness of the proposed algorithm.
Elas4RDF: Multi-perspective Triple-Centered Keyword Search over RDF Using Elasticsearch

The task of accessing knowledge graphs through structured query languages like SPARQL is rather demanding for ordinary users. Consequently, there are various approaches that attempt to exploit the simpler and widely used keyword-based search paradigm, either by translating keyword queries to structured queries, or by adopting classical information retrieval (IR) techniques. This paper demonstrates Elas4RDF, a keyword search system over RDF that is based on Elasticsearch, an out-of-the-box document-centric IR system. Elas4RDF indexes and retrieves triples (instead of entities), and thus yields more refined and informative results, that can be viewed through different perspectives. In this paper we demonstrate the performance of the Elas4RDF system in queries of various types, and showcase the benefits from offering different perspectives for aggregating and visualising the search results.
Learning Parities with Neural Networks

In recent years we see a rapidly growing line of research which shows learnability of various models via common neural network algorithms. Yet, besides a very few outliers, these results show learnability of models that can be learned using linear methods. Namely, such results show that learning neural-networks with gradient-descent is competitive with learning a linear classifier on top of a data-independent representation of the examples. This leaves much to be desired, as neural networks are far more successful than linear methods. Furthermore, on the more conceptual level, linear models don't seem to capture the deepness of deep networks. In this paper we make a step towards showing leanability of models that are inherently non-linear. We show that under certain distributions, sparse parities are learnable via gradient decent on depth-two network. On the other hand, under the same distributions, these parities cannot be learned efficiently by linear methods.
List-Decodable Mean Estimation via Iterative Multi-Filtering

We study the problem of {\em list-decodable mean estimation} for bounded covariance distributions. Specifically, we are given a set $T$ of points in $\mathbb{R}^d$ with the promise that an unknown $\alpha$-fraction of points in $T$, where $0< \alpha < 1/2$, are drawn from an unknown mean and bounded covariance distribution $D$, and no assumptions are made on the remaining points. The goal is to output a small list of hypothesis vectors such that at least one of them is close to the mean of $D$. We give the first practically viable estimator for this problem. In more detail, our algorithm is sample and computationally efficient, and achieves information-theoretically near-optimal error. While the only prior algorithm for this setting inherently relied on the ellipsoid method, our algorithm is iterative and only uses spectral techniques. Our main technical innovation is the design of a soft outlier removal procedure for high-dimensional heavy-tailed datasets with a majority of outliers.
Ultrahyperbolic Representation Learning

In machine learning, data is usually represented in a (flat) Euclidean space where distances between points are along straight lines. Researchers have recently considered more exotic (non-Euclidean) Riemannian manifolds such as hyperbolic space which is well suited for tree-like data. In this paper, we propose a representation living on a pseudo-Riemannian manifold of constant nonzero curvature. It is a generalization of hyperbolic and spherical geometries where the nondegenerate metric tensor need not be positive definite. We provide the necessary learning tools in this geometry and extend gradient-based optimization techniques. More specifically, we provide closed-form expressions for distances via geodesics and define a descent direction to minimize some objective function. Our novel framework is applied to graph representations.
Sketch-n-Sketch: Output-Directed Programming for SVG

For creative tasks, programmers face a choice: Use a GUI and sacrifice flexibility, or write code and sacrifice ergonomics? To obtain both flexibility and ease of use, a number of systems have explored a workflow that we call output-directed programming. In this paradigm, direct manipulation of the program's graphical output corresponds to writing code in a general-purpose programming language, and edits not possible with the mouse can still be enacted through ordinary text edits to the program. Such capabilities provide hope for integrating graphical user interfaces into what are currently text-centric programming environments. To further advance this vision, we present a variety of new output-directed techniques that extend the expressive power of Sketch-n-Sketch, an output-directed programming system for creating programs that generate vector graphics. To enable output-directed interaction at more stages of program construction, we expose intermediate execution products for manipulation and we present a mechanism for contextual drawing. Looking forward to output-directed programming beyond vector graphics, we also offer generic refactorings through the GUI, and our techniques employ a domain-agnostic provenance tracing scheme. To demonstrate the improved expressiveness, we implement a dozen new parametric designs in Sketch-n-Sketch without text-based edits. Among these is the first demonstration of building a recursive function in an output-directed programming setting.
Learning Linear Programs from Optimal Decisions

We propose a flexible gradient-based framework for learning linear programs from optimal decisions. Linear programs are often specified by hand, using prior knowledge of relevant costs and constraints. In some applications, linear programs must instead be learned from observations of optimal decisions. Learning from optimal decisions is a particularly challenging bi-level problem, and much of the related inverse optimization literature is dedicated to special cases. We tackle the general problem, learning all parameters jointly while allowing flexible parametrizations of costs, constraints, and loss functions. We also address challenges specific to learning linear programs, such as empty feasible regions and non-unique optimal decisions. Experiments show that our method successfully learns synthetic linear programs and minimum-cost multi-commodity flow instances for which previous methods are not directly applicable. We also provide a fast batch-mode PyTorch implementation of the homogeneous interior point algorithm, which supports gradients by implicit differentiation or backpropagation.
A simple normative network approximates local non-Hebbian learning in the cortex

To guide behavior, the brain extracts relevant features from high-dimensional data streamed by sensory organs. Neuroscience experiments demonstrate that the processing of sensory inputs by cortical neurons is modulated by instructive signals which provide context and task-relevant information. Here, adopting a normative approach, we model these instructive signals as supervisory inputs guiding the projection of the feedforward data. Mathematically, we start with a family of Reduced-Rank Regression (RRR) objective functions which include Reduced Rank (minimum) Mean Square Error (RRMSE) and Canonical Correlation Analysis (CCA), and derive novel offline and online optimization algorithms, which we call Bio-RRR. The online algorithms can be implemented by neural networks whose synaptic learning rules resemble calcium plateau potential dependent plasticity observed in the cortex. We detail how, in our model, the calcium plateau potential can be interpreted as a backpropagating error signal. We demonstrate that, despite relying exclusively on biologically plausible local learning rules, our algorithms perform competitively with existing implementations of RRMSE and CCA.
The Statistical Complexity of Early-Stopped Mirror Descent

Recently there has been a surge of interest in understanding implicit regularization properties of iterative gradient-based optimization algorithms. In this paper, we study the statistical guarantees on the excess risk achieved by early-stopped unconstrained mirror descent algorithms applied to the unregularized empirical risk with the squared loss for linear models and kernel methods. By completing an inequality that characterizes convexity for the squared loss, we identify an intrinsic link between offset Rademacher complexities and potential-based convergence analysis of mirror descent methods. Our observation immediately yields excess risk guarantees for the path traced by the iterates of mirror descent in terms of offset complexities of certain function classes depending only on the choice of the mirror map, initialization point, step-size, and the number of iterations. We apply our theory to recover, in a clean and elegant manner via rather short proofs, some of the recent results in the implicit regularization literature, while also showing how to improve upon them in some settings.