pwc-archive/papers-with-abstracts huggingface dataset:
Mixing Time Guarantees for Unadjusted Hamiltonian Monte Carlo
Authors: Nawaf Bou-Rabee, Andreas Eberle
Date: 2021-05-03 00:00:00

We provide quantitative upper bounds on the total variation mixing time of the Markov chain corresponding to the unadjusted Hamiltonian Monte Carlo (uHMC) algorithm. For two general classes of models and fixed time discretization step size $h$, the mixing time is shown to depend only logarithmically on the dimension. Moreover, we provide quantitative upper bounds on the total variation distance between the invariant measure of the uHMC chain and the true target measure. As a consequence, we show that an $\varepsilon$-accurate approximation of the target distribution $\mu$ in total variation distance can be achieved by uHMC for a broad class of models with $O\left(d^{3/4}\varepsilon^{-1/2}\log (d/\varepsilon )\right)$ gradient evaluations, and for mean field models with weak interactions with $O\left(d^{1/2}\varepsilon^{-1/2}\log (d/\varepsilon )\right)$ gradient evaluations. The proofs are based on the construction of successful couplings for uHMC that realize the upper bounds.
Spectral clustering under degree heterogeneity: a case for the random walk Laplacian
Authors: Alexander Modell, Patrick Rubin-Delanchy
Date: 2021-05-03 00:00:00
Tasks: Clustering, Stochastic Block Model

This paper shows that graph spectral embedding using the random walk Laplacian produces vector representations which are completely corrected for node degree. Under a generalised random dot product graph, the embedding provides uniformly consistent estimates of degree-corrected latent positions, with asymptotically Gaussian error. In the special case of a degree-corrected stochastic block model, the embedding concentrates about K distinct points, representing communities. These can be recovered perfectly, asymptotically, through a subsequent clustering step, without spherical projection, as commonly required by algorithms based on the adjacency or normalised, symmetric Laplacian matrices. While the estimand does not depend on degree, the asymptotic variance of its estimate does -- higher degree nodes are embedded more accurately than lower degree nodes. Our central limit theorem therefore suggests fitting a weighted Gaussian mixture model as the subsequent clustering step, for which we provide an expectation-maximisation algorithm.
Can we aggregate human intelligence? an approach for human centric aggregation using ordered weighted averaging operators
Authors: Shahab Saquib Sohail, Jamshed Siddiqui, Rashid Ali, S. Hamid Hasan, M. Afshar Alam
Date: 2021-05-01 00:00:00
Tasks: Decision Making, Recommendation Systems

The primary objective of this paper is to present an approach for recommender systems that can assimilate ranking to the voters or rankers so that recommendation can be made by giving priority to experts suggestion over usual recommendation. To accomplish this, we have incorporated the concept of human-centric aggregation via Ordered Weighted Aggregation (OWA). Here, we are advocating ranked recommendation where rankers are assigned weights according to their place in the ranking. Further, the recommendation process which is presented here for the recommendation of books to university students exploits linguistic data summaries and Ordered Weighted Aggregation (OWA) technique. In the suggested approach, the weights are assigned in a way that it associates higher weights to best ranked university. The approach has been evaluated over eight different parameters. The superiority of the proposed approach is evident from the evaluation results. We claim that proposed scheme saves storage spaces required in traditional recommender systems as well as it does not need users prior preferences and hence produce a solution for cold start problem. This envisaged that the proposed scheme can be very useful in decision making problems, especially for recommender systems. In addition, it emphasizes on how human-centric aggregation can be useful in recommendation researches, and also it gives a new direction about how various human specific tasks can be numerically aggregated.
Improving Community Detection Performance in Heterogeneous Music Network by Learning Edge-type Usefulness Distribution
Authors: Zheng Gao, Chun Guo, Shutian Ma, Xiaozhong Liu
Date: 2021-05-03 00:00:00
Tasks: Community Detection, Music Recommendation, Recommendation Systems

With music becoming an essential part of daily life, there is an urgent need to develop recommendation systems to assist people targeting better songs with fewer efforts. As the interactions between users and songs naturally construct a complex network, community detection approaches can be applied to reveal users' potential interests on songs by grouping relevant users & songs to the same community. However, as the types of interaction could be heterogeneous, it challenges conventional community detection methods designed originally for homogeneous networks. Although there are existing works on heterogeneous community detection, they are mostly task-driven approaches and not feasible for specific music recommendation. In this paper, we propose a genetic based approach to learn an edge-type usefulness distribution (ETUD) for all edge-types in heterogeneous music networks. ETUD can be regarded as a linear function to project all edges to the same latent space and make them comparable. Therefore a heterogeneous network can be converted to a homogeneous one where those conventional methods are eligible to use. We validate the proposed model on a heterogeneous music network constructed from an online music streaming service. Results show that for conventional methods, ETUD can help to detect communities significantly improving music recommendation accuracy while simultaneously reducing user searching cost.
Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation
Authors: Soyeong Jeong, Jinheon Baek, ChaeHun Park, Jong C. Park
Date: 2021-05-03 00:00:00
Proceeding: NAACL (sdp) 2021 6
Tasks: Information Retrieval, Language Modeling, Language Modelling, Retrieval, Text Generation

One of the challenges in information retrieval (IR) is the vocabulary mismatch problem, which happens when the terms between queries and documents are lexically different but semantically similar. While recent work has proposed to expand the queries or documents by enriching their representations with additional relevant terms to address this challenge, they usually require a large volume of query-document pairs to train an expansion model. In this paper, we propose an Unsupervised Document Expansion with Generation (UDEG) framework with a pre-trained language model, which generates diverse supplementary sentences for the original document without using labels on query-document pairs for training. For generating sentences, we further stochastically perturb their embeddings to generate more diverse sentences for document expansion. We validate our framework on two standard IR benchmark datasets. The results show that our framework significantly outperforms relevant expansion baselines for IR.
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness
Authors: Harrie Oosterhuis
Date: 2021-05-03 00:00:00
Tasks: Fairness

Recent work has proposed stochastic Plackett-Luce (PL) ranking models as a robust choice for optimizing relevance and fairness metrics. Unlike their deterministic counterparts that require heuristic optimization algorithms, PL models are fully differentiable. Theoretically, they can be used to optimize ranking metrics via stochastic gradient descent. However, in practice, the computation of the gradient is infeasible because it requires one to iterate over all possible permutations of items. Consequently, actual applications rely on approximating the gradient via sampling techniques. In this paper, we introduce a novel algorithm: PL-Rank, that estimates the gradient of a PL ranking model w.r.t. both relevance and fairness metrics. Unlike existing approaches that are based on policy gradients, PL-Rank makes use of the specific structure of PL models and ranking metrics. Our experimental analysis shows that PL-Rank has a greater sample-efficiency and is computationally less costly than existing policy gradients, resulting in faster convergence at higher performance. PL-Rank further enables the industry to apply PL models for more relevant and fairer real-world ranking systems.
Online Product Feature Recommendations with Interpretable Machine Learning
Authors: Mingming Guo, Nian Yan, Xiquan Cui, Simon Hughes, Khalifeh Al Jadda
Date: 2021-04-28 00:00:00
Tasks: BIG-bench Machine Learning, Interpretable Machine Learning, Open-Ended Question Answering, Recommendation Systems

Product feature recommendations are critical for online customers to purchase the right products based on the right features. For a customer, selecting the product that has the best trade-off between price and functionality is a time-consuming step in an online shopping experience, and customers can be overwhelmed by the available choices. However, determining the set of product features that most differentiate a particular product is still an open question in online recommender systems. In this paper, we focus on using interpretable machine learning methods to tackle this problem. First, we identify this unique product feature recommendation problem from a business perspective on a major US e-commerce site. Second, we formulate the problem into a price-driven supervised learning problem to discover the product features that could best explain the price of a product in a given product category. We build machine learning models with a model-agnostic method Shapley Values to understand the importance of each feature, rank and recommend the most essential features. Third, we leverage human experts to evaluate its relevancy. The results show that our method is superior to a strong baseline method based on customer behavior and significantly boosts the coverage by 45%. Finally, our proposed method shows comparable conversion rate against the baseline in online A/B tests.
SmoothI: Smooth Rank Indicators for Differentiable IR Metrics
Authors: Thibaut Thonet, Yagmur Gizem Cinar, Eric Gaussier, Minghan Li, Jean-Michel Renders
Date: 2021-05-03 00:00:00
Tasks: Information Retrieval, Learning-To-Rank, Retrieval

Information retrieval (IR) systems traditionally aim to maximize metrics built on rankings, such as precision or NDCG. However, the non-differentiability of the ranking operation prevents direct optimization of such metrics in state-of-the-art neural IR models, which rely entirely on the ability to compute meaningful gradients. To address this shortcoming, we propose SmoothI, a smooth approximation of rank indicators that serves as a basic building block to devise differentiable approximations of IR metrics. We further provide theoretical guarantees on SmoothI and derived approximations, showing in particular that the approximation errors decrease exponentially with an inverse temperature-like hyperparameter that controls the quality of the approximations. Extensive experiments conducted on four standard learning-to-rank datasets validate the efficacy of the listwise losses based on SmoothI, in comparison to previously proposed ones. Additional experiments with a vanilla BERT ranking model on a text-based IR task also confirm the benefits of our listwise approach.
A Single-Layer Asymmetric RNN: Potential Low Hardware Complexity Linear Equation Solver
Authors: Mohammad Samar Ansari
Date: 2021-05-01 00:00:00

A single layer neural network for the solution of linear equations is presented. The proposed circuit is based on the standard Hopfield model albeit with the added flexibility that the interconnection weight matrix need not be symmetric. This results in an asymmetric Hopfield neural network capable of solving linear equations. PSPICE simulation results are given which verify the theoretical predictions. Experimental results for circuits set up to solve small problems further confirm the operation of the proposed circuit.
Paradiseo: From a Modular Framework for Evolutionary Computation to the Automated Design of Metaheuristics ---22 Years of Paradiseo---
Authors: Johann Dreo, Arnaud Liefooghe, Sébastien Verel, Marc Schoenauer, Juan J. Merelo, Alexandre Quemy, Benjamin Bouvier, Jan Gmys
Date: 2021-05-02 00:00:00
Tasks: Efficient Exploration, Metaheuristic Optimization

The success of metaheuristic optimization methods has led to the development of a large variety of algorithm paradigms. However, no algorithm clearly dominates all its competitors on all problems. Instead, the underlying variety of landscapes of optimization problems calls for a variety of algorithms to solve them efficiently. It is thus of prior importance to have access to mature and flexible software frameworks which allow for an efficient exploration of the algorithm design space. Such frameworks should be flexible enough to accommodate any kind of metaheuristics, and open enough to connect with higher-level optimization, monitoring and evaluation softwares. This article summarizes the features of the ParadisEO framework, a comprehensive C++ free software which targets the development of modular metaheuristics. ParadisEO provides a highly modular architecture, a large set of components, speed of execution and automated algorithm design features, which are key to modern approaches to metaheuristics development.
Ensemble Feature Extraction for Multi-Container Quality-Diversity Algorithms
Authors: Leo Cazenille
Date: 2021-05-03 00:00:00
Tasks: Diversity

Quality-Diversity algorithms search for large collections of diverse and high-performing solutions, rather than just for a single solution like typical optimisation methods. They are specially adapted for multi-modal problems that can be solved in many different ways, such as complex reinforcement learning or robotics tasks. However, these approaches are highly dependent on the choice of feature descriptors (FDs) quantifying the similarity in behaviour of the solutions. While FDs usually needs to be hand-designed, recent studies have proposed ways to define them automatically by using feature extraction techniques, such as PCA or Auto-Encoders, to learn a representation of the problem from previously explored solutions. Here, we extend these approaches to more complex problems which cannot be efficiently explored by relying only on a single representation but require instead a set of diverse and complementary representations. We describe MC-AURORA, a Quality-Diversity approach that optimises simultaneously several collections of solutions, each with a different set of FDs, which are, in turn, defined automatically by an ensemble of modular auto-encoders. We show that this approach produces solutions that are more diverse than those produced by single-representation approaches.
Deep Face Recognition
Authors: O. M. Parkhi, A. Vedaldi, A. Zisserman
Date: 2015-01-01 00:00:00
Tasks: Face Recognition, Face Verification

The goal of this paper is face recognition -- from either a single photograph or from a set of faces tracked in a video. Recent progress in this area has been due to two factors: (i) end to end learning for the task using a convolutional neural network (CNN), and (ii) the availability of very large scale training datasets. We make two contributions: first, we show how a very large scale dataset (2.6M images, over 2.6K people) can be assembled by a combination of automation and human in the loop, and discuss the trade off between data purity and time; second, we traverse through the complexities of deep network training and face recognition to present methods and procedures to achieve comparable state of the art results on the standard LFW and YTF face benchmarks.
OpenFace: A general-purpose face recognition library with mobile applications
Authors: Brandon Amos, Bartosz Ludwiczuk, Mahadev Satyanarayanan
Date: 2016-01-01 00:00:00
Tasks: Face Recognition, Face Verification

Cameras are becoming ubiquitous in the Internet of Things (IoT) and can use face recognition technology to improve context. There is a large accuracy gap between today’s publicly available face recognition systems and the state-of-the-art private face recognition systems. This paper presents our OpenFace face recognition library that bridges this accuracy gap. We show that OpenFace provides near-human accuracy on the LFW benchmark and present a new classification benchmark for mobile scenarios. This paper is intended for non-experts interested in using OpenFace and provides a light introduction to the deep neural network techniques we use.
Dlib-ml: A Machine Learning Toolkit
Authors: Davis E. King
Date: 2009-01-01 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Clustering, Face Verification, regression

There are many excellent toolkits which provide support for developing machine learning software in Python, R, Matlab, and similar environments. Dlib-ml is an open source library, targeted at both engineers and research scientists, which aims to provide a similarly rich environment for developing machine learning software in the C++ language. Towards this end, dlib-ml contains an extensible linear algebra toolkit with built in BLAS support. It also houses implementations of algorithms for performing inference in Bayesian networks and kernel-based methods for classification, regression, clustering, anomaly detection, and feature ranking. To enable easy use of these
tools, the entire library has been developed with contract programming, which provides complete and precise documentation as well as powerful debugging tools
CTLR@WiC-TSV: Target Sense Verification using Marked Inputs andPre-trained Models
Authors: José G. Moreno, Elvys Linhares Pontes, Gaël Dias
Date: 2021-04-30 00:00:00
Proceeding: SemDeep 2021 1
Tasks: Entity Linking, Position

This paper describes the CTRL participation in the Target Sense Verification of the Words in Context challenge (WiC-TSV) at SemDeep6. Our strategy is based on a simplistic annotation scheme of the target words to later be classified by well-known pre-trained neural models. In particular, the marker allows to include position information to help models to correctly identify the word to disambiguate. Results on the challenge show that our strategy outperforms other participants (+11, 4 Accuracy points) and strong baselines (+1, 7 Accuracy points).
Word Sense Disambiguation with Transformer Models
Authors: Pierre-Yves Vandenbussche, Tony Scerri, Ron Daniel Jr.
Date: 2021-04-30 00:00:00
Proceeding: SemDeep 2021 1
Tasks: Entity Linking, Word Sense Disambiguation

In this paper, we tackle the task of Word Sense Disambiguation (WSD). We present our system submitted to the Word-in-Context Target Sense Verification challenge, part of the SemDeep workshop at IJCAI 2020 (Breit et al., 2020). That challenge asks participants to predict if a specific mention of a word in a text matches a pre-defined sense. Our approach uses pre-trained transformer models such as BERT that are fine-tuned on the task using different architecture strategies. Our model achieves the best accuracy and precision on Subtask 1 – make use of definitions for deciding whether the target word in context corresponds to the given sense or not. We believe the strategies we explored in the context of this challenge can be useful to other Natural Language Processing tasks.
Present and Future of Reconfigurable Intelligent Surface-Empowered Communications
Authors: Ertugrul Basar, H. Vincent Poor
Date: 2021-05-03 00:00:00

Signal processing and communication communities have witnessed the rise of many exciting communication technologies in recent years. Notable examples include alternative waveforms, massive multiple-input multiple-output (MIMO) signaling, non-orthogonal multiple access (NOMA), joint communications and sensing, sparse vector coding, index modulation, and so on. It is inevitable that 6G wireless networks will require a rethinking of wireless communication systems and technologies, particularly at the physical layer (PHY), considering the fact that the cellular industry reached another important milestone with the development of 5G wireless networks with diverse applications. Within this perspective, this article aims to shed light on the rising concept of reconfigurable intelligent surface (RIS)-empowered communications towards 6G wireless networks. Software-defined RISs can manipulate their impinging signals in an effective way to boost certain key performance indicators. We discuss the recent developments in the field and put forward promising candidates for future research and development. Specifically, we put our emphasis on active, transmitter-type, transmissive-reflective, and standalone RISs, by discussing their advantages and disadvantages compared to reflective RIS designs. Finally, we also envision an ultimate RIS architecture, which is able to adjust its operation modes dynamically, and introduce the new concept of PHY slicing over RISs towards 6G wireless networks.
A Rate-Splitting Strategy to Enable Joint Radar Sensing and Communication with Partial CSIT
Authors: Rafael Cerna-Loli, Onur Dizdar, Bruno Clerckx
Date: 2021-05-03 00:00:00
Tasks: Fairness, Management

In order to manage the increasing interference between radar and communication systems, joint radar and communication (RadCom) systems have attracted increased attention in recent years, with the studies so far considering the assumption of perfect Channel State Information at the Transmitter (CSIT). However, such an assumption is unrealistic and neglects the inevitable CSIT errors that need to be considered to fully exploit the multi-antenna processing and interference management capabilities of a joint RadCom system. In this work, a joint RadCom system is designed which marries the capabilities of a Multiple-Input Multiple-Output (MIMO) radar with Rate-Splitting Multiple Access (RSMA), a powerful downlink communications scheme based on linearly precoded Rate-Splitting (RS) to partially decode multi-user interference (MUI) and partially treat it as noise. In this way, the RadCom precoders are optimized in the presence of partial CSIT to simultaneously maximize the Average Weighted Sum-Rate (AWSR) under QoS rate constraints and minimize the RadCom Beampattern Squared Error (BSE) against an ideal MIMO radar beampattern. Simulation results demonstrate that RSMA provides the RadCom with more robustness, flexibility and user rate fairness compared to the baseline joint RadCom system based on Space Division Multiple Access (SDMA).
PER Measurement of BLE in RF Interference and Harsh Electromagnetic Environment
Authors: Mir Lodro, Gabriele Gradoni, Ana Vukovic, David Thomas, Steve Greedy
Date: 2021-05-01 00:00:00

Bluetooth Low Energy (BLE) is a short-range data transmission technology that is used for multimedia file sharing, home automation, and internet-of-things application. In this work, we perform packet error rate (PER) measurement and RF testing of BLE receiver in the harsh electromagnetic environment and in presence of RF interference. We check the PER performance in the line-of-sight (LOS) and non-line-of-sight (NLOS) scenario in absence of any interfering signal and in presence of wideband WLAN interference. The BLE PER measurements are conducted in a large reverberation chamber which is a rich scattering environment. Software-defined-radio has been used to create BLE communication link for PER measurement in LOS and NLOS configuration. The BLE PER is measured both in the presence and in absence of WLAN interference. Our measurement results show a higher PER for uncoded BLE PHY modes in NLOS channel condition and in presence of wideband interference. Whereas coded BLE PHY modes i.e. LE500K and LE125K are robust to interference with lower PER measurements.