pwc-archive/papers-with-abstracts huggingface dataset:
The Impossibility Theorem of Machine Fairness -- A Causal Perspective
Authors: Kailash Karthik Saravanakumar
Date: 2020-07-12 00:00:00
Tasks: Fairness

With the increasing pervasive use of machine learning in social and economic settings, there has been an interest in the notion of machine bias in the AI community. Models trained on historic data reflect biases that exist in society and propagated them to the future through their decisions. There are three prominent metrics of machine fairness used in the community, and it has been shown statistically that it is impossible to satisfy them all at the same time. This has led to an ambiguity with regards to the definition of fairness. In this report, a causal perspective to the impossibility theorem of fairness is presented along with a causal goal for machine fairness.
NISP: A Multi-lingual Multi-accent Dataset for Speaker Profiling
Authors: Shareef Babu Kalluri, Deepu Vijayasenan, Sriram Ganapathy, Ragesh Rajan M, Prashant Krishnan
Date: 2020-07-12 00:00:00
Tasks: Speaker Profiling

Many commercial and forensic applications of speech demand the extraction of information about the speaker characteristics, which falls into the broad category of speaker profiling. The speaker characteristics needed for profiling include physical traits of the speaker like height, age, and gender of the speaker along with the native language of the speaker. Many of the datasets available have only partial information for speaker profiling. In this paper, we attempt to overcome this limitation by developing a new dataset which has speech data from five different Indian languages along with English. The metadata information for speaker profiling applications like linguistic information, regional information, and physical characteristics of a speaker are also collected. We call this dataset as NITK-IISc Multilingual Multi-accent Speaker Profiling (NISP) dataset. The description of the dataset, potential applications, and baseline results for speaker profiling on this dataset are provided in this paper.
Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation
Authors: Yuxuan Song, Ning Miao, Hao Zhou, Lantao Yu, Mingxuan Wang, Lei LI
Date: 2020-07-12 00:00:00
Tasks: Density Ratio Estimation, Diversity, Text Generation

Auto-regressive sequence generative models trained by Maximum Likelihood Estimation suffer the exposure bias problem in practical finite sample scenarios. The crux is that the number of training samples for Maximum Likelihood Estimation is usually limited and the input data distributions are different at training and inference stages. Many method shave been proposed to solve the above problem (Yu et al., 2017; Lu et al., 2018), which relies on sampling from the non-stationary model distribution and suffers from high variance or biased estimations. In this paper, we propose{\psi}-MLE, a new training scheme for auto-regressive sequence generative models, which is effective and stable when operating at large sample space encountered in text generation. We derive our algorithm from a new perspective of self-augmentation and introduce bias correction with density ratio estimation. Extensive experimental results on synthetic data and real-world text generation tasks demonstrate that our method stably outperforms Maximum Likelihood Estimation and other state-of-the-art sequence generative models in terms of both quality and diversity.
MeDaS: An open-source platform as service to help break the walls between medicine and informatics
Authors: Liang Zhang, Johann Li, Ping Li, Xiaoyuan Lu, Peiyi Shen, Guangming Zhu, Syed Afaq Shah, Mohammed Bennarmoun, Kun Qian, Björn W. Schuller
Date: 2020-07-12 00:00:00
Tasks: Medical Image Analysis

In the past decade, deep learning (DL) has achieved unprecedented success in numerous fields including computer vision, natural language processing, and healthcare. In particular, DL is experiencing an increasing development in applications for advanced medical image analysis in terms of analysis, segmentation, classification, and furthermore. On the one hand, tremendous needs that leverage the power of DL for medical image analysis are arising from the research community of a medical, clinical, and informatics background to jointly share their expertise, knowledge, skills, and experience. On the other hand, barriers between disciplines are on the road for them often hampering a full and efficient collaboration. To this end, we propose our novel open-source platform, i.e., MeDaS -- the MeDical open-source platform as Service. To the best of our knowledge, MeDaS is the first open-source platform proving a collaborative and interactive service for researchers from a medical background easily using DL related toolkits, and at the same time for scientists or engineers from information sciences to understand the medical knowledge side. Based on a series of toolkits and utilities from the idea of RINV (Rapid Implementation aNd Verification), our proposed MeDaS platform can implement pre-processing, post-processing, augmentation, visualization, and other phases needed in medical image analysis. Five tasks including the subjects of lung, liver, brain, chest, and pathology, are validated and demonstrated to be efficiently realisable by using MeDaS.
Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies
Authors: Daniel Vidali Fryer, Inga Strümke, Hien Nguyen
Date: 2020-07-12 00:00:00
Tasks: Attribute, BIG-bench Machine Learning, Fairness

Shapley values have become increasingly popular in the machine learning literature thanks to their attractive axiomatisation, flexibility, and uniqueness in satisfying certain notions of `fairness'. The flexibility arises from the myriad potential forms of the Shapley value \textit{game formulation}. Amongst the consequences of this flexibility is that there are now many types of Shapley values being discussed, with such variety being a source of potential misunderstanding. To the best of our knowledge, all existing game formulations in the machine learning and statistics literature fall into a category which we name the model-dependent category of game formulations. In this work, we consider an alternative and novel formulation which leads to the first instance of what we call model-independent Shapley values. These Shapley values use a (non-parametric) measure of non-linear dependence as the characteristic function. The strength of these Shapley values is in their ability to uncover and attribute non-linear dependencies amongst features. We introduce and demonstrate the use of the energy distance correlations, affine-invariant distance correlation, and Hilbert-Shmidt independence criterion as Shapley value characteristic functions. In particular, we demonstrate their potential value for exploratory data analysis and model diagnostics. We conclude with an interesting expository application to a classical medical survey data set.
Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory
Authors: Paulo Tabuada, Bahman Gharesifard
Date: 2020-07-12 00:00:00
Proceeding: ICLR 2021 1

In this paper, we explain the universal approximation capabilities of deep residual neural networks through geometric nonlinear control. Inspired by recent work establishing links between residual networks and control systems, we provide a general sufficient condition for a residual network to have the power of universal approximation by asking the activation function, or one of its derivatives, to satisfy a quadratic differential equation. Many activation functions used in practice satisfy this assumption, exactly or approximately, and we show this property to be sufficient for an adequately deep neural network with $n+1$ neurons per layer to approximate arbitrarily well, on a compact set and with respect to the supremum norm, any continuous function from $\mathbb{R}^n$ to $\mathbb{R}^n$. We further show this result to hold for very simple architectures for which the weights only need to assume two values. The first key technical contribution consists of relating the universal approximation problem to controllability of an ensemble of control systems corresponding to a residual network and to leverage classical Lie algebraic techniques to characterize controllability. The second technical contribution is to identify monotonicity as the bridge between controllability of finite ensembles and uniform approximability on compact sets.
Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search
Authors: Yige Peng, Lei Bi, Michael Fulham, Dagan Feng, Jinman Kim
Date: 2020-07-12 00:00:00
Tasks: Computed Tomography (CT), Neural Architecture Search, Prognosis

'Radiomics' is a method that extracts mineable quantitative features from radiographic images. These features can then be used to determine prognosis, for example, predicting the development of distant metastases (DM). Existing radiomics methods, however, require complex manual effort including the design of hand-crafted radiomic features and their extraction and selection. Recent radiomics methods, based on convolutional neural networks (CNNs), also require manual input in network architecture design and hyper-parameter tuning. Radiomic complexity is further compounded when there are multiple imaging modalities, for example, combined positron emission tomography - computed tomography (PET-CT) where there is functional information from PET and complementary anatomical localization information from computed tomography (CT). Existing multi-modality radiomics methods manually fuse the data that are extracted separately. Reliance on manual fusion often results in sub-optimal fusion because they are dependent on an 'expert's' understanding of medical images. In this study, we propose a multi-modality neural architecture search method (MM-NAS) to automatically derive optimal multi-modality image features for radiomics and thus negate the dependence on a manual process. We evaluated our MM-NAS on the ability to predict DM using a public PET-CT dataset of patients with soft-tissue sarcomas (STSs). Our results show that our MM-NAS had a higher prediction accuracy when compared to state-of-the-art radiomics methods.
Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model
Authors: John Janiczek, Parth Thaker, Gautam Dasarathy, Christopher S. Edwards, Philip Christensen, Suren Jayasuriya
Date: 2020-07-12 00:00:00
Proceeding: ECCV 2020 8
Tasks: Hyperspectral Unmixing, Inverse Rendering

Hyperspectral unmixing is an important remote sensing task with applications including material identification and analysis. Characteristic spectral features make many pure materials identifiable from their visible-to-infrared spectra, but quantifying their presence within a mixture is a challenging task due to nonlinearities and factors of variation. In this paper, spectral variation is considered from a physics-based approach and incorporated into an end-to-end spectral unmixing algorithm via differentiable programming. The dispersion model is introduced to simulate realistic spectral variation, and an efficient method to fit the parameters is presented. Then, this dispersion model is utilized as a generative model within an analysis-by-synthesis spectral unmixing algorithm. Further, a technique for inverse rendering using a convolutional neural network to predict parameters of the generative model is introduced to enhance performance and speed when training data is available. Results achieve state-of-the-art on both infrared and visible-to-near-infrared (VNIR) datasets, and show promise for the synergy between physics-based models and deep learning in hyperspectral unmixing in the future.
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
Authors: William J. Wilkinson, Paul E. Chang, Michael Riis Andersen, Arno Solin
Date: 2020-07-12 00:00:00
Proceeding: ICML 2020 1
Tasks: Bayesian Inference, Computational Efficiency, Gaussian Processes, Variational Inference

We formulate approximate Bayesian inference in non-conjugate temporal and spatio-temporal Gaussian process models as a simple parameter update rule applied during Kalman smoothing. This viewpoint encompasses most inference schemes, including expectation propagation (EP), the classical (Extended, Unscented, etc.) Kalman smoothers, and variational inference. We provide a unifying perspective on these algorithms, showing how replacing the power EP moment matching step with linearisation recovers the classical smoothers. EP provides some benefits over the traditional methods via introduction of the so-called cavity distribution, and we combine these benefits with the computational efficiency of linearisation, providing extensive empirical analysis demonstrating the efficacy of various algorithms under this unifying framework. We provide a fast implementation of all methods in JAX.
Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction
Authors: Chen Qin, Jo Schlemper, Kerstin Hammernik, Jinming Duan, Ronald M. Summers, Daniel Rueckert
Date: 2020-07-12 00:00:00
Tasks: Image Reconstruction, SSIM

We present a deep network interpolation strategy for accelerated parallel MR image reconstruction. In particular, we examine the network interpolation in parameter space between a source model that is formulated in an unrolled scheme with L1 and SSIM losses and its counterpart that is trained with an adversarial loss. We show that by interpolating between the two different models of the same network structure, the new interpolated network can model a trade-off between perceptual quality and fidelity.
IllumiNet: Transferring Illumination from Planar Surfaces to Virtual Objects in Augmented Reality
Authors: Di Xu, Zhen Li, Yanning Zhang, Qi Cao
Date: 2020-07-12 00:00:00

This paper presents an illumination estimation method for virtual objects in real environment by learning. While previous works tackled this problem by reconstructing high dynamic range (HDR) environment maps or the corresponding spherical harmonics, we do not seek to recover the lighting environment of the entire scene. Given a single RGB image, our method directly infers the relit virtual object by transferring the illumination features extracted from planar surfaces in the scene to the desired geometries. Compared to previous works, our approach is more robust as it works in both indoor and outdoor environments with spatially-varying illumination. Experiments and evaluation results show that our approach outperforms the state-of-the-art quantitatively and qualitatively, achieving realistic augmented experience.
Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals
Authors: Tomi Kinnunen, Héctor Delgado, Nicholas Evans, Kong Aik Lee, Ville Vestman, Andreas Nautsch, Massimiliano Todisco, Xin Wang, Md Sahidullah, Junichi Yamagishi, Douglas A. Reynolds
Date: 2020-07-12 00:00:00
Tasks: Speaker Verification

Recent years have seen growing efforts to develop spoofing countermeasures (CMs) to protect automatic speaker verification (ASV) systems from being deceived by manipulated or artificial inputs. The reliability of spoofing CMs is typically gauged using the equal error rate (EER) metric. The primitive EER fails to reflect application requirements and the impact of spoofing and CMs upon ASV and its use as a primary metric in traditional ASV research has long been abandoned in favour of risk-based approaches to assessment. This paper presents several new extensions to the tandem detection cost function (t-DCF), a recent risk-based approach to assess the reliability of spoofing CMs deployed in tandem with an ASV system. Extensions include a simplified version of the t-DCF with fewer parameters, an analysis of a special case for a fixed ASV system, simulations which give original insights into its interpretation and new analyses using the ASVspoof 2019 database. It is hoped that adoption of the t-DCF for the CM assessment will help to foster closer collaboration between the anti-spoofing and ASV research communities.
Stance Detection in Web and Social Media: A Comparative Study
Authors: Shalmoli Ghosh, Prajwal Singhania, Siddharth Singh, Koustav Rudra, Saptarshi Ghosh
Date: 2020-07-12 00:00:00
Tasks: Articles, Stance Detection

Online forums and social media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances. Several methodologies for automatic stance detection from text have been proposed in literature. To our knowledge, there has not been any systematic investigation towards their reproducibility, and their comparative performances. In this work, we explore the reproducibility of several existing stance detection models, including both neural models and classical classifier-based models. Through experiments on two datasets -- (i)~the popular SemEval microblog dataset, and (ii)~a set of health-related online news articles -- we also perform a detailed comparative analysis of various methods and explore their shortcomings. Implementations of all algorithms discussed in this paper are available at https://github.com/prajwal1210/Stance-Detection-in-Web-and-Social-Media.
A Graph Symmetrisation Bound on Channel Information Leakage under Blowfish Privacy
Authors: Tobias Edwards, Benjamin I. P. Rubinstein, Zuhe Zhang, Sanming Zhou
Date: 2020-07-12 00:00:00

Blowfish privacy is a recent generalisation of differential privacy that enables improved utility while maintaining privacy policies with semantic guarantees, a factor that has driven the popularity of differential privacy in computer science. This paper relates Blowfish privacy to an important measure of privacy loss of information channels from the communications theory community: min-entropy leakage. Symmetry in an input data neighbouring relation is central to known connections between differential privacy and min-entropy leakage. But while differential privacy exhibits strong symmetry, Blowfish neighbouring relations correspond to arbitrary simple graphs owing to the framework's flexible privacy policies. To bound the min-entropy leakage of Blowfish-private mechanisms we organise our analysis over symmetrical partitions corresponding to orbits of graph automorphism groups. A construction meeting our bound with asymptotic equality demonstrates tightness.
Probability Learning based Tabu Search for the Budgeted Maximum Coverage Problem
Authors: Liwen Li, Zequn Wei, Jin-Kao Hao, Kun He
Date: 2020-07-12 00:00:00

Knapsack problems are classic models that can formulate a wide range of applications. In this work, we deal with the Budgeted Maximum Coverage Problem (BMCP), which is a generalized 0-1 knapsack problem. Given a set of items with nonnegative weights and a set of elements with nonnegative profits, where each item is composed of a subset of elements, BMCP aims to pack a subset of items in a capacity-constrained knapsack such that the total weight of the selected items does not exceed the knapsack capacity, and the total profit of the associated elements is maximized. Note that each element is counted once even if it is covered multiple times. BMCP is closely related to the Set-Union Knapsack Problem (SUKP) that is well studied in recent years. As the counterpart problem of SUKP, however, BMCP was introduced early in 1999 but since then it has been rarely studied, especially there is no practical algorithm proposed. By combining the reinforcement learning technique to the local search procedure, we propose a probability learning based tabu search (PLTS) algorithm for addressing this NP-hard problem. The proposed algorithm iterates through two distinct phases, namely a tabu search phase and a probability learning based perturbation phase. As there is no benchmark instances proposed in the literature, we generate 30 benchmark instances with varied properties. Experimental results demonstrate that our PLTS algorithm significantly outperforms the general CPLEX solver for solving the challenging BMCP in terms of the solution quality.
Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?
Authors: Tian Bian, Xi Xiao, Tingyang Xu, Yu Rong, Wenbing Huang, Peilin Zhao, Junzhou Huang
Date: 2020-07-12 00:00:00
Tasks: Clustering, Community Detection, Graph Attention, Graph Learning, Node Clustering

Graph Identification (GI) has long been researched in graph learning and is essential in certain applications (e.g. social community detection). Specifically, GI requires to predict the label/score of a target graph given its collection of node features and edge connections. While this task is common, more complex cases arise in practice---we are supposed to do the inverse thing by, for example, grouping similar users in a social network given the labels of different communities. This triggers an interesting thought: can we identify nodes given the labels of the graphs they belong to? Therefore, this paper defines a novel problem dubbed Inverse Graph Identification (IGI), as opposed to GI. Upon a formal discussion of the variants of IGI, we choose a particular case study of node clustering by making use of the graph labels and node features, with an assistance of a hierarchical graph that further characterizes the connections between different graphs. To address this task, we propose Gaussian Mixture Graph Convolutional Network (GMGCN), a simple yet effective method that makes the node-level message passing process using Graph Attention Network (GAT) under the protocol of GI and then infers the category of each node via a Gaussian Mixture Layer (GML). The training of GMGCN is further boosted by a proposed consensus loss to take advantage of the structure of the hierarchical graph. Extensive experiments are conducted to test the rationality of the formulation of IGI. We verify the superiority of the proposed method compared to other baselines on several benchmarks we have built up. We will release our codes along with the benchmark data to facilitate more research attention to the IGI problem.
Relational-Grid-World: A Novel Relational Reasoning Environment and An Agent Model for Relational Information Extraction
Authors: Faruk Kucuksubasi, Elif Surer
Date: 2020-07-12 00:00:00
Tasks: Decision Making, Reinforcement Learning (RL), Relational Reasoning

Reinforcement learning (RL) agents are often designed specifically for a particular problem and they generally have uninterpretable working processes. Statistical methods-based agent algorithms can be improved in terms of generalizability and interpretability using symbolic Artificial Intelligence (AI) tools such as logic programming. In this study, we present a model-free RL architecture that is supported with explicit relational representations of the environmental objects. For the first time, we use the PrediNet network architecture in a dynamic decision-making problem rather than image-based tasks, and Multi-Head Dot-Product Attention Network (MHDPA) as a baseline for performance comparisons. We tested two networks in two environments ---i.e., the baseline Box-World environment and our novel environment, Relational-Grid-World (RGW). With the procedurally generated RGW environment, which is complex in terms of visual perceptions and combinatorial selections, it is easy to measure the relational representation performance of the RL agents. The experiments were carried out using different configurations of the environment so that the presented module and the environment were compared with the baselines. We reached similar policy optimization performance results with the PrediNet architecture and MHDPA; additionally, we achieved to extract the propositional representation explicitly ---which makes the agent's statistical policy logic more interpretable and tractable. This flexibility in the agent's policy provides convenience for designing non-task-specific agent architectures. The main contributions of this study are two-fold ---an RL agent that can explicitly perform relational reasoning, and a new environment that measures the relational reasoning capabilities of RL agents.