pwc-archive/papers-with-abstracts huggingface dataset:
Complete Variable-Length Codes: An Excursion into Word Edit Operations
Authors: Jean Néraud
Date: 2019-12-05 00:00:00

Given an alphabet A and a binary relation $\tau$ $\subseteq$ A * x A * , a language X $\subseteq$ A * is $\tau$-independent if $\tau$ (X) $\cap$ X = $\emptyset$; X is $\tau$-closed if $\tau$ (X) $\subseteq$ X. The language X is complete if any word over A is a factor of some concatenation of words in X. Given a family of languages F containing X, X is maximal in F if no other set of F can stricly contain X. A language X $\subseteq$ A * is a variable-length code if any equation among the words of X is necessarily trivial. The study discusses the relationship between maximality and completeness in the case of $\tau$-independent or $\tau$-closed variable-length codes. We focus to the binary relations by which the images of words are computed by deleting, inserting, or substituting some characters.
Measuring Social Bias in Knowledge Graph Embeddings
Authors: Joseph Fisher, Dave Palfrey, Christos Christodoulopoulos, Arpit Mittal
Date: 2019-12-05 00:00:00
Tasks: Knowledge Graph Embeddings, Word Embeddings

It has recently been shown that word embeddings encode social biases, with a harmful impact on downstream tasks. However, to this point there has been no similar work done in the field of graph embeddings. We present the first study on social bias in knowledge graph embeddings, and propose a new metric suitable for measuring such bias. We conduct experiments on Wikidata and Freebase, and show that, as with word embeddings, harmful social biases related to professions are encoded in the embeddings with respect to gender, religion, ethnicity and nationality. For example, graph embeddings encode the information that men are more likely to be bankers, and women more likely to be homekeepers. As graph embeddings become increasingly utilized, we suggest that it is important the existence of such biases are understood and steps taken to mitigate their impact.
pyannote.audio: neural building blocks for speaker diarization
Authors: Hervé Bredin, Ruiqing Yin, Juan Manuel Coria, Gregory Gelly, Pavel Korshunov, Marvin Lavechin, Diego Fustes, Hadrien Titeux, Wassim Bouaziz, Marie-Philippe Gill
Date: 2019-11-04 00:00:00
Tasks: Action Detection, Activity Detection, BIG-bench Machine Learning, Change Detection, speaker-diarization, Speaker Diarization

We introduce pyannote.audio, an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it provides a set of trainable end-to-end neural building blocks that can be combined and jointly optimized to build speaker diarization pipelines. pyannote.audio also comes with pre-trained models covering a wide range of domains for voice activity detection, speaker change detection, overlapped speech detection, and speaker embedding -- reaching state-of-the-art performance for most of them.
An Implementation of the Poisson Multi-Bernoulli Mixture Trajectory Filter via Dual Decomposition
Authors: Yuxuan Xia, Karl Granström, Lennart Svensson, Ángel F. García-Fernández
Date: 2018-11-29 00:00:00

This paper proposes an efficient implementation of the Poisson multi-Bernoulli mixture (PMBM) trajectory filter. The proposed implementation performs track-oriented N-scan pruning to limit complexity, and uses dual decomposition to solve the involved multi-frame assignment problem. In contrast to the existing PMBM filter for sets of targets, the PMBM trajectory filter is based on sets of trajectories which ensures that track continuity is formally maintained. The resulting filter is an efficient and scalable approximation to a Bayes optimal multi-target tracking algorithm, and its performance is compared, in a simulation study, to the PMBM target filter, and the delta generalized labelled multi-Bernoulli filter, in terms of state/trajectory estimation error and computational time.
On Learning Symmetric Locomotion
Authors: Farzad Abdolhosseini, Hung Yu Ling, Zhaoming Xie, Xue Bin Peng, Michiel Van de Panne
Date: 2019-10-28 00:00:00
Proceeding: Proceedings of ACM SIGGRAPH Motion, Interaction, and Games (MIG 2019) 2019 10

Human and animal gaits are often symmetric in nature, which points to the use of motion symmetry as a potentially useful source of structure that can be exploited for learning. By encouraging symmetric motion, the learning may be faster, converge to more efficient solutions, and be more aesthetically pleasing. We describe, compare, and evaluate four practical methods for encouraging motion symmetry. These are implemented via particular choices of structure for the policy network, data duplication, or via the loss function. We experimentally evaluate the methods in terms of learning performance and achieved symmetry, and provide summary guidelines for the choice of symmetry method. We further describe some practical and conceptual issues that arise. Because similar implementation choices exist for other types of inductive biases, the insights gained may also be relevant to other learning problems with applicable symmetry abstractions.
Unveil stock correlation via a new tensor-based decomposition method
Authors: Giuseppe Brandi, Ruggero Gramatica, Tiziana Di Matteo
Date: 2019-11-14 00:00:00
Tasks: Management, Tensor Decomposition

Portfolio allocation and risk management make use of correlation matrices and heavily rely on the choice of a proper correlation matrix to be used. In this regard, one important question is related to the choice of the proper sample period to be used to estimate a stable correlation matrix. This paper addresses this question and proposes a new methodology to estimate the correlation matrix which doesn't depend on the chosen sample period. This new methodology is based on tensor factorization techniques. In particular, combining and normalizing factor components, we build a correlation matrix which shows emerging structural dependency properties not affected by the sample period. To retrieve the factor components, we propose a new tensor decomposition (which we name Slice-Diagonal Tensor (SDT) factorization) and compare it to the two most used tensor decompositions, the Tucker and the PARAFAC. We have that the new factorization is more parsimonious than the Tucker decomposition and more flexible than the PARAFAC. Moreover, this methodology applied to both simulated and empirical data shows results which are robust to two non-parametric tests, namely Kruskal-Wallis and Kolmogorov-Smirnov tests. Since the resulting correlation matrix features stability and emerging structural dependency properties, it can be used as alternative to other correlation matrices type of measures, including the Person correlation.
Deep Reinforcement Learning in Cryptocurrency Market Making
Authors: Jonathan Sadighian
Date: 2019-11-20 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper sets forth a framework for deep reinforcement learning as applied to market making (DRLMM) for cryptocurrencies. Two advanced policy gradient-based algorithms were selected as agents to interact with an environment that represents the observation space through limit order book data, and order flow arrival statistics. Within the experiment, a forward-feed neural network is used as the function approximator and two reward functions are compared. The performance of each combination of agent and reward function is evaluated by daily and average trade returns. Using this DRLMM framework, this paper demonstrates the effectiveness of deep reinforcement learning in solving stochastic inventory control challenges market makers face.
Assembly ASM291031v2 (Genbank: GCA_002910315.2) identified as assembly of the Northern Dolly Varden (Salvelinus malma malma) genome, and not the Arctic char (S. alpinus) genome
Authors: S. V. Shedko
Date: 2019-12-05 00:00:00
Tasks: Diagnostic

To date, twelve complete genomes representing eleven species belonging to six genera have been sequenced in salmonids. For the genus Salvelinus, it was supposed to sequence the genome of Arctic char, one of the most variable species of vertebrate animals. Sequencing was carried out (Christensen et al., 2018) using the tissues of the female IW2-2015 obtained from the company engaged in industrial aquaculture of chars - Icy Waters Ltd. The company exploits two of its own broodstocks - NL and TR, originating from the chars from the Nauyuk Lake and the Tree River (Nunavut, Canada). Since the complete mitochondrial genome of the female IW2-2015 was absent in the published assembly ASM291031v2, we determined its type and complete sequence from the sequence read archives taken from Genbank. It was found that the female's mitogenome belongs to the BERING haplogroup, which is characteristic of Northern Dolly Varden S. malma malma. Analysis of other unlinked diagnostic loci encoded by nuclear DNA (ITS1, RAG1, SFO-12, SFO-18, SMM-21) also revealed distinctive characters of Northern Dolly Varden in female IW2-2015. It was concluded that the genomic assembly ASM291031v2 was obtained not from an individual of Arctic char S. alpinus, but from an individual of a related species - Northern Dolly Varden S. malma malma. The identical to the IW2-2015 female characteristics of diagnostic loci were found in other individuals from the broodstock TR. Apparently, the broodstock TR is entirely a strain derived from Northern Dolly Varden. Since assembly ASM291031v2 was obtained from a specimen originated from the marginal population of Northern Dolly Varden (Tree R.) isolated from the main range of the species and with some traces of introgressive hybridization, this assembly can hardly be considered as a description of a typical genome of S. malma malma.
SAPD：Soft Anchor-Point Detector
Authors: Chenchen Zhu, Fangyi Chen, Zhiqiang Shen, Marios Savvides
Date: 2019-11-27 00:00:00
Proceeding: arXiv 2019 11
Tasks: feature selection, Object Detection

Recently, anchor-free detectors have shown great potential to outperform anchor-based detectors in terms of both accuracy and speed. In this work, we aim at finding a new balance of speed and accuracy for anchor-free detectors. Two questions are studied: 1) how to make the anchor-free detection head better? 2) how to utilize the power of feature pyramid better? We identify attention bias and feature selection as the main issues for these two questions respectively. We propose to address these issues with a novel training strategy that has two soften optimization techniques, i.e. soft-weighted anchor points and soft-selected pyramid levels. To evaluate the effectiveness, we train a single-stage anchor-free detector called Soft Anchor-Point Detector (SAPD). Experiments show that our concise SAPD pushes the envelope of speed/accuracy trade-off to a new level, outperforming recent state-of-the-art anchor-based and anchor-free, single-stage and multi-stage detectors. Without bells and whistles, our best model can achieve a single-model single-scale AP of 47.4% on COCO. Our fastest version can run up to 5x faster than other detectors with comparable accuracy.
Powerful Speaker Embedding Training Framework by Adversarially Disentangled Identity Representation
Authors: Jianwei Tai, Hang Zhou, Qingjia Huang, Xiaoqi Jia
Date: 2019-11-27 00:00:00
Tasks: Speaker Verification

The main challenge of speaker verification in the wild is the interference caused by irrelevant information in speech and the lack of speaker labels in speech datasets. In order to solve the above problems, we propose a novel speaker embedding training framework based on adversarially disentangled identity representation. Our key insight is to adversarially learn the identity-purified features for speaker verification, and learn an identity-irrelated feature whose speaker information cannot be distinguished. Based on the existing state-of-the-art speaker verification models, we improve them without adjusting the structure and hyper-parameters of any model. Experiments prove that the framework we propose can significantly improve the performance of speaker verification from the original model without any empirical adjustments. Proving that it is particularly useful for alleviating the lack of speaker labels.
High-quality Speech Synthesis Using Super-resolution Mel-Spectrogram
Authors: Leyuan Sheng, Dong-Yan Huang, Evgeniy N. Pavlovskiy
Date: 2019-12-03 00:00:00
Tasks: Image-to-Image Translation, Speech Enhancement, Speech Synthesis, Super-Resolution, Translation, Vocal Bursts Intensity Prediction

In speech synthesis and speech enhancement systems, melspectrograms need to be precise in acoustic representations. However, the generated spectrograms are over-smooth, that could not produce high quality synthesized speech. Inspired by image-to-image translation, we address this problem by using a learning-based post filter combining Pix2PixHD and ResUnet to reconstruct the mel-spectrograms together with super-resolution. From the resulting super-resolution spectrogram networks, we can generate enhanced spectrograms to produce high quality synthesized speech. Our proposed model achieves improved mean opinion scores (MOS) of 3.71 and 4.01 over baseline results of 3.29 and 3.84, while using vocoder Griffin-Lim and WaveNet, respectively.
Making Smart Homes Smarter: Optimizing Energy Consumption with Human in the Loop
Authors: Mudit Verma, Siddhant Bhambri, Saurabh Gupta, Arun Balaji Buduru
Date: 2019-12-06 00:00:00
Tasks: Clustering, Reinforcement Learning, Reinforcement Learning (RL)

Rapid advancements in the Internet of Things (IoT) have facilitated more efficient deployment of smart environment solutions for specific user requirement. With the increase in the number of IoT devices, it has become difficult for the user to control or operate every individual smart device into achieving some desired goal like optimized power consumption, scheduled appliance running time, etc. Furthermore, existing solutions to automatically adapt the IoT devices are not capable enough to incorporate the user behavior. This paper presents a novel approach to accurately configure IoT devices while achieving the twin objectives of energy optimization along with conforming to user preferences. Our work comprises of unsupervised clustering of devices' data to find the states of operation for each device, followed by probabilistically analyzing user behavior to determine their preferred states. Eventually, we deploy an online reinforcement learning (RL) agent to find the best device settings automatically. Results for three different smart homes' data-sets show the effectiveness of our methodology. To the best of our knowledge, this is the first time that a practical approach has been adopted to achieve the above mentioned objectives without any human interaction within the system.
Tools for Mathematical Ludology
Authors: Paul Riggins, David McPherson
Date: 2019-12-06 00:00:00
Tasks: Decision Making, Game Design

We propose the study of mathematical ludology, which aims to formally interrogate questions of interest to game studies and game design in particular. The goal is to extend our mathematical understanding of complex games beyond decision-making---the typical focus of game theory and artificial intelligence efforts---to explore other aspects such as game mechanics, structure, relationships between games, and connections between game rules and user-interfaces, as well as exploring related gameplay phenomena and typical player behavior. In this paper, we build a basic foundation for this line of study by developing a hierarchy of game descriptions, mathematical formalism to compactly describe complex discrete games, and equivalence relations on the space of game systems.
A quantum active learning algorithm for sampling against adversarial attacks
Authors: P. A. M. Casares, M. A. Martin-Delgado
Date: 2019-12-06 00:00:00
Tasks: Active Learning

Adversarial attacks represent a serious menace for learning algorithms and may compromise the security of future autonomous systems. A theorem by Khoury and Hadfield-Menell (KH), provides sufficient conditions to guarantee the robustness of machine learning algorithms, but comes with a caveat: it is crucial to know the smallest distance among the classes of the corresponding classification problem. We propose a theoretical framework that allows us to think of active learning as sampling the most promising new points to be classified, so that the minimum distance between classes can be found and the theorem KH used. Additionally, we introduce a quantum active learning algorithm that makes use of such framework and whose complexity is polylogarithmic in the dimension of the space, $m$, and the size of the initial training data $n$, provided the use of qRAMs; and polynomial in the precision, achieving an exponential speedup over the equivalent classical algorithm in $n$ and $m$. This algorithm may be nevertheless `dequantized' reducing the advantage to polynomial.
Recent advances in deep learning applied to skin cancer detection
Authors: Andre G. C. Pacheco, Renato A. Krohling
Date: 2019-12-06 00:00:00
Tasks: Deep Learning

Skin cancer is a major public health problem around the world. Its early detection is very important to increase patient prognostics. However, the lack of qualified professionals and medical instruments are significant issues in this field. In this context, over the past few years, deep learning models applied to automated skin cancer detection have become a trend. In this paper, we present an overview of the recent advances reported in this field as well as a discussion about the challenges and opportunities for improvement in the current models. In addition, we also present some important aspects regarding the use of these models in smartphones and indicate future directions we believe the field will take.
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Authors: Divyat Mahajan, Chenhao Tan, Amit Sharma
Date: 2019-12-06 00:00:00
Tasks: BIG-bench Machine Learning, counterfactual, Decision Making

To construct interpretable explanations that are consistent with the original ML model, counterfactual examples---showing how the model's output changes with small perturbations to the input---have been proposed. This paper extends the work in counterfactual explanations by addressing the challenge of feasibility of such examples. For explanations of ML models in critical domains such as healthcare and finance, counterfactual examples are useful for an end-user only to the extent that perturbation of feature inputs is feasible in the real world. We formulate the problem of feasibility as preserving causal relationships among input features and present a method that uses (partial) structural causal models to generate actionable counterfactuals. When feasibility constraints cannot be easily expressed, we consider an alternative mechanism where people can label generated CF examples on feasibility: whether it is feasible to intervene and realize the candidate CF example from the original input. To learn from this labelled feasibility data, we propose a modified variational auto encoder loss for generating CF examples that optimizes for feasibility as people interact with its output. Our experiments on Bayesian networks and the widely used ''Adult-Income'' dataset show that our proposed methods can generate counterfactual explanations that better satisfy feasibility constraints than existing methods.. Code repository can be accessed here: \textit{https://github.com/divyat09/cf-feasibility}
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
Authors: Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
Date: 2019-12-06 00:00:00
Proceeding: ICLR 2020 1

We propose to reinterpret a standard discriminative classifier of p(y|x) as an energy based model for the joint distribution p(x,y). In this setting, the standard class probabilities can be easily computed as well as unnormalized values of p(x) and p(x|y). Within this framework, standard discriminative architectures may beused and the model can also be trained on unlabeled data. We demonstrate that energy based training of the joint distribution improves calibration, robustness, andout-of-distribution detection while also enabling our models to generate samplesrivaling the quality of recent GAN approaches. We improve upon recently proposed techniques for scaling up the training of energy based models and presentan approach which adds little overhead compared to standard classification training. Our approach is the first to achieve performance rivaling the state-of-the-artin both generative and discriminative learning within one hybrid model.
A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?
Authors: Mario Bijelic, Tobias Gruber, Werner Ritter
Date: 2019-12-06 00:00:00
Tasks: Autonomous Driving

Autonomous driving at level five does not only means self-driving in the sunshine. Adverse weather is especially critical because fog, rain, and snow degrade the perception of the environment. In this work, current state of the art light detection and ranging (lidar) sensors are tested in controlled conditions in a fog chamber. We present current problems and disturbance patterns for four different state of the art lidar systems. Moreover, we investigate how tuning internal parameters can improve their performance in bad weather situations. This is of great importance because most state of the art detection algorithms are based on undisturbed lidar data.
Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning
Authors: Uthaipon Tantipongpipat, Chris Waites, Digvijay Boob, Amaresh Ankit Siva, Rachel Cummings
Date: 2019-12-06 00:00:00
Tasks: Diversity, Synthetic Data Generation, Vocal Bursts Type Prediction

We introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversarial Networks (GANs). This framework can be used to take in raw sensitive data and privately train a model for generating synthetic data that will satisfy similar statistical properties as the original data. This learned model can generate an arbitrary amount of synthetic data, which can then be freely shared due to the post-processing guarantee of differential privacy. Our framework is applicable to unlabeled mixed-type data, that may include binary, categorical, and real-valued data. We implement this framework on both binary data (MIMIC-III) and mixed-type data (ADULT), and compare its performance with existing private algorithms on metrics in unsupervised settings. We also introduce a new quantitative metric able to detect diversity, or lack thereof, of synthetic data.
Gaussian Process Priors for View-Aware Inference
Authors: Yuxin Hou, Ari Heljakka, Arno Solin
Date: 2019-12-06 00:00:00
Tasks: Novel View Synthesis, Translation

While frame-independent predictions with deep neural networks have become the prominent solutions to many computer vision tasks, the potential benefits of utilizing correlations between frames have received less attention. Even though probabilistic machine learning provides the ability to encode correlation as prior knowledge for inference, there is a tangible gap between the theory and practice of applying probabilistic methods to modern vision problems. For this, we derive a principled framework to combine information coupling between camera poses (translation and orientation) with deep models. We proposed a novel view kernel that generalizes the standard periodic kernel in $\mathrm{SO}(3)$. We show how this soft-prior knowledge can aid several pose-related vision tasks like novel view synthesis and predict arbitrary points in the latent space of generative models, pointing towards a range of new applications for inter-frame reasoning.