pwc-archive/papers-with-abstracts huggingface dataset:
On the Development of a Large Scale Corpus for Native Language Identification
Authors: Thomas Hudson, Sardar Jaf
Date: 2018-12-10 00:00:00
Proceeding: TLT17 2018 12
Tasks: BIG-bench Machine Learning, Language Identification, Native Language Identification

Native Language Identification (NLI) is the task of identifying an author’s native language from their writings in a second language. In this paper, we introduce a new corpus (italki), which is larger than the current corpora. It can be used for training machine learning based systems for classifying and identifying the native language of authors of English text. To examine the usefulness of italki, we evaluate it by using it to train and test some of the well performing NLI systems presented in the 2017 NLI shared task. In this paper, we present some aspects of italki. We show the impact of the variation of italki’s training dataset size of some languages on systems performance. From our empirical finding, we highlight the potential of italki as a large scale corpus for training machine learning classifiers for classifying the native language of authors from their written English text. We obtained promising results that show the potential of italki to improve the performance of current NLI systems. More importantly, we found that training the current NLI systems on italki generalize better than training them on the current corpora.
Where is my URI?
Authors: Andre Valdestilhas, Tommaso Soru, Markus Nentwig, Edgard Marx, Muhammad Saleem, Axel-Cyrille Ngonga Ngomo
Date: 2018-06-15 00:00:00
Proceeding: European Semantic Web Conference 2018 6
Tasks: RDF Dataset Discovery

One of the Semantic Web foundations is the possibility to dereference URIs to let applications negotiate their semantic content. However, this exploitation is often infeasible as the availability of such information depends on the reliability of networks, services, and human factors. Moreover, it has been shown that around 90% of the information published as Linked Open Data is available as data dumps and more than 60% of endpoints are offline. To this end, we propose a Web service called Where is my URI?. Our service aims at indexing URIs and their use in order to let Linked Data consumers find the respective RDF data source, in case such information cannot be retrieved from the URI alone. We rank the corresponding datasets by following the rationale upon which a dataset contributes to the definition of a URI proportionally to the number of literals. We finally describe potential use-cases of applications that can immediately benefit from our simple yet useful service.
More Complete Resultset Retrieval from Large Heterogeneous RDF Sources
Authors: Andre Valdestilhas, Tommaso Soru, Muhammad Saleem
Date: 2019-11-12 00:00:00
Proceeding: ACM 10th International Conference on Knowledge Capture 2019 11
Tasks: RDF Dataset Discovery, Retrieval

Over the last years, the Web of Data has grown significantly. Various interfaces such as LOD Stats, LOD Laudromat, SPARQL endpoints provide access to the hundered of thousands of RDF datasets, representing billions of facts. These datasets are available in different formats such as raw data dumps and HDT files or directly accessible via SPARQL endpoints. Querying such large amount of distributed data is particularly challenging and many of these datasets cannot be directly queried using the SPARQL query language. In order to tackle these problems, we present WimuQ, an integrated query engine to execute SPARQL queries and retrieve results from large amount of heterogeneous RDF data sources. Presently, WimuQ is able to execute both federated and non-federated SPARQL queries over a total of 668,166 datasets from LOD Stats and LOD Laudromat as well as 559 active SPARQL endpoints. These data sources represent a total of 221.7 billion triples from more than 5 terabytes of information from datasets retrieved using the service "Where is My URI" (WIMU). Our evaluation on state-of-the-art real-data benchmarks shows that WimuQ retrieves more complete results for the benchmark queries.
Cross-media Multi-level Alignment with Relation Attention Network
Authors: Jinwei Qi, Yuxin Peng, Yuxin Yuan
Date: 2018-04-25 00:00:00
Tasks: Relation, Retrieval

With the rapid growth of multimedia data, such as image and text, it is a highly challenging problem to effectively correlate and retrieve the data of different media types. Naturally, when correlating an image with textual description, people focus on not only the alignment between discriminative image regions and key words, but also the relations lying in the visual and textual context. Relation understanding is essential for cross-media correlation learning, which is ignored by prior cross-media retrieval works. To address the above issue, we propose Cross-media Relation Attention Network (CRAN) with multi-level alignment. First, we propose visual-language relation attention model to explore both fine-grained patches and their relations of different media types. We aim to not only exploit cross-media fine-grained local information, but also capture the intrinsic relation information, which can provide complementary hints for correlation learning. Second, we propose cross-media multi-level alignment to explore global, local and relation alignments across different media types, which can mutually boost to learn more precise cross-media correlation. We conduct experiments on 2 cross-media datasets, and compare with 10 state-of-the-art methods to verify the effectiveness of proposed approach.
SVQN: Sequential Variational Soft Q-Learning Networks
Authors: Shiyu Huang, Hang Su, Jun Zhu, Ting Chen
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Decision Making, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Partially Observable Markov Decision Processes (POMDPs) are popular and flexible models for real-world decision-making applications that demand the information from past observations to make optimal decisions. Standard reinforcement learning algorithms for solving Markov Decision Processes (MDP) tasks are not applicable, as they cannot infer the unobserved states. In this paper, we propose a novel algorithm for POMDPs, named sequential variational soft Q-learning networks (SVQNs), which formalizes the inference of hidden states and maximum entropy reinforcement learning (MERL) under a unified graphical model and optimizes the two modules jointly. We further design a deep recurrent neural network to reduce the computational complexity of the algorithm. Experimental results show that SVQNs can utilize past information to help decision making for efficient inference, and outperforms other baselines on several challenging tasks. Our ablation study shows that SVQNs have the generalization ability over time and are robust to the disturbance of the observation.
Application of independent component analysis and TOPSIS to deal with dependent criteria in multicriteria decision problems
Authors: Guilherme Dean Pelegrina, Leonardo Tomazeli Duarte, João Marcos Travassos Romano
Date: 2020-02-06 00:00:00
Tasks: blind source separation, Decision Making

A vast number of multicriteria decision making methods have been developed to deal with the problem of ranking a set of alternatives evaluated in a multicriteria fashion. Very often, these methods assume that the evaluation among criteria is statistically independent. However, in actual problems, the observed data may comprise dependent criteria, which, among other problems, may result in biased rankings. In order to deal with this issue, we propose a novel approach whose aim is to estimate, from the observed data, a set of independent latent criteria, which can be seen as an alternative representation of the original decision matrix. A central element of our approach is to formulate the decision problem as a blind source separation problem, which allows us to apply independent component analysis techniques to estimate the latent criteria. Moreover, we consider TOPSIS-based approaches to obtain the ranking of alternatives from the latent criteria. Results in both synthetic and actual data attest the relevance of the proposed approach.
Image Recoloring Based on Object Color Distributions
Authors: Mahmoud Afifi, Brian Price, Scott Cohen, and Michael S. Brown
Date: 2019-05-06 00:00:00
Proceeding: Eurographics 2019 - Short Papers 2019 5
Tasks: Object, Segmentation, Semantic Segmentation

We present a method to perform automatic image recoloring based on the distribution of colors associated with objects present in an image. For example, when recoloring an image containing a sky object, our method incorporates the observation that objects of class 'sky' have a color distribution with three dominant modes for blue (daytime), yellow/red (dusk/dawn), and dark (nighttime). Our work leverages recent deep-learning methods that can perform reasonably accurate object-level segmentation. By using the images in datasets used to train deep-learning object segmentation methods, we are able to model the color distribution of each object class in the dataset. Given a new input image and its associated semantic segmentation (i.e., object mask), we perform color transfer to map the input image color histogram to a set of target color histograms that were constructed based on the learned color distribution of the objects in the image. We show that our framework is able to produce compelling color variations that are often more interesting and unique than results produced by existing methods.
MPB: A modified Poisson blending technique
Authors: Mahmoud Afifi, Khaled F. Hussain
Date: 2015-11-24 00:00:00
Proceeding: Computational Visual Media 2015 11

Image cloning has many useful applications, such as removing unwanted objects, fixing damaged parts of images, and panorama stitching. Instead of using pixel intensities, the gradient domain is used in Poisson image editing; however, it suffers from two main problems: color bleeding and bleeding artifacts. In this paper, a modified Poisson blending (MPB) technique is presented which ensures dependency on the boundary pixels of both target and source images rather than just those of the target. The problem of bleeding artifacts is reduced. This makes the proposed technique suitable for use in video compositing as it avoids the flickering caused by bleeding artifacts. To reduce the problem of color bleeding, we use an additional alpha compositing step. Our experimental results using the proposed technique show that MPB reduces the bleeding problems and generates more natural composited images than other techniques.
As-projective-as-possible bias correction for illumination estimation algorithms
Authors: Mahmoud Afifi, Abhijith Punnappurath, Graham Finlayson, Michael S. Brown
Date: 2019-01-01 00:00:00
Proceeding: JOSA A 2019 1

Illumination estimation is the key routine in a camera's onboard auto-white-balance (AWB) function. Illumination estimation algorithms estimate the color of the scene's illumination from an image in the form of an R,G,B vector in the sensor's raw-RGB color space. While learning-based methods have demonstrated impressive performance for illumination estimation, cameras still rely on simple statistical-based algorithms that are less accurate but capable of executing quickly on the camera's hardware. An effective strategy to improve the accuracy of these fast statistical-based algorithms is to apply a post-estimate bias correction function to transform the estimated R,G,B vector such that it lies closer to the correct solution. Recent work by Finlayson, Interface Focus, 2018 showed that a bias correction function can be formulated as a projective transform because the magnitude of the R,G,B illumination vector does not matter to the AWB procedure. This paper builds on this finding and shows that further improvements can be obtained by using an as-projective-as-possible (APAP) projective transform that locally adapts the projective transform to the input R,G,B vector. We demonstrate the effectiveness of the proposed APAP bias correction on several well-known statistical illumination estimation methods. We also describe a fast lookup method that allows the APAP transform to be performed with only a few lookup operations.
Revisiting Spatial Invariance with Low-Rank Local Connectivity
Authors: Gamaleldin F. Elsayed, Prajit Ramachandran, Jonathon Shlens, Simon Kornblith
Date: 2020-02-07 00:00:00
Proceeding: ICML 2020 1
Tasks: Inductive Bias

Convolutional neural networks are among the most successful architectures in deep learning with this success at least partially attributable to the efficacy of spatial invariance as an inductive bias. Locally connected layers, which differ from convolutional layers only in their lack of spatial invariance, usually perform poorly in practice. However, these observations still leave open the possibility that some degree of relaxation of spatial invariance may yield a better inductive bias than either convolution or local connectivity. To test this hypothesis, we design a method to relax the spatial invariance of a network layer in a controlled manner; we create a \textit{low-rank} locally connected layer, where the filter bank applied at each position is constructed as a linear combination of basis set of filter banks with spatially varying combining weights. By varying the number of basis filter banks, we can control the degree of relaxation of spatial invariance. In experiments with small convolutional networks, we find that relaxing spatial invariance improves classification accuracy over both convolution and locally connected layers across MNIST, CIFAR-10, and CelebA datasets, thus suggesting that spatial invariance may be an overly restrictive prior.
$M^3$T: Multi-Modal Continuous Valence-Arousal Estimation in the Wild
Authors: Yuan-Hang Zhang, Rulin Huang, Jiabei Zeng, Shiguang Shan, Xilin Chen
Date: 2020-02-07 00:00:00
Tasks: Arousal Estimation, Gesture Recognition

This report describes a multi-modal multi-task ($M^3$T) approach underlying our submission to the valence-arousal estimation track of the Affective Behavior Analysis in-the-wild (ABAW) Challenge, held in conjunction with the IEEE International Conference on Automatic Face and Gesture Recognition (FG) 2020. In the proposed $M^3$T framework, we fuse both visual features from videos and acoustic features from the audio tracks to estimate the valence and arousal. The spatio-temporal visual features are extracted with a 3D convolutional network and a bidirectional recurrent neural network. Considering the correlations between valence / arousal, emotions, and facial actions, we also explores mechanisms to benefit from other tasks. We evaluated the $M^3$T framework on the validation set provided by ABAW and it significantly outperforms the baseline method.
A Multilingual View of Unsupervised Machine Translation
Authors: Xavier Garcia, Pierre Foret, Thibault Sellam, Ankur P. Parikh
Date: 2020-02-07 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Machine Translation, Translation, Unsupervised Machine Translation

We present a probabilistic framework for multilingual neural machine translation that encompasses supervised and unsupervised setups, focusing on unsupervised translation. In addition to studying the vanilla case where there is only monolingual data available, we propose a novel setup where one language in the (source, target) pair is not associated with any parallel data, but there may exist auxiliary parallel data that contains the other. This auxiliary data can naturally be utilized in our probabilistic framework via a novel cross-translation loss term. Empirically, we show that our approach results in higher BLEU scores over state-of-the-art unsupervised models on the WMT'14 English-French, WMT'16 English-German, and WMT'16 English-Romanian datasets in most directions. In particular, we obtain a +1.65 BLEU advantage over the best-performing unsupervised model in the Romanian-English direction.
Logistic Regression Regret: What's the Catch?
Authors: Gil I. Shamir
Date: 2020-02-07 00:00:00
Tasks: regression

We address the problem of the achievable regret rates with online logistic regression. We derive lower bounds with logarithmic regret under $L_1$, $L_2$, and $L_\infty$ constraints on the parameter values. The bounds are dominated by $d/2 \log T$, where $T$ is the horizon and $d$ is the dimensionality of the parameter space. We show their achievability for $d=o(T^{1/3})$ in all these cases with Bayesian methods, that achieve them up to a $d/2 \log d$ term. Interesting different behaviors are shown for larger dimensionality. Specifically, on the negative side, if $d = \Omega(\sqrt{T})$, any algorithm is guaranteed regret of $\Omega(d \log T)$ (greater than $\Omega(\sqrt{T})$) under $L_\infty$ constraints on the parameters (and the example features). On the positive side, under $L_1$ constraints on the parameters, there exist algorithms that can achieve regret that is sub-linear in $d$ for the asymptotically larger values of $d$. For $L_2$ constraints, it is shown that for large enough $d$, the regret remains linear in $d$ but no longer logarithmic in $T$. Adapting the redundancy-capacity theorem from information theory, we demonstrate a principled methodology based on grids of parameters to derive lower bounds. Grids are also utilized to derive some upper bounds. Our results strengthen results by Kakade and Ng (2005) and Foster et al. (2018) for upper bounds for this problem, introduce novel lower bounds, and adapt a methodology that can be used to obtain such bounds for other related problems. They also give a novel characterization of the asymptotic behavior when the dimension of the parameter space is allowed to grow with $T$. They additionally establish connections to the information theory literature, demonstrating that the actual regret for logistic regression depends on the richness of the parameter class, where even within this problem, richer classes lead to greater regret.
Activation Density driven Energy-Efficient Pruning in Training
Authors: Timothy Foldy-Porto, Yeshwanth Venkatesha, Priyadarshini Panda
Date: 2020-02-07 00:00:00
Tasks: Network Pruning

Neural network pruning with suitable retraining can yield networks with considerably fewer parameters than the original with comparable degrees of accuracy. Typical pruning methods require large, fully trained networks as a starting point from which they perform a time-intensive iterative pruning and retraining procedure to regain the original accuracy. We propose a novel pruning method that prunes a network real-time during training, reducing the overall training time to achieve an efficient compressed network. We introduce an activation density based analysis to identify the optimal relative sizing or compression for each layer of the network. Our method is architecture agnostic, allowing it to be employed on a wide variety of systems. For VGG-19 and ResNet18 on CIFAR-10, CIFAR-100, and TinyImageNet, we obtain exceedingly sparse networks (up to $200 \times$ reduction in parameters and over $60 \times$ reduction in inference compute operations in the best case) with accuracy comparable to the baseline network. By reducing the network size periodically during training, we achieve total training times that are shorter than those of previously proposed pruning methods. Furthermore, training compressed networks at different epochs with our proposed method yields considerable reduction in training compute complexity ($1.6\times$ to $3.2\times$ lower) at near iso-accuracy as compared to a baseline network trained entirely from scratch.
A deep-learning view of chemical space designed to facilitate drug discovery
Authors: Paul Maragakis, Hunter Nisonoff, Brian Cole, David E. Shaw
Date: 2020-02-07 00:00:00
Tasks: Design Synthesis, Drug Discovery

Drug discovery projects entail cycles of design, synthesis, and testing that yield a series of chemically related small molecules whose properties, such as binding affinity to a given target protein, are progressively tailored to a particular drug discovery goal. The use of deep learning technologies could augment the typical practice of using human intuition in the design cycle, and thereby expedite drug discovery projects. Here we present DESMILES, a deep neural network model that advances the state of the art in machine learning approaches to molecular design. We applied DESMILES to a previously published benchmark that assesses the ability of a method to modify input molecules to inhibit the dopamine receptor D2, and DESMILES yielded a 77% lower failure rate compared to state-of-the-art models. To explain the ability of DESMILES to hone molecular properties, we visualize a layer of the DESMILES network, and further demonstrate this ability by using DESMILES to tailor the same molecules used in the D2 benchmark test to dock more potently against seven different receptors.
On the Robustness of Face Recognition Algorithms Against Attacks and Bias
Authors: Richa Singh, Akshay Agarwal, Maneet Singh, Shruti Nagpal, Mayank Vatsa
Date: 2020-02-07 00:00:00
Tasks: Face Recognition

Face recognition algorithms have demonstrated very high recognition performance, suggesting suitability for real world applications. Despite the enhanced accuracies, robustness of these algorithms against attacks and bias has been challenged. This paper summarizes different ways in which the robustness of a face recognition algorithm is challenged, which can severely affect its intended working. Different types of attacks such as physical presentation attacks, disguise/makeup, digital adversarial attacks, and morphing/tampering using GANs have been discussed. We also present a discussion on the effect of bias on face recognition models and showcase that factors such as age and gender variations affect the performance of modern algorithms. The paper also presents the potential reasons for these challenges and some of the future research directions for increasing the robustness of face recognition models.
Student/Teacher Advising through Reward Augmentation
Authors: Cameron Reid
Date: 2020-02-07 00:00:00
Tasks: General Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Transfer learning is an important new subfield of multiagent reinforcement learning that aims to help an agent learn about a problem by using knowledge that it has gained solving another problem, or by using knowledge that is communicated to it by an agent who already knows the problem. This is useful when one wishes to change the architecture or learning algorithm of an agent (so that the new knowledge need not be built "from scratch"), when new agents are frequently introduced to the environment with no knowledge, or when an agent must adapt to similar but different problems. Great progress has been made in the agent-to-agent case using the Teacher/Student framework proposed by (Torrey and Taylor 2013). However, that approach requires that learning from a teacher be treated differently from learning in every other reinforcement learning context. In this paper, I propose a method which allows the teacher/student framework to be applied in a way that fits directly and naturally into the more general reinforcement learning framework by integrating the teacher feedback into the reward signal received by the learning agent. I show that this approach can significantly improve the rate of learning for an agent playing a one-player stochastic game; I give examples of potential pitfalls of the approach; and I propose further areas of research building on this framework.