pwc-archive/papers-with-abstracts huggingface dataset:
Generating Object Stamps
Authors: Youssef Alami Mejjati, Zejiang Shen, Michael Snower, Aaron Gokaslan, Oliver Wang, James Tompkin, Kwang In Kim
Date: 2020-01-01 00:00:00
Tasks: Diversity, Object

We present an algorithm to generate diverse foreground objects and composite them into background images using a GAN architecture. Given an object class, a user-provided bounding box, and a background image, we first use a mask generator to create an object shape, and then use a texture generator to fill the mask such that the texture integrates with the background. By separating the problem of object insertion into these two stages, we show that our model allows us to improve the realism of diverse object generation that also agrees with the provided background image. Our results on the challenging COCO dataset show improved overall quality and diversity compared to state-of-the-art object insertion approaches.
Dragonfly Algorithm and its Applications in Applied Science -- Survey
Authors: Chnoor M. Rahman, Tarik A. Rashid
Date: 2019-11-25 00:00:00
Tasks: Survey

One of the most recently developed heuristic optimization algorithms is dragonfly by Mirjalili. Dragonfly algorithm has shown its ability to optimizing different real world problems. It has three variants. In this work, an overview of the algorithm and its variants is presented. Moreover, the hybridization versions of the algorithm are discussed. Furthermore, the results of the applications that utilized dragonfly algorithm in applied science are offered in the following area: Machine Learning, Image Processing, Wireless, and Networking. It is then compared with some other metaheuristic algorithms. In addition, the algorithm is tested on the CEC-C06 2019 benchmark functions. The results prove that the algorithm has great exploration ability and its convergence rate is better than other algorithms in the literature, such as PSO and GA. In general, in this survey the strong and weak points of the algorithm are discussed. Furthermore, some future works that will help in improving the algorithm's weak points are recommended. This study is conducted with the hope of offering beneficial information about dragonfly algorithm to the researchers who want to study the algorithm.
GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms
Authors: Hanqing Zeng, Viktor Prasanna
Date: 2019-12-31 00:00:00
Tasks: CPU, Representation Learning

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art deep learning model for representation learning on graphs. It is challenging to accelerate training of GCNs, due to (1) substantial and irregular data communication to propagate information within the graph, and (2) intensive computation to propagate information along the neural network layers. To address these challenges, we design a novel accelerator for training GCNs on CPU-FPGA heterogeneous systems, by incorporating multiple algorithm-architecture co-optimizations. We first analyze the computation and communication characteristics of various GCN training algorithms, and select a subgraph-based algorithm that is well suited for hardware execution. To optimize the feature propagation within subgraphs, we propose a lightweight pre-processing step based on a graph theoretic approach. Such pre-processing performed on the CPU significantly reduces the memory access requirements and the computation to be performed on the FPGA. To accelerate the weight update in GCN layers, we propose a systolic array based design for efficient parallelization. We integrate the above optimizations into a complete hardware pipeline, and analyze its load-balance and resource utilization by accurate performance modeling. We evaluate our design on a Xilinx Alveo U200 board hosted by a 40-core Xeon server. On three large graphs, we achieve an order of magnitude training speedup with negligible accuracy loss, compared with state-of-the-art implementation on a multi-core platform.
Deep learning reveals hidden interactions in complex systems
Authors: Seungwoong Ha, Hawoong Jeong
Date: 2020-01-03 00:00:00
Tasks: Deep Learning, Graph Attention

Rich phenomena from complex systems have long intrigued researchers, and yet modeling system micro-dynamics and inferring the forms of interaction remain challenging for conventional data-driven approaches, being generally established by human scientists. In this study, we propose AgentNet, a model-free data-driven framework consisting of deep neural networks to reveal and analyze the hidden interactions in complex systems from observed data alone. AgentNet utilizes a graph attention network with novel variable-wise attention to model the interaction between individual agents, and employs various encoders and decoders that can be selectively applied to any desired system. Our model successfully captured a wide variety of simulated complex systems, namely cellular automata (discrete), the Vicsek model (continuous), and active Ornstein--Uhlenbeck particles (non-Markovian) in which, notably, AgentNet's visualized attention values coincided with the true interaction strength and exhibited collective behavior that was absent in the training data. A demonstration with empirical data from a flock of birds showed that AgentNet could identify hidden interaction ranges exhibited by real birds, which cannot be detected by conventional velocity correlation analysis. We expect our framework to open a novel path to investigating complex systems and to provide insight into general process-driven modeling.
Incremental Monoidal Grammars
Authors: Dan Shiebler, Alexis Toumi, Mehrnoosh Sadrzadeh
Date: 2020-01-02 00:00:00
Tasks: BIG-bench Machine Learning, Language Modeling, Language Modelling

In this work we define formal grammars in terms of free monoidal categories, along with a functor from the category of formal grammars to the category of automata. Generalising from the Booleans to arbitrary semirings, we extend our construction to weighted formal grammars and weighted automata. This allows us to link the categorical viewpoint on natural language to the standard machine learning notion of probabilistic language model.
Coverage Path Planning using Path Primitive Sampling and Primitive Coverage Graph for Visual Inspection
Authors: Wei Jing, Di Deng2, Zhe Xiao3, Yong Liu1， Kenji Shimada2
Date: 2019-08-08 00:00:00
Proceeding: 2019年8月8日 2019 8

Abstract— Planning the path to gather the surface information of the target objects is crucial to improve the efficiency
of and reduce the overall cost, for visual inspection applications with Unmanned Aerial Vehicles (UAVs). Coverage Path
Planning (CPP) problem is often formulated for these inspection
applications because of the coverage requirement. Traditionally,
researchers usually plan and optimize the viewpoints to capture
the surface information first, and then optimize the path to
visit the selected viewpoints. In this paper, we propose a novel
planning method to directly sample and plan the inspection path
for a camera-equipped UAV to acquire visual and geometric
information of the target structures as a video stream setting
in complex 3D environment. The proposed planning method
first generates via-points and path primitives around the target
object by using sampling methods based on voxel dilation and
subtraction. A novel Primitive Coverage Graph (PCG) is then
proposed to encode the topological information, flying distances,
and visibility information, with the sampled via-points and
path primitives. Finally graph search is performed to find the
resultant path in the PCG to complete the inspection task with
the coverage requirements. The effectiveness of the proposed
method is demonstrated through simulation and field tests in
this paper.
Collaborative Metric Learning
Authors: Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge Belongie, Deborah Estrin
Date: 2017-04-01 00:00:00
Proceeding: WWW 2017 4
Tasks: Collaborative Filtering, Metric Learning, Recommendation Systems

Metric learning algorithms produce distance metrics that capture the important relationships among data. In this work we study the connection between metric learning and collaborative filtering. We propose Collaborative Metric Learning (CML) which learns a joint metric space to encode not only users’ preferences but also the user-user and item-item similarity. The proposed algorithm outperforms state-of-the-art collaborative filtering algorithms on a wide range of recommendation tasks and uncovers the underlying spectrum of users’ fine-grained preferences. CML also achieves significant speedup for Top-K recommendation tasks using off-the-shelf, approximate nearest-neighbor search, with negligible accuracy reduction.
Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
Authors: Krishna Kumar Singh, Dhruv Mahajan, Kristen Grauman, Yong Jae Lee, Matt Feiszli, Deepti Ghadiyaram
Date: 2020-01-09 00:00:00
Proceeding: CVPR 2020 6
Tasks: Attribute

Existing models often leverage co-occurrences between objects and their context to improve recognition accuracy. However, strongly relying on context risks a model's generalizability, especially when typical co-occurrence patterns are absent. This work focuses on addressing such contextual biases to improve the robustness of the learnt feature representations. Our goal is to accurately recognize a category in the absence of its context, without compromising on performance when it co-occurs with context. Our key idea is to decorrelate feature representations of a category from its co-occurring context. We achieve this by learning a feature subspace that explicitly represents categories occurring in the absence of context along side a joint feature subspace that represents both categories and context. Our very simple yet effective method is extensible to two multi-label tasks -- object and attribute classification. On 4 challenging datasets, we demonstrate the effectiveness of our method in reducing contextual bias.
Regularity and stability of feedback relaxed controls
Authors: Christoph Reisinger, Yufei Zhang
Date: 2020-01-09 00:00:00
Tasks: Decision Making

This paper proposes a relaxed control regularization with general exploration rewards to design robust feedback controls for multi-dimensional continuous-time stochastic exit time problems. We establish that the regularized control problem admits a H\"{o}lder continuous feedback control, and demonstrate that both the value function and the feedback control of the regularized control problem are Lipschitz stable with respect to parameter perturbations. Moreover, we show that a pre-computed feedback relaxed control has a robust performance in a perturbed system, and derive a first-order sensitivity equation for both the value function and optimal feedback relaxed control. These stability results provide a theoretical justification for recent reinforcement learning heuristics that including an exploration reward in the optimization objective leads to more robust decision making. We finally prove first-order monotone convergence of the value functions for relaxed control problems with vanishing exploration parameters, which subsequently enables us to construct the pure exploitation strategy of the original control problem based on the feedback relaxed controls.
Offensive Language Detection: A Comparative Analysis
Authors: Vyshnav M T, Sachin Kumar S, Soman K. P
Date: 2020-01-09 00:00:00
Tasks: Sentence

Offensive behaviour has become pervasive in the Internet community. Individuals take the advantage of anonymity in the cyber world and indulge in offensive communications which they may not consider in the real life. Governments, online communities, companies etc are investing into prevention of offensive behaviour content in social media. One of the most effective solution for tacking this enigmatic problem is the use of computational techniques to identify offensive content and take action. The current work focuses on detecting offensive language in English tweets. The dataset used for the experiment is obtained from SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval). The dataset contains 14,460 annotated English tweets. The present paper provides a comparative analysis and Random kitchen sink (RKS) based approach for offensive language detection. We explore the effectiveness of Google sentence encoder, Fasttext, Dynamic mode decomposition (DMD) based features and Random kitchen sink (RKS) method for offensive language detection. From the experiments and evaluation we observed that RKS with fastetxt achieved competing results. The evaluation measures used are accuracy, precision, recall, f1-score.
The Counterfactual $χ$-GAN
Authors: Amelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath, Adler J. Perotte
Date: 2020-01-09 00:00:00
Tasks: Causal Inference, counterfactual, Generative Adversarial Network

Causal inference often relies on the counterfactual framework, which requires that treatment assignment is independent of the outcome, known as strong ignorability. Approaches to enforcing strong ignorability in causal analyses of observational data include weighting and matching methods. Effect estimates, such as the average treatment effect (ATE), are then estimated as expectations under the reweighted or matched distribution, P . The choice of P is important and can impact the interpretation of the effect estimate and the variance of effect estimates. In this work, instead of specifying P, we learn a distribution that simultaneously maximizes coverage and minimizes variance of ATE estimates. In order to learn this distribution, this research proposes a generative adversarial network (GAN)-based model called the Counterfactual $\chi$-GAN (cGAN), which also learns feature-balancing weights and supports unbiased causal estimation in the absence of unobserved confounding. Our model minimizes the Pearson $\chi^2$ divergence, which we show simultaneously maximizes coverage and minimizes the variance of importance sampling estimates. To our knowledge, this is the first such application of the Pearson $\chi^2$ divergence. We demonstrate the effectiveness of cGAN in achieving feature balance relative to established weighting methods in simulation and with real-world medical data.
Feedback Capacity and a Variant of the Kalman Filter with ARMA Gaussian Noises: Explicit Bounds and Feedback Coding Design
Authors: Song Fang, Quanyan Zhu
Date: 2020-01-09 00:00:00

In this paper, we relate a feedback channel with any finite-order autoregressive moving-average (ARMA) Gaussian noises to a variant of the Kalman filter. In light of this, we obtain relatively explicit lower bounds on the feedback capacity for such colored Gaussian noises, and the bounds are seen to be consistent with various existing results in the literature. Meanwhile, this variant of the Kalman filter also leads to explicit recursive coding schemes with clear structures to achieve the lower bounds. In general, our results provide an alternative perspective while pointing to potentially tighter bounds for the feedback capacity problem.
Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality Reduction
Authors: Zhenhua Shi, Dongrui Wu, Jian Huang, Yu-Kai Wang, Chin-Teng Lin
Date: 2020-01-09 00:00:00
Tasks: Clustering, Dimensionality Reduction, General Classification, Supervised dimensionality reduction

Dimensionality reduction is an important operation in information visualization, feature extraction, clustering, regression, and classification, especially for processing noisy high dimensional data. However, most existing approaches preserve either the global or the local structure of the data, but not both. Approaches that preserve only the global data structure, such as principal component analysis (PCA), are usually sensitive to outliers. Approaches that preserve only the local data structure, such as locality preserving projections, are usually unsupervised (and hence cannot use label information) and uses a fixed similarity graph. We propose a novel linear dimensionality reduction approach, supervised discriminative sparse PCA with adaptive neighbors (SDSPCAAN), to integrate neighborhood-free supervised discriminative sparse PCA and projected clustering with adaptive neighbors. As a result, both global and local data structures, as well as the label information, are used for better dimensionality reduction. Classification experiments on nine high-dimensional datasets validated the effectiveness and robustness of our proposed SDSPCAAN.
Compression of descriptor models for mobile applications
Authors: Roy Miles, Krystian Mikolajczyk
Date: 2020-01-09 00:00:00

Deep neural networks have demonstrated state-of-the-art performance for feature-based image matching through the advent of new large and diverse datasets. However, there has been little work on evaluating the computational cost, model size, and matching accuracy tradeoffs for these models. This paper explicitly addresses these practical metrics by considering the state-of-the-art HardNet model. We observe a significant redundancy in the learned weights, which we exploit through the use of depthwise separable layers and an efficient Tucker decomposition. We demonstrate that a combination of these methods is very effective, but still sacrifices the top-end accuracy. To resolve this, we propose the Convolution-Depthwise-Pointwise(CDP) layer, which provides a means of interpolating between the standard and depthwise separable convolutions. With this proposed layer, we can achieve an 8 times reduction in the number of parameters on the HardNet model, 13 times reduction in the computational complexity, while sacrificing less than 1% on the overall accuracy across theHPatchesbenchmarks. To further demonstrate the generalisation of this approach, we apply it to the state-of-the-art SuperPoint model, where we can significantly reduce the number of parameters and floating-point operations, with minimal degradation in the matching accuracy.
Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data
Authors: Tim Salzmann, Boris Ivanovic, Punarjay Chakravarty, Marco Pavone
Date: 2020-01-09 00:00:00
Proceeding: ECCV 2020 8
Tasks: Motion Forecasting, Self-Driving Cars, Trajectory Forecasting, Trajectory Prediction

Reasoning about human motion is an important prerequisite to safe and socially-aware robotic navigation. As a result, multi-agent behavior prediction has become a core component of modern human-robot interactive systems, such as self-driving cars. While there exist many methods for trajectory forecasting, most do not enforce dynamic constraints and do not account for environmental information (e.g., maps). Towards this end, we present Trajectron++, a modular, graph-structured recurrent model that forecasts the trajectories of a general number of diverse agents while incorporating agent dynamics and heterogeneous data (e.g., semantic maps). Trajectron++ is designed to be tightly integrated with robotic planning and control frameworks; for example, it can produce predictions that are optionally conditioned on ego-agent motion plans. We demonstrate its performance on several challenging real-world trajectory forecasting datasets, outperforming a wide array of state-of-the-art deterministic and generative methods.
Sampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach
Authors: Serena Booth, Ankit Shah, Yilun Zhou, Julie Shah
Date: 2020-01-09 00:00:00
Tasks: General Classification, Prediction, Probabilistic Programming

Though neural network models demonstrate impressive performance, we do not understand exactly how these black-box models make individual predictions. This drawback has led to substantial research devoted to understand these models in areas such as robustness, interpretability, and generalization ability. In this paper, we consider the problem of exploring the prediction level sets of a classifier using probabilistic programming. We define a prediction level set to be the set of examples for which the predictor has the same specified prediction confidence with respect to some arbitrary data distribution. Notably, our sampling-based method does not require the classifier to be differentiable, making it compatible with arbitrary classifiers. As a specific instantiation, if we take the classifier to be a neural network and the data distribution to be that of the training data, we can obtain examples that will result in specified predictions by the neural network. We demonstrate this technique with experiments on a synthetic dataset and MNIST. Such level sets in classification may facilitate human understanding of classification behaviors.
Investigating the Impact of Inclusion in Face Recognition Training Data on Individual Face Identification
Authors: Chris Dulhanty, Alexander Wong
Date: 2020-01-09 00:00:00
Tasks: Face Identification, Face Recognition, Face Verification

Modern face recognition systems leverage datasets containing images of hundreds of thousands of specific individuals' faces to train deep convolutional neural networks to learn an embedding space that maps an arbitrary individual's face to a vector representation of their identity. The performance of a face recognition system in face verification (1:1) and face identification (1:N) tasks is directly related to the ability of an embedding space to discriminate between identities. Recently, there has been significant public scrutiny into the source and privacy implications of large-scale face recognition training datasets such as MS-Celeb-1M and MegaFace, as many people are uncomfortable with their face being used to train dual-use technologies that can enable mass surveillance. However, the impact of an individual's inclusion in training data on a derived system's ability to recognize them has not previously been studied. In this work, we audit ArcFace, a state-of-the-art, open source face recognition system, in a large-scale face identification experiment with more than one million distractor images. We find a Rank-1 face identification accuracy of 79.71% for individuals present in the model's training data and an accuracy of 75.73% for those not present. This modest difference in accuracy demonstrates that face recognition systems using deep learning work better for individuals they are trained on, which has serious privacy implications when one considers all major open source face recognition training datasets do not obtain informed consent from individuals during their collection.
Domain-independent Extraction of Scientific Concepts from Research Articles
Authors: Arthur Brack, Jennifer D'Souza, Anett Hoppe, Sören Auer, Ralph Ewerth
Date: 2020-01-09 00:00:00
Proceeding: Accepted for publishing in 42nd European Conference on IR Research, ECIR 2020 2020 1
Tasks: Active Learning, Articles, Named Entity Recognition (NER), Scientific Concept Extraction

We examine the novel task of domain-independent scientific concept extraction from abstracts of scholarly articles and present two contributions. First, we suggest a set of generic scientific concepts that have been identified in a systematic annotation process. This set of concepts is utilised to annotate a corpus of scientific abstracts from 10 domains of Science, Technology and Medicine at the phrasal level in a joint effort with domain experts. The resulting dataset is used in a set of benchmark experiments to (a) provide baseline performance for this task, (b) examine the transferability of concepts between domains. Second, we present two deep learning systems as baselines. In particular, we propose active learning to deal with different domains in our task. The experimental results show that (1) a substantial agreement is achievable by non-experts after consultation with domain experts, (2) the baseline system achieves a fairly high F1 score, (3) active learning enables us to nearly halve the amount of required training data.