pwc-archive/papers-with-abstracts huggingface dataset:
Same-Cluster Querying for Overlapping Clusters
Authors: Wasim Huleihel, Arya Mazumdar, Muriel Médard, Soumyabrata Pal
Date: 2019-10-28 00:00:00
Proceeding: NeurIPS 2019 12

Overlapping clusters are common in models of many practical data-segmentation applications. Suppose we are given $n$ elements to be clustered into $k$ possibly overlapping clusters, and an oracle that can interactively answer queries of the form "do elements $u$ and $v$ belong to the same cluster?" The goal is to recover the clusters with minimum number of such queries. This problem has been of recent interest for the case of disjoint clusters. In this paper, we look at the more practical scenario of overlapping clusters, and provide upper bounds (with algorithms) on the sufficient number of queries. We provide algorithmic results under both arbitrary (worst-case) and statistical modeling assumptions. Our algorithms are parameter free, efficient, and work in the presence of random noise. We also derive information-theoretic lower bounds on the number of queries needed, proving that our algorithms are order optimal. Finally, we test our algorithms over both synthetic and real-world data, showing their practicality and effectiveness.
Generative Well-intentioned Networks
Authors: Justin Cosentino, Jun Zhu
Date: 2019-10-28 00:00:00
Proceeding: NeurIPS 2019 12

We propose Generative Well-intentioned Networks (GWINs), a novel framework for increasing the accuracy of certainty-based, closed-world classifiers. A conditional generative network recovers the distribution of observations that the classifier labels correctly with high certainty. We introduce a reject option to the classifier during inference, allowing the classifier to reject an observation instance rather than predict an uncertain label. These rejected observations are translated by the generative network to high-certainty representations, which are then relabeled by the classifier. This architecture allows for any certainty-based classifier or rejection function and is not limited to multilayer perceptrons. The capability of this framework is assessed using benchmark classification datasets and shows that GWINs significantly improve the accuracy of uncertain observations.
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes
Authors: Greg Yang
Date: 2019-10-28 00:00:00
Tasks: Gaussian Processes

Wide neural networks with random weights and biases are Gaussian processes, as originally observed by Neal (1995) and more recently by Lee et al. (2018) and Matthews et al. (2018) for deep fully-connected networks, as well as by Novak et al. (2019) and Garriga-Alonso et al. (2019) for deep convolutional networks. We show that this Neural Network-Gaussian Process correspondence surprisingly extends to all modern feedforward or recurrent neural networks composed of multilayer perceptron, RNNs (e.g. LSTMs, GRUs), (nD or graph) convolution, pooling, skip connection, attention, batch normalization, and/or layer normalization. More generally, we introduce a language for expressing neural network computations, and our result encompasses all such expressible neural networks. This work serves as a tutorial on the *tensor programs* technique formulated in Yang (2019) and elucidates the Gaussian Process results obtained there. We provide open-source implementations of the Gaussian Process kernels of simple RNN, GRU, transformer, and batchnorm+ReLU network at github.com/thegregyang/GP4A.
Multi-Module System for Open Domain Chinese Question Answering over Knowledge Base
Authors: Yiying Yang, Xiahui He, Kaijie Zhou, Zhongyu Wei
Date: 2019-10-28 00:00:00
Tasks: Information Retrieval, Question Answering, Relation, Retrieval, Semantic Parsing

For the task of open domain Knowledge Based Question Answering in CCKS2019, we propose a method combining information retrieval and semantic parsing. This multi-module system extracts the topic entity and the most related relation predicate from a question and transforms it into a Sparql query statement. Our method obtained the F1 score of 70.45% on the test data.
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling
Authors: Qitian Wu, Zixuan Zhang, Xiaofeng Gao, Junchi Yan, Guihai Chen
Date: 2019-10-28 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Imitation Learning, Relation Network, Vocal Bursts Intensity Prediction

We target modeling latent dynamics in high-dimension marked event sequences without any prior knowledge about marker relations. Such problem has been rarely studied by previous works which would have fundamental difficulty to handle the arisen challenges: 1) the high-dimensional markers and unknown relation network among them pose intractable obstacles for modeling the latent dynamic process; 2) one observed event sequence may concurrently contain several different chains of interdependent events; 3) it is hard to well define the distance between two high-dimension event sequences. To these ends, in this paper, we propose a seminal adversarial imitation learning framework for high-dimension event sequence generation which could be decomposed into: 1) a latent structural intensity model that estimates the adjacent nodes without explicit networks and learns to capture the temporal dynamics in the latent space of markers over observed sequence; 2) an efficient random walk based generation model that aims at imitating the generation process of high-dimension event sequences from a bottom-up view; 3) a discriminator specified as a seq2seq network optimizing the rewards to help the generator output event sequences as real as possible. Experimental results on both synthetic and real-world datasets demonstrate that the proposed method could effectively detect the hidden network among markers and make decent prediction for future marked events, even when the number of markers scales to million level.
Image-Based Place Recognition on Bucolic Environment Across Seasons From Semantic Edge Description
Authors: Assia Benbihi, Stéphanie Aravecchia, Matthieu Geist, Cédric Pradalier
Date: 2019-10-28 00:00:00
Tasks: Image Retrieval, Retrieval

Most of the research effort on image-based place recognition is designed for urban environments. In bucolic environments such as natural scenes with low texture and little semantic content, the main challenge is to handle the variations in visual appearance across time such as illumination, weather, vegetation state or viewpoints. The nature of the variations is different and this leads to a different approach to describing a bucolic scene. We introduce a global image descriptor computed from its semantic and topological information. It is built from the wavelet transforms of the image semantic edges. Matching two images is then equivalent to matching their semantic edge descriptors. We show that this method reaches state-of-the-art image retrieval performance on two multi-season environment-monitoring datasets: the CMU-Seasons and the Symphony Lake dataset. It also generalises to urban scenes on which it is on par with the current baselines NetVLAD and DELF.
Use of a Capsule Network to Detect Fake Images and Videos
Authors: Huy H. Nguyen, Junichi Yamagishi, Isao Echizen
Date: 2019-10-28 00:00:00
Tasks: Image and Video Forgery Detection

The revolution in computer hardware, especially in graphics processing units and tensor processing units, has enabled significant advances in computer graphics and artificial intelligence algorithms. In addition to their many beneficial applications in daily life and business, computer-generated/manipulated images and videos can be used for malicious purposes that violate security systems, privacy, and social trust. The deepfake phenomenon and its variations enable a normal user to use his or her personal computer to easily create fake videos of anybody from a short real online video. Several countermeasures have been introduced to deal with attacks using such videos. However, most of them are targeted at certain domains and are ineffective when applied to other domains or new attacks. In this paper, we introduce a capsule network that can detect various kinds of attacks, from presentation attacks using printed images and replayed videos to attacks using fake videos created using deep learning. It uses many fewer parameters than traditional convolutional neural networks with similar performance. Moreover, we explain, for the first time ever in the literature, the theory behind the application of capsule networks to the forensics problem through detailed analysis and visualization.
Biomimetic Ultra-Broadband Perfect Absorbers Optimised with Reinforcement Learning
Authors: Trevon Badloe, Inki Kim, Junsuk Rho
Date: 2019-10-28 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

By learning the optimal policy with a double deep Q-learning network, we design ultra-broadband, biomimetic, perfect absorbers with various materials, based the structure of a moths eye. All absorbers achieve over 90% average absorption from 400 to 1,600 nm. By training a DDQN with motheye structures made up of chromium, we transfer the learned knowledge to other, similar materials to quickly and efficiently find the optimal parameters from the around 1 billion possible options. The knowledge learned from previous optimisations helps the network to find the best solution for a new material in fewer steps, dramatically increasing the efficiency of finding designs with ultra-broadband absorption.
Fine-Grained Object Detection over Scientific Document Images with Region Embeddings
Authors: Ankur Goswami, Joshua McGrath, Shanan Peters, Theodoros Rekatsinas
Date: 2019-10-28 00:00:00
Tasks: Object, object-detection, Object Detection

We study the problem of object detection over scanned images of scientific documents. We consider images that contain objects of varying aspect ratios and sizes and range from coarse elements such as tables and figures to fine elements such as equations and section headers. We find that current object detectors fail to produce properly localized region proposals over such page objects. We revisit the original R-CNN model and present a method for generating fine-grained proposals over document elements. We also present a region embedding model that uses the convolutional maps of a proposal's neighbors as context to produce an embedding for each proposal. This region embedding is able to capture the semantic relationships between a target region and its surrounding context. Our end-to-end model produces an embedding for each proposal, then classifies each proposal by using a multi-head attention model that attends to the most important neighbors of a proposal. To evaluate our model, we collect and annotate a dataset of publications from heterogeneous journals. We show that our model, referred to as Attentive-RCNN, yields a 17% mAP improvement compared to standard object detection models.
Applications of Generative Adversarial Models in Visual Search Reformulation
Authors: Kyle Xiao, Houdong Hu, Yan Wang
Date: 2019-10-28 00:00:00

Query reformulation is the process by which a input search query is refined by the user to match documents outside the original top-n results. On average, roughly 50% of text search queries involve some form of reformulation, and term suggestion tools are used 35% of the time when offered to users. As prevalent as text search queries are, however, such a feature has yet to be explored at scale for visual search. This is because reformulation for images presents a novel challenge to seamlessly transform visual features to match user intent within the context of a typical user session. In this paper, we present methods of semantically transforming visual queries, such as utilizing operations in the latent space of a generative adversarial model for the scenarios of fashion and product search.
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems
Authors: Alexander Potapov, Ian Colbert, Ken Kreutz-Delgado, Alexander Cloninger, Srinjoy Das
Date: 2019-10-28 00:00:00
Tasks: Denoising, Model Selection, Motion Synthesis

Stochastic-sampling-based Generative Neural Networks, such as Restricted Boltzmann Machines and Generative Adversarial Networks, are now used for applications such as denoising, image occlusion removal, pattern completion, and motion synthesis. In scenarios which involve performing such inference tasks with these models, it is critical to determine metrics that allow for model selection and/or maintenance of requisite generative performance under pre-specified implementation constraints. In this paper, we propose a new metric for evaluating generative model performance based on $p$-values derived from the combined use of Maximum Mean Discrepancy (MMD) and permutation-based (PT-based) resampling, which we refer to as PT-MMD. We demonstrate the effectiveness of this metric for two cases: (1) Selection of bitwidth and activation function complexity to achieve minimum power-at-performance for Restricted Boltzmann Machines; (2) Quantitative comparison of images generated by two types of Generative Adversarial Networks (PGAN and WGAN) to facilitate model selection in order to maximize the fidelity of generated images. For these applications, our results are shown using Euclidean and Haar-based kernels for the PT-MMD two sample hypothesis test. This demonstrates the critical role of distance functions in comparing generated images against their corresponding ground truth counterparts as what would be perceived by human users.
Asynchronous Methods for Model-Based Reinforcement Learning
Authors: Yunzhi Zhang, Ignasi Clavera, Boren Tsai, Pieter Abbeel
Date: 2019-10-28 00:00:00
Tasks: model, Model-based Reinforcement Learning, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Significant progress has been made in the area of model-based reinforcement learning. State-of-the-art algorithms are now able to match the asymptotic performance of model-free methods while being significantly more data efficient. However, this success has come at a price: state-of-the-art model-based methods require significant computation interleaved with data collection, resulting in run times that take days, even if the amount of agent interaction might be just hours or even minutes. When considering the goal of learning in real-time on real robots, this means these state-of-the-art model-based algorithms still remain impractical. In this work, we propose an asynchronous framework for model-based reinforcement learning methods that brings down the run time of these algorithms to be just the data collection time. We evaluate our asynchronous framework on a range of standard MuJoCo benchmarks. We also evaluate our asynchronous framework on three real-world robotic manipulation tasks. We show how asynchronous learning not only speeds up learning w.r.t wall-clock time through parallelization, but also further reduces the sample complexity of model-based approaches by means of improving the exploration and by means of effectively avoiding the policy overfitting to the deficiencies of learned dynamics models.
Efficient Regret Minimization Algorithm for Extensive-Form Correlated Equilibrium
Authors: Gabriele Farina, Chun Kai Ling, Fei Fang, Tuomas Sandholm
Date: 2019-10-28 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Form

Self-play methods based on regret minimization have become the state of the art for computing Nash equilibria in large two-players zero-sum extensive-form games. These methods fundamentally rely on the hierarchical structure of the players' sequential strategy spaces to construct a regret minimizer that recursively minimizes regret at each decision point in the game tree. In this paper, we introduce the first efficient regret minimization algorithm for computing extensive-form correlated equilibria in large two-player general-sum games with no chance moves. Designing such an algorithm is significantly more challenging than designing one for the Nash equilibrium counterpart, as the constraints that define the space of correlation plans lack the hierarchical structure and might even form cycles. We show that some of the constraints are redundant and can be excluded from consideration, and present an efficient algorithm that generates the space of extensive-form correlation plans incrementally from the remaining constraints. This structural decomposition is achieved via a special convexity-preserving operation that we coin scaled extension. We show that a regret minimizer can be designed for a scaled extension of any two convex sets, and that from the decomposition we then obtain a global regret minimizer. Our algorithm produces feasible iterates. Experiments show that it significantly outperforms prior approaches and for larger problems it is the only viable option.
Towards Successful Social Media Advertising: Predicting the Influence of Commercial Tweets
Authors: Renhao Cui, Gagan Agrawal, Rajiv Ramnath
Date: 2019-10-28 00:00:00

Businesses communicate using Twitter for a variety of reasons -- to raise awareness of their brands, to market new products, to respond to community comments, and to connect with their customers and potential customers in a targeted manner. For businesses to do this effectively, they need to understand which content and structural elements about a tweet make it influential, that is, widely liked, followed, and retweeted. This paper presents a systematic methodology for analyzing commercial tweets, and predicting the influence on their readers. Our model, which use a combination of decoration and meta features, outperforms the prediction ability of the baseline model as well as the tweet embedding model. Further, in order to demonstrate a practical use of this work, we show how an unsuccessful tweet may be engineered (for example, reworded) to increase its potential for success.
Online News Media Website Ranking Using User Generated Content
Authors: Samaneh Karimi, Azadeh Shakery, Rakesh Verma
Date: 2019-10-28 00:00:00
Tasks: Diversity, News Recommendation, News Retrieval, Retrieval

News media websites are important online resources that have drawn great attention of text mining researchers. The main aim of this study is to propose a framework for ranking online news websites from different viewpoints. The ranking of news websites is useful information, which can benefit many news-related tasks such as news retrieval and news recommendation. In the proposed framework, the ranking of news websites is obtained by calculating three measures introduced in the paper and based on user-generated content. Each proposed measure is concerned with the performance of news websites from a particular viewpoint including the completeness of news reports, the diversity of events being covered by the website and its speed. The use of user-generated content in this framework, as a partly-unbiased, real-time and low cost content on the web distinguishes the proposed news website ranking framework from the literature. The results obtained for three prominent news websites, BBC, CNN, NYTimes, show that BBC has the best performance in terms of news completeness and speed, and NYTimes has the best diversity in comparison with the other two websites.
Secure Evaluation of Quantized Neural Networks
Authors: Anders Dalskov, Daniel Escudero, Marcel Keller
Date: 2019-10-28 00:00:00
Tasks: Image Classification, Quantization

We investigate two questions in this paper: First, we ask to what extent "MPC friendly" models are already supported by major Machine Learning frameworks such as TensorFlow or PyTorch. Prior works provide protocols that only work on fixed-point integers and specialized activation functions, two aspects that are not supported by popular Machine Learning frameworks, and the need for these specialized model representations means that it is hard, and often impossible, to use e.g., TensorFlow to design, train and test models that later have to be evaluated securely. Second, we ask to what extent the functionality for evaluating Neural Networks already exists in general-purpose MPC frameworks. These frameworks have received more scrutiny, are better documented and supported on more platforms. Furthermore, they are typically flexible in terms of the threat model they support. In contrast, most secure evaluation protocols in the literature are targeted to a specific threat model and their implementations are only a "proof-of-concept", making it very hard for their adoption in practice. We answer both of the above questions in a positive way: We observe that the quantization techniques supported by both TensorFlow, PyTorch and MXNet can provide models in a representation that can be evaluated securely; and moreover, that this evaluation can be performed by a general purpose MPC framework. We perform extensive benchmarks to understand the exact trade-offs between different corruption models, network sizes and efficiency. These experiments provide an interesting insight into cost between active and passive security, as well as honest and dishonest majority. Our work shows then that the separating line between existing ML frameworks and existing MPC protocols may be narrower than implicitly suggested by previous works.
Differentiable Convex Optimization Layers
Authors: Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, Zico Kolter
Date: 2019-10-28 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Inductive Bias

Recent work has shown how to embed differentiable optimization problems (that is, problems whose solutions can be backpropagated through) as layers within deep learning architectures. This method provides a useful inductive bias for certain problems, but existing software for differentiable optimization layers is rigid and difficult to apply to new settings. In this paper, we propose an approach to differentiating through disciplined convex programs, a subclass of convex optimization problems used by domain-specific languages (DSLs) for convex optimization. We introduce disciplined parametrized programming, a subset of disciplined convex programming, and we show that every disciplined parametrized program can be represented as the composition of an affine map from parameters to problem data, a solver, and an affine map from the solver's solution to a solution of the original problem (a new form we refer to as affine-solver-affine form). We then demonstrate how to efficiently differentiate through each of these components, allowing for end-to-end analytical differentiation through the entire convex program. We implement our methodology in version 1.1 of CVXPY, a popular Python-embedded DSL for convex optimization, and additionally implement differentiable layers for disciplined convex programs in PyTorch and TensorFlow 2.0. Our implementation significantly lowers the barrier to using convex optimization problems in differentiable programs. We present applications in linear machine learning models and in stochastic control, and we show that our layer is competitive (in execution time) compared to specialized differentiable solvers from past work.