pwc-archive/papers-with-abstracts huggingface dataset:
Train No Evil: Selective Masking for Task-Guided Pre-Training
Authors: Yuxian Gu, Zhengyan Zhang, Xiaozhi Wang, Zhiyuan Liu, Maosong Sun
Date: 2020-04-21 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Language Modeling, Language Modelling, Masked Language Modeling, Sentiment Analysis

Recently, pre-trained language models mostly follow the pre-train-then-fine-tuning paradigm and have achieved great performance on various downstream tasks. However, since the pre-training stage is typically task-agnostic and the fine-tuning stage usually suffers from insufficient supervised data, the models cannot always well capture the domain-specific and task-specific patterns. In this paper, we propose a three-stage framework by adding a task-guided pre-training stage with selective masking between general pre-training and fine-tuning. In this stage, the model is trained by masked language modeling on in-domain unsupervised data to learn domain-specific patterns and we propose a novel selective masking strategy to learn task-specific patterns. Specifically, we design a method to measure the importance of each token in sequences and selectively mask the important tokens. Experimental results on two sentiment analysis tasks show that our method can achieve comparable or even better performance with less than 50% of computation cost, which indicates our method is both effective and efficient. The source code of this paper can be obtained from https://github.com/thunlp/SelectiveMasking.
Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness
Authors: Zheng Zhang, Lizi Liao, Xiaoyan Zhu, Tat-Seng Chua, Zitao Liu, Yan Huang, Minlie Huang
Date: 2020-04-21 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Decision Making, Reinforcement Learning

Most existing approaches for goal-oriented dialogue policy learning used reinforcement learning, which focuses on the target agent policy and simply treat the opposite agent policy as part of the environment. While in real-world scenarios, the behavior of an opposite agent often exhibits certain patterns or underlies hidden policies, which can be inferred and utilized by the target agent to facilitate its own decision making. This strategy is common in human mental simulation by first imaging a specific action and the probable results before really acting it. We therefore propose an opposite behavior aware framework for policy learning in goal-oriented dialogues. We estimate the opposite agent's policy from its behavior and use this estimation to improve the target agent by regarding it as part of the target policy. We evaluate our model on both cooperative and competitive dialogue tasks, showing superior performance over state-of-the-art baselines.
TrueBranch: Metric Learning-based Verification of Forest Conservation Projects
Authors: Simona Santamaria, David Dao, Björn Lütjens, Ce Zhang
Date: 2020-04-21 00:00:00
Tasks: Metric Learning

International stakeholders increasingly invest in offsetting carbon emissions, for example, via issuing Payments for Ecosystem Services (PES) to forest conservation projects. Issuing trusted payments requires a transparent monitoring, reporting, and verification (MRV) process of the ecosystem services (e.g., carbon stored in forests). The current MRV process, however, is either too expensive (on-ground inspection of forest) or inaccurate (satellite). Recent works propose low-cost and accurate MRV via automatically determining forest carbon from drone imagery, collected by the landowners. The automation of MRV, however, opens up the possibility that landowners report untruthful drone imagery. To be robust against untruthful reporting, we propose TrueBranch, a metric learning-based algorithm that verifies the truthfulness of drone imagery from forest conservation projects. TrueBranch aims to detect untruthfully reported drone imagery by matching it with public satellite imagery. Preliminary results suggest that nominal distance metrics are not sufficient to reliably detect untruthfully reported imagery. TrueBranch leverages metric learning to create a feature embedding in which truthfully and untruthfully collected imagery is easily distinguishable by distance thresholding.
An Early Warning Sign of Critical Transition in The Antarctic Ice Sheet -- A Data Driven Tool for Spatiotemporal Tipping Point
Authors: Abd AlRahman AlMomani, Erik Bollt
Date: 2020-04-21 00:00:00
Tasks: Benchmarking, Clustering

Our recently developed tool, called Directed Affinity Segmentation was originally designed for data-driven discovery of coherent sets in fluidic systems. Here we interpret that it can also be used to indicate early warning signs of critical transitions in ice shelves as seen from remote sensing data. We apply a directed spectral clustering methodology, including an asymmetric affinity matrix and the associated directed graph Laplacian, to reprocess the ice velocity data and remote sensing satellite images of the Larsen C ice shelf. Our tool has enabled the simulated prediction of historical events from historical data, fault lines responsible for the critical transitions leading to the break up of the Larsen C ice shelf crack, which resulted in the A68 iceberg. Such benchmarking of methods using data from the past to forecast events that are now also in the past is sometimes called post-casting, analogous to forecasting into the future. Our method indicated the coming crisis months before the actual occurrence.
M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network
Authors: Baichuan Huang, Hongwei Yi, Can Huang, Yijia He, Jingbin Liu, Xiao Liu
Date: 2020-04-21 00:00:00
Tasks: Point cloud reconstruction

The present Multi-view stereo (MVS) methods with supervised learning-based networks have an impressive performance comparing with traditional MVS methods. However, the ground-truth depth maps for training are hard to be obtained and are within limited kinds of scenarios. In this paper, we propose a novel unsupervised multi-metric MVS network, named M^3VSNet, for dense point cloud reconstruction without any supervision. To improve the robustness and completeness of point cloud reconstruction, we propose a novel multi-metric loss function that combines pixel-wise and feature-wise loss function to learn the inherent constraints from different perspectives of matching correspondences. Besides, we also incorporate the normal-depth consistency in the 3D point cloud format to improve the accuracy and continuity of the estimated depth maps. Experimental results show that M3VSNet establishes the state-of-the-arts unsupervised method and achieves comparable performance with previous supervised MVSNet on the DTU dataset and demonstrates the powerful generalization ability on the Tanks and Temples benchmark with effective improvement. Our code is available at https://github.com/whubaichuan/M3VSNet.
Quarantine Deceiving Yelp's Users by Detecting Unreliable Rating Reviews
Authors: Viet Trinh, Vikrant More, Samira Zare, Sheideh Homayon
Date: 2020-04-21 00:00:00
Tasks: Decision Making, Spam detection

Online reviews have become a valuable and significant resource, for not only consumers but companies, in decision making. In the absence of a trusted system, highly popular and trustworthy internet users will be assumed as members of the trusted circle. In this paper, we describe our focus on quarantining deceiving Yelp's users that employ both review spike detection (RSD) algorithm and spam detection technique in bridging review networks (BRN), on extracted key features. We found that more than 80% of Yelp's accounts are unreliable, and more than 80% of highly-rated businesses are subject to spamming.
Word Embedding-based Text Processing for Comprehensive Summarization and Distinct Information Extraction
Authors: Xiangpeng Wan, Hakim Ghazzai, Yehia Massoud
Date: 2020-04-21 00:00:00
Tasks: Clustering, Community Detection, Question Answering, Sentence, TAG

In this paper, we propose two automated text processing frameworks specifically designed to analyze online reviews. The objective of the first framework is to summarize the reviews dataset by extracting essential sentence. This is performed by converting sentences into numerical vectors and clustering them using a community detection algorithm based on their similarity levels. Afterwards, a correlation score is measured for each sentence to determine its importance level in each cluster and assign it as a tag for that community. The second framework is based on a question-answering neural network model trained to extract answers to multiple different questions. The collected answers are effectively clustered to find multiple distinct answers to a single question that might be asked by a customer. The proposed frameworks are shown to be more comprehensive than existing reviews processing solutions.
Automatic Tag Recommendation for Painting Artworks Using Diachronic Descriptions
Authors: Gianlucca Zuin, Adriano Veloso, João Cândido Portinari, Nivio Ziviani
Date: 2020-04-21 00:00:00
Tasks: Clustering, TAG

In this paper, we deal with the problem of automatic tag recommendation for painting artworks. Diachronic descriptions containing deviations on the vocabulary used to describe each painting usually occur when the work is done by many experts over time. The objective of this work is to provide a framework that produces a more accurate and homogeneous set of tags for each painting in a large collection. To validate our method we build a model based on a weakly-supervised neural network for over $5{,}300$ paintings with hand-labeled descriptions made by experts for the paintings of the Brazilian painter Candido Portinari. This work takes place with the Portinari Project which started in 1979 intending to recover and catalog the paintings of the Brazilian painter. The Portinari paintings at that time were in private collections and museums spread around the world and thus inaccessible to the public. The descriptions of each painting were made by a large number of collaborators over 40 years as the paintings were recovered and these diachronic descriptions caused deviations on the vocabulary used to describe each painting. Our proposed framework consists of (i) a neural network that receives as input the image of each painting and uses frequent itemsets as possible tags, and (ii) a clustering step in which we group related tags based on the output of the pre-trained classifiers.
Identifiability and consistency of network inference using the hub model and variants
Authors: Yunpeng Zhao, Peter Bickel, Charles Weko
Date: 2020-04-21 00:00:00

Statistical network analysis primarily focuses on inferring the parameters of an observed network. In many applications, especially in the social sciences, the observed data is the groups formed by individual subjects. In these applications, the network is itself a parameter of a statistical model. Zhao and Weko (2019) propose a model-based approach, called the hub model, to infer implicit networks from grouping behavior. The hub model assumes that each member of the group is brought together by a member of the group called the hub. The hub model belongs to the family of Bernoulli mixture models. Identifiability of parameters is a notoriously difficult problem for Bernoulli mixture models. This paper proves identifiability of the hub model parameters and estimation consistency under mild conditions. Furthermore, this paper generalizes the hub model by introducing a model component that allows hubless groups in which individual nodes spontaneously appear independent of any other individual. We refer to this additional component as the null component. The new model bridges the gap between the hub model and the degenerate case of the mixture model -- the Bernoulli product. Identifiability and consistency are also proved for the new model. Numerical studies are provided to demonstrate the theoretical results.
Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects
Authors: Will Y. Zou, Smitha Shyam, Michael Mui, Mingshi Wang, Jan Pedersen, Zoubin Ghahramani
Date: 2020-04-21 00:00:00
Tasks: Causal Inference, counterfactual

Causal inference methods are widely applied in the fields of medicine, policy, and economics. Central to these applications is the estimation of treatment effects to make decisions. Current methods make binary yes-or-no decisions based on the treatment effect of a single outcome dimension. These methods are unable to capture continuous space treatment policies with a measure of intensity. They also lack the capacity to consider the complexity of treatment such as matching candidate treatments with the subject. We propose to formulate the effectiveness of treatment as a parametrizable model, expanding to a multitude of treatment intensities and complexities through the continuous policy treatment function, and the likelihood of matching. Our proposal to decompose treatment effect functions into effectiveness factors presents a framework to model a rich space of actions using causal inference. We utilize deep learning to optimize the desired holistic metric space instead of predicting single-dimensional treatment counterfactual. This approach employs a population-wide effectiveness measure and significantly improves the overall effectiveness of the model. The performance of our algorithms is. demonstrated with experiments. When using generic continuous space treatments and matching architecture, we observe a 41% improvement upon prior art with cost-effectiveness and 68% improvement upon a similar method in the average treatment effect. The algorithms capture subtle variations in treatment space, structures the efficient optimizations techniques, and opens up the arena for many applications.
Heterogeneous Causal Learning for Effectiveness Optimization in User Marketing
Authors: Will Y. Zou, Shuyang Du, James Lee, Jan Pedersen
Date: 2020-04-21 00:00:00
Tasks: counterfactual, Marketing

User marketing is a key focus of consumer-based internet companies. Learning algorithms are effective to optimize marketing campaigns which increase user engagement, and facilitates cross-marketing to related products. By attracting users with rewards, marketing methods are effective to boost user activity in the desired products. Rewards incur significant cost that can be off-set by increase in future revenue. Most methodologies rely on churn predictions to prevent losing users to make marketing decisions, which cannot capture up-lift across counterfactual outcomes with business metrics. Other predictive models are capable of estimating heterogeneous treatment effects, but fail to capture the balance of cost versus benefit. We propose a treatment effect optimization methodology for user marketing. This algorithm learns from past experiments and utilizes novel optimization methods to optimize cost efficiency with respect to user selection. The method optimizes decisions using deep learning optimization models to treat and reward users, which is effective in producing cost-effective, impactful marketing campaigns. Our methodology demonstrates superior algorithmic flexibility with integration with deep learning methods and dealing with business constraints. The effectiveness of our model surpasses the quasi-oracle estimation (R-learner) model and causal forests. We also established evaluation metrics that reflect the cost-efficiency and real-world business value. Our proposed constrained and direct optimization algorithms outperform by 24.6% compared with the best performing method in prior art and baseline methods. The methodology is useful in many product scenarios such as optimal treatment allocation and it has been deployed in production world-wide.
Image Retrieval using Multi-scale CNN Features Pooling
Authors: Federico Vaccaro, Marco Bertini, Tiberio Uricchio, Alberto del Bimbo
Date: 2020-04-21 00:00:00
Tasks: Image Retrieval, Retrieval, Triplet

In this paper, we address the problem of image retrieval by learning images representation based on the activations of a Convolutional Neural Network. We present an end-to-end trainable network architecture that exploits a novel multi-scale local pooling based on NetVLAD and a triplet mining procedure based on samples difficulty to obtain an effective image representation. Extensive experiments show that our approach is able to reach state-of-the-art results on three standard datasets.
Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification
Authors: Wei Zhu, Haofu Liao, Wenbin Li, Weijian Li, Jiebo Luo
Date: 2020-04-21 00:00:00
Tasks: Few-Shot Learning, General Classification, image-classification, Image Classification, Mutual Information Estimation

Skin disease classification from images is crucial to dermatological diagnosis. However, identifying skin lesions involves a variety of aspects in terms of size, color, shape, and texture. To make matters worse, many categories only contain very few samples, posing great challenges to conventional machine learning algorithms and even human experts. Inspired by the recent success of Few-Shot Learning (FSL) in natural image classification, we propose to apply FSL to skin disease identification to address the extreme scarcity of training sample problem. However, directly applying FSL to this task does not work well in practice, and we find that the problem can be largely attributed to the incompatibility between Cross Entropy (CE) and episode training, which are both commonly used in FSL. Based on a detailed analysis, we propose the Query-Relative (QR) loss, which proves superior to CE under episode training and is closely related to recently proposed mutual information estimation. Moreover, we further strengthen the proposed QR loss with a novel adaptive hard margin strategy. Comprehensive experiments validate the effectiveness of the proposed FSL scheme and the possibility to diagnosis rare skin disease with a few labeled samples.
A Data and Compute Efficient Design for Limited-Resources Deep Learning
Authors: Mirgahney Mohamed, Gabriele Cesa, Taco S. Cohen, Max Welling
Date: 2020-04-21 00:00:00
Tasks: Deep Learning, Quantization

Thanks to their improved data efficiency, equivariant neural networks have gained increased interest in the deep learning community. They have been successfully applied in the medical domain where symmetries in the data can be effectively exploited to build more accurate and robust models. To be able to reach a much larger body of patients, mobile, on-device implementations of deep learning solutions have been developed for medical applications. However, equivariant models are commonly implemented using large and computationally expensive architectures, not suitable to run on mobile devices. In this work, we design and test an equivariant version of MobileNetV2 and further optimize it with model quantization to enable more efficient inference. We achieve close-to state of the art performance on the Patch Camelyon (PCam) medical dataset while being more computationally efficient.
Mirror Ritual: An Affective Interface for Emotional Self-Reflection
Authors: Nina Rajcic, Jon McCormack
Date: 2020-04-21 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

This paper introduces a new form of real-time affective interface that engages the user in a process of conceptualisation of their emotional state. Inspired by Barrett's Theory of Constructed Emotion, `Mirror Ritual' aims to expand upon the user's accessible emotion concepts, and to ultimately provoke emotional reflection and regulation. The interface uses classified emotions -- obtained through facial expression recognition -- as a basis for dynamically generating poetry. The perceived emotion is used to seed a poetry generation system based on OpenAI's GPT-2 model, fine-tuned on a specially curated corpus. We evaluate the device's ability to foster a personalised, meaningful experience for individual users over a sustained period. A qualitative analysis revealed that participants were able to affectively engage with the mirror, with each participant developing a unique interpretation of its poetry in the context of their own emotional landscape.
Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution
Authors: Yingruo Fan, Jacqueline C. K. Lam, Victor O. K. Li
Date: 2020-04-20 00:00:00
Tasks: Semantic correspondence

The intensity estimation of facial action units (AUs) is challenging due to subtle changes in the person's facial appearance. Previous approaches mainly rely on probabilistic models or predefined rules for modeling co-occurrence relationships among AUs, leading to limited generalization. In contrast, we present a new learning framework that automatically learns the latent relationships of AUs via establishing semantic correspondences between feature maps. In the heatmap regression-based network, feature maps preserve rich semantic information associated with AU intensities and locations. Moreover, the AU co-occurring pattern can be reflected by activating a set of feature channels, where each channel encodes a specific visual pattern of AU. This motivates us to model the correlation among feature channels, which implicitly represents the co-occurrence relationship of AU intensity levels. Specifically, we introduce a semantic correspondence convolution (SCC) module to dynamically compute the correspondences from deep and low resolution feature maps, and thus enhancing the discriminability of features. The experimental results demonstrate the effectiveness and the superior performance of our method on two benchmark datasets.
MGX: Near-Zero Overhead Memory Protection for Data-Intensive Accelerators
Authors: Weizhe Hua, Muhammad Umar, Zhiru Zhang, G. Edward Suh
Date: 2020-04-20 00:00:00

This paper introduces MGX, a near-zero overhead memory protection scheme for hardware accelerators. MGX minimizes the performance overhead of off-chip memory encryption and integrity verification by exploiting the application-specific properties of the accelerator execution. In particular, accelerators tend to explicitly manage data movement between on-chip and off-chip memories. Therefore, the general memory access pattern of an accelerator can largely be determined for a given application. Exploiting these characteristics, MGX generates version numbers used in memory encryption and integrity verification using on-chip accelerator state rather than storing them in the off-chip memory; it also customizes the granularity of the memory protection to match the granularity used by the accelerator. To demonstrate the efficacy of MGX, we present an in-depth study of MGX for DNN and graph algorithms. Experimental results show that on average, MGX lowers the performance overhead of memory protection from 28% and 33% to 4% and 5% for DNN and graph processing accelerators in a wide range of benchmarks, respectively.
Approximate exploitability: Learning a best response in large games
Authors: Finbarr Timbers, Nolan Bard, Edward Lockhart, Marc Lanctot, Martin Schmid, Neil Burch, Julian Schrittwieser, Thomas Hubert, Michael Bowling
Date: 2020-04-20 00:00:00
Tasks: Deep Reinforcement Learning

Researchers have demonstrated that neural networks are vulnerable to adversarial examples and subtle environment changes, both of which one can view as a form of distribution shift. To humans, the resulting errors can look like blunders, eroding trust in these agents. In prior games research, agent evaluation often focused on the in-practice game outcomes. While valuable, such evaluation typically fails to evaluate robustness to worst-case outcomes. Prior research in computer poker has examined how to assess such worst-case performance, both exactly and approximately. Unfortunately, exact computation is infeasible with larger domains, and existing approximations rely on poker-specific knowledge. We introduce ISMCTS-BR, a scalable search-based deep reinforcement learning algorithm for learning a best response to an agent, thereby approximating worst-case performance. We demonstrate the technique in several two-player zero-sum games against a variety of agents, including several AlphaZero-based agents.