pwc-archive/papers-with-abstracts huggingface dataset:
FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification
Authors: Zhuo Zheng, Yanfei Zhong, Ailong Ma, Liangpei Zhang
Date: 2020-11-11 00:00:00
Tasks: Classification, Decoder, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Deep learning techniques have provided significant improvements in hyperspectral image (HSI) classification. The current deep learning based HSI classifiers follow a patch-based learning framework by dividing the image into overlapping patches. As such, these methods are local learning methods, which have a high computational cost. In this paper, a fast patch-free global learning (FPGA) framework is proposed for HSI classification. In FPGA, an encoder-decoder based FCN is utilized to consider the global spatial information by processing the whole image, which results in fast inference. However, it is difficult to directly utilize the encoder-decoder based FCN for HSI classification as it always fails to converge due to the insufficiently diverse gradients caused by the limited training samples. To solve the divergence problem and maintain the abilities of FCN of fast inference and global spatial information mining, a global stochastic stratified sampling strategy is first proposed by transforming all the training samples into a stochastic sequence of stratified samples. This strategy can obtain diverse gradients to guarantee the convergence of the FCN in the FPGA framework. For a better design of FCN architecture, FreeNet, which is a fully end-to-end network for HSI classification, is proposed to maximize the exploitation of the global spatial information and boost the performance via a spectral attention based encoder and a lightweight decoder. A lateral connection module is also designed to connect the encoder and decoder, fusing the spatial details in the encoder and the semantic features in the decoder. The experimental results obtained using three public benchmark datasets suggest that the FPGA framework is superior to the patch-based framework in both speed and accuracy for HSI classification. Code has been made available at: https://github.com/Z-Zheng/FreeNet.
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions
Authors: Yanbo Xu, Divyat Mahajan, Liz Manrao, Amit Sharma, Emre Kiciman
Date: 2020-11-11 00:00:00
Tasks: Causal Inference, Marketing

For many kinds of interventions, such as a new advertisement, marketing intervention, or feature recommendation, it is important to target a specific subset of people for maximizing its benefits at minimum cost or potential harm. However, a key challenge is that no data is available about the effect of such a prospective intervention since it has not been deployed yet. In this work, we propose a split-treatment analysis that ranks the individuals most likely to be positively affected by a prospective intervention using past observational data. Unlike standard causal inference methods, the split-treatment method does not need any observations of the target treatments themselves. Instead it relies on observations of a proxy treatment that is caused by the target treatment. Under reasonable assumptions, we show that the ranking of heterogeneous causal effect based on the proxy treatment is the same as the ranking based on the target treatment's effect. In the absence of any interventional data for cross-validation, Split-Treatment uses sensitivity analyses for unobserved confounding to select model parameters. We apply Split-Treatment to both a simulated data and a large-scale, real-world targeting task and validate our discovered rankings via a randomized experiment for the latter.
Phase transition in Kermack-McKendrick Model of Epidemic: Effects of Additional Nonlinearity and Introduction of Medicated Immunity
Authors: Agniva Datta, Muktish Acharyya
Date: 2020-11-06 00:00:00
Tasks: Epidemiology

Mathematical modelling of the spread of epidemics has been an interesting challenge in the field of epidemiology. The SIR Model proposed by Kermack and McKendrick in 1927 is a prototypical model of epidemiology. However, it has its limitations. In this paper, we show two independent ways of generalizing this model, the first one if the vaccine isn't discovered or ready to use and the next one, if the vaccine is discovered and ready to use. In the first part, we have pointed out a major over-simplification, i.e., assumption of variation of the time derivatives of the variables with the linear or quadratic powers of the individual variables and introduce two new parameters to incorporate further nonlinearity in the number of infected people in the model. As a result of this, we show how this additional nonlinearity, in the newly introduced parameters, can bring a significant shift in the peak time of infection, i.e., the time at which the infected population reaches maximum. We show that in special cases, even we can get a transition from epidemic to a non-epidemic stage of a particular infectious disease. We further study one such special case and treat it as a problem of phase transition. Then, we investigate all the necessary parameters of this phase transition, like the order parameter and critical exponent. We observe that $O_p \sim (q_c-q)^{\beta}$. {\it As far as we know the phase transition and its quantification in terms of the scaling behaviour is not yet know in the context of pandemic}. In the second part, we incorporate in the model, a consideration of artificial herd immunity and show how we can decrease the peak time of infection with a subsequent decrease in the maximum number of infected people.
GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts
Authors: Lin Cheng, Zijiang Yang
Date: 2020-11-11 00:00:00
Tasks: Program Synthesis

Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specifications. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and nodes are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program.
The Impact of Text Presentation on Translator Performance
Authors: Samuel Läubli, Patrick Simianer, Joern Wuebker, Geza Kovacs, Rico Sennrich, Spence Green
Date: 2020-11-11 00:00:00
Tasks: Sentence, Translation

Widely used computer-aided translation (CAT) tools divide documents into segments such as sentences and arrange them in a side-by-side, spreadsheet-like view. We present the first controlled evaluation of these design choices on translator performance, measuring speed and accuracy in three experimental text processing tasks. We find significant evidence that sentence-by-sentence presentation enables faster text reproduction and within-sentence error identification compared to unsegmented text, and that a top-and-bottom arrangement of source and target sentences enables faster text reproduction compared to a side-by-side arrangement. For revision, on the other hand, our results suggest that presenting unsegmented text results in the highest accuracy and time efficiency. Our findings have direct implications for best practices in designing CAT tools.
Transformers for One-Shot Visual Imitation
Authors: Sudeep Dasari, Abhinav Gupta
Date: 2020-11-11 00:00:00
Tasks: Imitation Learning

Humans are able to seamlessly visually imitate others, by inferring their intentions and using past experience to achieve the same end goal. In other words, we can parse complex semantic knowledge from raw video and efficiently translate that into concrete motor control. Is it possible to give a robot this same capability? Prior research in robot imitation learning has created agents which can acquire diverse skills from expert human operators. However, expanding these techniques to work with a single positive example during test time is still an open challenge. Apart from control, the difficulty stems from mismatches between the demonstrator and robot domains. For example, objects may be placed in different locations (e.g. kitchen layouts are different in every house). Additionally, the demonstration may come from an agent with different morphology and physical appearance (e.g. human), so one-to-one action correspondences are not available. This paper investigates techniques which allow robots to partially bridge these domain gaps, using their past experience. A neural network is trained to mimic ground truth robot actions given context video from another agent, and must generalize to unseen task instances when prompted with new videos during test time. We hypothesize that our policy representations must be both context driven and dynamics aware in order to perform these tasks. These assumptions are baked into the neural network using the Transformers attention mechanism and a self-supervised inverse dynamics loss. Finally, we experimentally determine that our method accomplishes a $\sim 2$x improvement in terms of task success rate over prior baselines in a suite of one-shot manipulation tasks.
Real-Time Decentralized knowledge Transfer at the Edge
Authors: Orpaz Goldstein, Mohammad Kachuee, Derek Shiell, Majid Sarrafzadeh
Date: 2020-11-11 00:00:00
Tasks: Knowledge Distillation, Transfer Learning

The proliferation of edge networks creates islands of learning agents working on local streams of data. Transferring knowledge between these agents in real-time without exposing private data allows for collaboration to decrease learning time and increase model confidence. Incorporating knowledge from data that a local model did not see creates an ability to debias a local model or add to classification abilities on data never before seen. Transferring knowledge in a selective decentralized approach enables models to retain their local insights, allowing for local flavors of a machine learning model. This approach suits the decentralized architecture of edge networks, as a local edge node will serve a community of learning agents that will likely encounter similar data. We propose a method based on knowledge distillation for pairwise knowledge transfer pipelines from models trained on non-i.i.d. data and compare it to other popular knowledge transfer methods. Additionally, we test different scenarios of knowledge transfer network construction and show the practicality of our approach. Our experiments show knowledge transfer using our model outperforms standard methods in a real-time transfer scenario.
On End-to-end Multi-channel Time Domain Speech Separation in Reverberant Environments
Authors: Jisi Zhang, Catalin Zorila, Rama Doddipatla, Jon Barker
Date: 2020-11-11 00:00:00
Tasks: speech-recognition, Speech Recognition, Speech Separation

This paper introduces a new method for multi-channel time domain speech separation in reverberant environments. A fully-convolutional neural network structure has been used to directly separate speech from multiple microphone recordings, with no need of conventional spatial feature extraction. To reduce the influence of reverberation on spatial feature extraction, a dereverberation pre-processing method has been applied to further improve the separation performance. A spatialized version of wsj0-2mix dataset has been simulated to evaluate the proposed system. Both source separation and speech recognition performance of the separated signals have been evaluated objectively. Experiments show that the proposed fully-convolutional network improves the source separation metric and the word error rate (WER) by more than 13% and 50% relative, respectively, over a reference system with conventional features. Applying dereverberation as pre-processing to the proposed system can further reduce the WER by 29% relative using an acoustic model trained on clean and reverberated data.
$(f,Γ)$-Divergences: Interpolating between $f$-Divergences and Integral Probability Metrics
Authors: Jeremiah Birrell, Paul Dupuis, Markos A. Katsoulakis, Yannis Pantazis, Luc Rey-Bellet
Date: 2020-11-11 00:00:00
Tasks: Image Generation, Uncertainty Quantification

We develop a rigorous and general framework for constructing information-theoretic divergences that subsume both $f$-divergences and integral probability metrics (IPMs), such as the $1$-Wasserstein distance. We prove under which assumptions these divergences, hereafter referred to as $(f,\Gamma)$-divergences, provide a notion of `distance' between probability measures and show that they can be expressed as a two-stage mass-redistribution/mass-transport process. The $(f,\Gamma)$-divergences inherit features from IPMs, such as the ability to compare distributions which are not absolutely continuous, as well as from $f$-divergences, namely the strict concavity of their variational representations and the ability to control heavy-tailed distributions for particular choices of $f$. When combined, these features establish a divergence with improved properties for estimation, statistical learning, and uncertainty quantification applications. Using statistical learning as an example, we demonstrate their advantage in training generative adversarial networks (GANs) for heavy-tailed, not-absolutely continuous sample distributions. We also show improved performance and stability over gradient-penalized Wasserstein GAN in image generation.
Asymptotically Optimal Information-Directed Sampling
Authors: Johannes Kirschner, Tor Lattimore, Claire Vernade, Csaba Szepesvári
Date: 2020-11-11 00:00:00

We introduce a simple and efficient algorithm for stochastic linear bandits with finitely many actions that is asymptotically optimal and (nearly) worst-case optimal in finite time. The approach is based on the frequentist information-directed sampling (IDS) framework, with a surrogate for the information gain that is informed by the optimization problem that defines the asymptotic lower bound. Our analysis sheds light on how IDS balances the trade-off between regret and information and uncovers a surprising connection between the recently proposed primal-dual methods and the IDS algorithm. We demonstrate empirically that IDS is competitive with UCB in finite-time, and can be significantly better in the asymptotic regime.
LittleYOLO-SPP: A Delicate Real-Time Vehicle Detection Algorithm
Authors: Sri Jamiya S, Esther Rani P
Date: 2020-11-11 00:00:00
Tasks: Fast Vehicle Detection, object-detection, Object Detection, vehicle detection

Vehicle detection in real-time is a challenging and important task. The existing real-time vehicle detection lacks accuracy and speed. Real-time systems must detect and locate vehicles during criminal activities like theft of vehicle and road traffic violations with high accuracy. Detection of vehicles in complex scenes with occlusion is also extremely difficult. In this study, a lightweight model of deep neural network LittleYOLO-SPP based on the YOLOv3-tiny network is proposed to detect vehicles effectively in real-time. The YOLOv3-tiny object detection network is improved by modifying its feature extraction network to increase the speed and accuracy of vehicle detection. The proposed network incorporated Spatial pyramid pooling into the network, which consists of different scales of pooling layers for concatenation of features to enhance network learning capability. The Mean square error (MSE) and Generalized IoU (GIoU) loss function for bounding box regression is used to increase the performance of the network. The network training includes vehicle-based classes from PASCAL VOC 2007,2012 and MS COCO 2014 datasets such as car, bus, and truck. LittleYOLO-SPP network detects the vehicle in real-time with high accuracy regardless of video frame and weather conditions. The improved network achieves a higher mAP of 77.44% on PASCAL VOC and 52.95% mAP on MS COCO datasets.
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy
Authors: Di Wang, Marco Gaboardi, Adam Smith, Jinhui Xu
Date: 2020-11-11 00:00:00

In this paper, we study the Empirical Risk Minimization (ERM) problem in the non-interactive Local Differential Privacy (LDP) model. Previous research on this problem \citep{smith2017interaction} indicates that the sample complexity, to achieve error $\alpha$, needs to be exponentially depending on the dimensionality $p$ for general loss functions. In this paper, we make two attempts to resolve this issue by investigating conditions on the loss functions that allow us to remove such a limit. In our first attempt, we show that if the loss function is $(\infty, T)$-smooth, by using the Bernstein polynomial approximation we can avoid the exponential dependency in the term of $\alpha$. We then propose player-efficient algorithms with $1$-bit communication complexity and $O(1)$ computation cost for each player. The error bound of these algorithms is asymptotically the same as the original one. With some additional assumptions, we also give an algorithm which is more efficient for the server. In our second attempt, we show that for any $1$-Lipschitz generalized linear convex loss function, there is an $(\epsilon, \delta)$-LDP algorithm whose sample complexity for achieving error $\alpha$ is only linear in the dimensionality $p$. Our results use a polynomial of inner product approximation technique. Finally, motivated by the idea of using polynomial approximation and based on different types of polynomial approximations, we propose (efficient) non-interactive locally differentially private algorithms for learning the set of k-way marginal queries and the set of smooth queries.
Overview of CAPITEL Shared Tasks at IberLEF 2020: Named Entity Recognition and Universal Dependencies Parsing
Authors: Jordi Porta-Zamorano, Luis Espinosa-Anke
Date: 2020-11-11 00:00:00
Tasks: Articles, Dependency Parsing, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

We present the results of the CAPITEL-EVAL shared task, held in the context of the IberLEF 2020 competition series. CAPITEL-EVAL consisted on two subtasks: (1) Named Entity Recognition and Classification and (2) Universal Dependency parsing. For both, the source data was a newly annotated corpus, CAPITEL, a collection of Spanish articles in the newswire domain. A total of seven teams participated in CAPITEL-EVAL, with a total of 13 runs submitted across all subtasks. Data, results and further information about this task can be found at sites.google.com/view/capitel2020.
J-Recs: Principled and Scalable Recommendation Justification
Authors: Namyong Park, Andrey Kan, Christos Faloutsos, Xin Luna Dong
Date: 2020-11-11 00:00:00
Tasks: Persuasiveness

Online recommendation is an essential functionality across a variety of services, including e-commerce and video streaming, where items to buy, watch, or read are suggested to users. Justifying recommendations, i.e., explaining why a user might like the recommended item, has been shown to improve user satisfaction and persuasiveness of the recommendation. In this paper, we develop a method for generating post-hoc justifications that can be applied to the output of any recommendation algorithm. Existing post-hoc methods are often limited in providing diverse justifications, as they either use only one of many available types of input data, or rely on the predefined templates. We address these limitations of earlier approaches by developing J-Recs, a method for producing concise and diverse justifications. J-Recs is a recommendation model-agnostic method that generates diverse justifications based on various types of product and user data (e.g., purchase history and product attributes). The challenge of jointly processing multiple types of data is addressed by designing a principled graph-based approach for justification generation. In addition to theoretical analysis, we present an extensive evaluation on synthetic and real-world data. Our results show that J-Recs satisfies desirable properties of justifications, and efficiently produces effective justifications, matching user preferences up to 20% more accurately than baselines.
On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension
Authors: Udari Madhushani, Biswadip Dey, Naomi Ehrich Leonard, Amit Chakraborty
Date: 2020-11-11 00:00:00
Tasks: Matrix Completion, Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

Value function based reinforcement learning (RL) algorithms, for example, $Q$-learning, learn optimal policies from datasets of actions, rewards, and state transitions. However, when the underlying state transition dynamics are stochastic and evolve on a high-dimensional space, generating independent and identically distributed (IID) data samples for creating these datasets poses a significant challenge due to the intractability of the associated normalizing integral. In these scenarios, Hamiltonian Monte Carlo (HMC) sampling offers a computationally tractable way to generate data for training RL algorithms. In this paper, we introduce a framework, called \textit{Hamiltonian $Q$-Learning}, that demonstrates, both theoretically and empirically, that $Q$ values can be learned from a dataset generated by HMC samples of actions, rewards, and state transitions. Furthermore, to exploit the underlying low-rank structure of the $Q$ function, Hamiltonian $Q$-Learning uses a matrix completion algorithm for reconstructing the updated $Q$ function from $Q$ value updates over a much smaller subset of state-action pairs. Thus, by providing an efficient way to apply $Q$-learning in stochastic, high-dimensional settings, the proposed approach broadens the scope of RL algorithms for real-world applications.
Situated Data, Situated Systems: A Methodology to Engage with Power Relations in Natural Language Processing Research
Authors: Lucy Havens, Melissa Terras, Benjamin Bach, Beatrice Alex
Date: 2020-11-11 00:00:00
Proceeding: GeBNLP (COLING) 2020 12

We propose a bias-aware methodology to engage with power relations in natural language processing (NLP) research. NLP research rarely engages with bias in social contexts, limiting its ability to mitigate bias. While researchers have recommended actions, technical methods, and documentation practices, no methodology exists to integrate critical reflections on bias with technical NLP methods. In this paper, after an extensive and interdisciplinary literature review, we contribute a bias-aware methodology for NLP research. We also contribute a definition of biased text, a discussion of the implications of biased NLP systems, and a case study demonstrating how we are executing the bias-aware methodology in research on archival metadata descriptions.