pwc-archive/papers-with-abstracts huggingface dataset:
A new approach in model selection for ordinal target variables
Authors: Elena Ballante, Pierpaolo Uberti, Silvia Figini
Date: 2020-03-05 00:00:00
Tasks: Model Selection

This paper introduces a novel approach to assess model performance for predictive models characterized by an ordinal target variable in order to satisfy the lack of suitable tools in this framework. Our methodological proposal is a new index for model assessment which satisfies mathematical properties and can be easily computed. In order to show how our performance indicator works, empirical evidence achieved on a toy examples and simulated data are provided. On the basis of results at hand, we underline that our approach discriminates better for model selection with respect to performance indexes proposed in the literature.
HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference
Authors: Tianyu Liu, Xin Zheng, Baobao Chang, Zhifang Sui
Date: 2020-03-05 00:00:00
Proceeding: LREC 2020 5
Tasks: Natural Language Inference

Many recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by merely looking at the hypothesis while completely ignoring the premise. In this work, we manage to derive adversarial examples in terms of the hypothesis-only bias and explore eligible ways to mitigate such bias. Specifically, we extract various phrases from the hypotheses (artificial patterns) in the training sets, and show that they have been strong indicators to the specific labels. We then figure out `hard' and `easy' instances from the original test sets whose labels are opposite to or consistent with those indications. We also set up baselines including both pretrained models (BERT, RoBERTa, XLNet) and competitive non-pretrained models (InferSent, DAM, ESIM). Apart from the benchmark and baselines, we also investigate two debiasing approaches which exploit the artificial pattern modeling to mitigate such hypothesis-only bias: down-sampling and adversarial training. We believe those methods can be treated as competitive baselines in NLI debiasing tasks.
Combating noisy labels by agreement: A joint training method with co-regularization
Authors: Hongxin Wei, Lei Feng, Xiangyu Chen, Bo An
Date: 2020-03-05 00:00:00
Proceeding: CVPR 2020 6
Tasks: Diversity, Image Classification, Learning with noisy labels, Weakly-supervised Learning

Deep Learning with noisy labels is a practically challenging problem in weakly supervised learning. The state-of-the-art approaches "Decoupling" and "Co-teaching+" claim that the "disagreement" strategy is crucial for alleviating the problem of learning with noisy labels. In this paper, we start from a different perspective and propose a robust learning paradigm called JoCoR, which aims to reduce the diversity of two networks during training. Specifically, we first use two networks to make predictions on the same mini-batch data and calculate a joint loss with Co-Regularization for each training example. Then we select small-loss examples to update the parameters of both two networks simultaneously. Trained by the joint loss, these two networks would be more and more similar due to the effect of Co-Regularization. Extensive experimental results on corrupted data from benchmark datasets including MNIST, CIFAR-10, CIFAR-100 and Clothing1M demonstrate that JoCoR is superior to many state-of-the-art approaches for learning with noisy labels.
Search Space of Adversarial Perturbations against Image Filters
Authors: Dang Duy Thang, Toshihiro Matsui
Date: 2020-03-05 00:00:00
Tasks: Adversarial Attack, Deep Learning, image-classification, Image Classification

The superiority of deep learning performance is threatened by safety issues for itself. Recent findings have shown that deep learning systems are very weak to adversarial examples, an attack form that was altered by the attacker's intent to deceive the deep learning system. There are many proposed defensive methods to protect deep learning systems against adversarial examples. However, there is still a lack of principal strategies to deceive those defensive methods. Any time a particular countermeasure is proposed, a new powerful adversarial attack will be invented to deceive that countermeasure. In this study, we focus on investigating the ability to create adversarial patterns in search space against defensive methods that use image filters. Experimental results conducted on the ImageNet dataset with image classification tasks showed the correlation between the search space of adversarial perturbation and filters. These findings open a new direction for building stronger offensive methods towards deep learning systems.
Balance Between Efficient and Effective Learning: Dense2Sparse Reward Shaping for Robot Manipulation with Environment Uncertainty
Authors: Yongle Luo, Kun Dong, Lili Zhao, Zhiyong Sun, Chao Zhou, Bo Song
Date: 2020-03-05 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Robot Manipulation

Efficient and effective learning is one of the ultimate goals of the deep reinforcement learning (DRL), although the compromise has been made in most of the time, especially for the application of robot manipulations. Learning is always expensive for robot manipulation tasks and the learning effectiveness could be affected by the system uncertainty. In order to solve above challenges, in this study, we proposed a simple but powerful reward shaping method, namely Dense2Sparse. It combines the advantage of fast convergence of dense reward and the noise isolation of the sparse reward, to achieve a balance between learning efficiency and effectiveness, which makes it suitable for robot manipulation tasks. We evaluated our Dense2Sparse method with a series of ablation experiments using the state representation model with system uncertainty. The experiment results show that the Dense2Sparse method obtained higher expected reward compared with the ones using standalone dense reward or sparse reward, and it also has a superior tolerance of system uncertainty.
Zero-Shot Cross-Lingual Transfer with Meta Learning
Authors: Farhad Nooralahzadeh, Giannis Bekoulis, Johannes Bjerva, Isabelle Augenstein
Date: 2020-03-05 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Cross-Lingual Transfer, Few-Shot NLI, Language Modelling, Meta-Learning, Natural Language Inference, Natural Language Understanding, Question Answering, Zero-Shot Cross-Lingual Transfer

Learning what to share between tasks has been a topic of great importance recently, as strategic sharing of knowledge has been shown to improve downstream task performance. This is particularly important for multilingual applications, as most languages in the world are under-resourced. Here, we consider the setting of training models on multiple different languages at the same time, when little or no data is available for languages other than English. We show that this challenging setup can be approached using meta-learning, where, in addition to training a source language model, another model learns to select which training instances are the most beneficial to the first. We experiment using standard supervised, zero-shot cross-lingual, as well as few-shot cross-lingual settings for different natural language understanding tasks (natural language inference, question answering). Our extensive experimental setup demonstrates the consistent effectiveness of meta-learning for a total of 15 languages. We improve upon the state-of-the-art for zero-shot and few-shot NLI (on MultiNLI and XNLI) and QA (on the MLQA dataset). A comprehensive error analysis indicates that the correlation of typological features between languages can partly explain when parameter sharing learned via meta-learning is beneficial.
BERT as a Teacher: Contextual Embeddings for Sequence-Level Reward
Authors: Florian Schmidt, Thomas Hofmann
Date: 2020-03-05 00:00:00
Tasks: Reinforcement Learning

Measuring the quality of a generated sequence against a set of references is a central problem in many learning frameworks, be it to compute a score, to assign a reward, or to perform discrimination. Despite great advances in model architectures, metrics that scale independently of the number of references are still based on n-gram estimates. We show that the underlying operations, counting words and comparing counts, can be lifted to embedding words and comparing embeddings. An in-depth analysis of BERT embeddings shows empirically that contextual embeddings can be employed to capture the required dependencies while maintaining the necessary scalability through appropriate pruning and smoothing techniques. We cast unconditional generation as a reinforcement learning problem and show that our reward function indeed provides a more effective learning signal than n-gram reward in this challenging setting.
Claim Check-Worthiness Detection as Positive Unlabelled Learning
Authors: Dustin Wright, Isabelle Augenstein
Date: 2020-03-05 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Fact Checking, Rumour Detection, Sentence

As the first step of automatic fact checking, claim check-worthiness detection is a critical component of fact checking systems. There are multiple lines of research which study this problem: check-worthiness ranking from political speeches and debates, rumour detection on Twitter, and citation needed detection from Wikipedia. To date, there has been no structured comparison of these various tasks to understand their relatedness, and no investigation into whether or not a unified approach to all of them is achievable. In this work, we illuminate a central challenge in claim check-worthiness detection underlying all of these tasks, being that they hinge upon detecting both how factual a sentence is, as well as how likely a sentence is to be believed without verification. As such, annotators only mark those instances they judge to be clear-cut check-worthy. Our best performing method is a unified approach which automatically corrects for this using a variant of positive unlabelled learning that finds instances which were incorrectly labelled as not check-worthy. In applying this, we out-perform the state of the art in two of the three tasks studied for claim check-worthiness detection in English.
Recognition of Smoking Gesture Using Smart Watch Technology
Authors: Casey A. Cole, Bethany Janos, Dien Anshari, James F. Thrasher, Scott Strayer, Homayoun Valafar
Date: 2020-03-05 00:00:00

Diseases resulting from prolonged smoking are the most common preventable causes of death in the world today. In this report we investigate the success of utilizing accelerometer sensors in smart watches to identify smoking gestures. Early identification of smoking gestures can help to initiate the appropriate intervention method and prevent relapses in smoking. Our experiments indicate 85%-95% success rates in identification of smoking gesture among other similar gestures using Artificial Neural Networks (ANNs). Our investigations concluded that information obtained from the x-dimension of accelerometers is the best means of identifying the smoking gesture, while y and z dimensions are helpful in eliminating other gestures such as: eating, drinking, and scratch of nose. We utilized sensor data from the Apple Watch during the training of the ANN. Using sensor data from another participant collected on Pebble Steel, we obtained a smoking identification accuracy of greater than 90% when using an ANN trained on data previously collected from the Apple Watch. Finally, we have demonstrated the possibility of using smart watches to perform continuous monitoring of daily activities.
Confusing and Detecting ML Adversarial Attacks with Injected Attractors
Authors: Jiyi Zhang, Ee-Chien Chang, Hwee Kuan Lee
Date: 2020-03-05 00:00:00
Tasks: Decoder

Many machine learning adversarial attacks find adversarial samples of a victim model ${\mathcal M}$ by following the gradient of some attack objective functions, either explicitly or implicitly. To confuse and detect such attacks, we take the proactive approach that modifies those functions with the goal of misleading the attacks to some local minimals, or to some designated regions that can be easily picked up by an analyzer. To achieve this goal, we propose adding a large number of artifacts, which we called $attractors$, onto the otherwise smooth function. An attractor is a point in the input space, where samples in its neighborhood have gradient pointing toward it. We observe that decoders of watermarking schemes exhibit properties of attractors and give a generic method that injects attractors from a watermark decoder into the victim model ${\mathcal M}$. This principled approach allows us to leverage on known watermarking schemes for scalability and robustness and provides explainability of the outcomes. Experimental studies show that our method has competitive performance. For instance, for un-targeted attacks on CIFAR-10 dataset, we can reduce the overall attack success rate of DeepFool to 1.9%, whereas known defense LID, FS and MagNet can reduce the rate to 90.8%, 98.5% and 78.5% respectively.
Knot Selection in Sparse Gaussian Processes with a Variational Objective Function
Authors: Nathaniel Garton, Jarad Niemi, Alicia Carriquiry
Date: 2020-03-05 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes

Sparse, knot-based Gaussian processes have enjoyed considerable success as scalable approximations to full Gaussian processes. Certain sparse models can be derived through specific variational approximations to the true posterior, and knots can be selected to minimize the Kullback-Leibler divergence between the approximate and true posterior. While this has been a successful approach, simultaneous optimization of knots can be slow due to the number of parameters being optimized. Furthermore, there have been few proposed methods for selecting the number of knots, and no experimental results exist in the literature. We propose using a one-at-a-time knot selection algorithm based on Bayesian optimization to select the number and locations of knots. We showcase the competitive performance of this method relative to simultaneous optimization of knots on three benchmark data sets, but at a fraction of the computational cost.
Self-Supervised Visual Learning by Variable Playback Speeds Prediction of a Video
Authors: Hyeon Cho, Tae-hoon Kim, Hyung Jin Chang, Wonjun Hwang
Date: 2020-03-05 00:00:00
Tasks: Action Recognition, Representation Learning, Retrieval, Self-Supervised Action Recognition, Self-Supervised Learning, Video Retrieval

We propose a self-supervised visual learning method by predicting the variable playback speeds of a video. Without semantic labels, we learn the spatio-temporal visual representation of the video by leveraging the variations in the visual appearance according to different playback speeds under the assumption of temporal coherence. To learn the spatio-temporal visual variations in the entire video, we have not only predicted a single playback speed but also generated clips of various playback speeds and directions with randomized starting points. Hence the visual representation can be successfully learned from the meta information (playback speeds and directions) of the video. We also propose a new layer dependable temporal group normalization method that can be applied to 3D convolutional networks to improve the representation learning performance where we divide the temporal features into several groups and normalize each one using the different corresponding parameters. We validate the effectiveness of our method by fine-tuning it to the action recognition and video retrieval tasks on UCF-101 and HMDB-51.
SketchyCOCO: Image Generation from Freehand Scene Sketches
Authors: Chengying Gao, Qi Liu, Qi Xu, Li-Min Wang, Jianzhuang Liu, Changqing Zou
Date: 2020-03-05 00:00:00
Proceeding: CVPR 2020 6
Tasks: Attribute, Generative Adversarial Network, Image Generation, Object, Sketch-to-Image Translation

We introduce the first method for automatic image generation from scene-level freehand sketches. Our model allows for controllable image generation by specifying the synthesis goal via freehand sketches. The key contribution is an attribute vector bridged Generative Adversarial Network called EdgeGAN, which supports high visual-quality object-level image content generation without using freehand sketches as training data. We have built a large-scale composite dataset called SketchyCOCO to support and evaluate the solution. We validate our approach on the tasks of both object-level and scene-level image generation on SketchyCOCO. Through quantitative, qualitative results, human evaluation and ablation studies, we demonstrate the method's capacity to generate realistic complex scene-level images from various freehand sketches.
Stochastic Linear Contextual Bandits with Diverse Contexts
Authors: Weiqiang Wu, Jing Yang, Cong Shen
Date: 2020-03-05 00:00:00
Tasks: Diversity, Multi-Armed Bandits

In this paper, we investigate the impact of context diversity on stochastic linear contextual bandits. As opposed to the previous view that contexts lead to more difficult bandit learning, we show that when the contexts are sufficiently diverse, the learner is able to utilize the information obtained during exploitation to shorten the exploration process, thus achieving reduced regret. We design the LinUCB-d algorithm, and propose a novel approach to analyze its regret performance. The main theoretical result is that under the diverse context assumption, the cumulative expected regret of LinUCB-d is bounded by a constant. As a by-product, our results improve the previous understanding of LinUCB and strengthen its performance guarantee.
SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives
Authors: Emmanouil Angelis, Philippe Wenk, Bernhard Schölkopf, Stefan Bauer, Andreas Krause
Date: 2020-03-05 00:00:00
Tasks: Gaussian Processes, regression

Gaussian processes are an important regression tool with excellent analytic properties which allow for direct integration of derivative observations. However, vanilla GP methods scale cubically in the amount of observations. In this work, we propose a novel approach for scaling GP regression with derivatives based on quadrature Fourier features. We then prove deterministic, non-asymptotic and exponentially fast decaying error bounds which apply for both the approximated kernel as well as the approximated posterior. To furthermore illustrate the practical applicability of our method, we then apply it to ODIN, a recently developed algorithm for ODE parameter inference. In an extensive experiments section, all results are empirically validated, demonstrating the speed, accuracy, and practical applicability of this approach.
Learning the sense of touch in simulation: a sim-to-real strategy for vision-based tactile sensing
Authors: Carmelo Sferrazza, Thomas Bi, Raffaello D'Andrea
Date: 2020-03-05 00:00:00

Data-driven approaches to tactile sensing aim to overcome the complexity of accurately modeling contact with soft materials. However, their widespread adoption is impaired by concerns about data efficiency and the capability to generalize when applied to various tasks. This paper focuses on both these aspects with regard to a vision-based tactile sensor, which aims to reconstruct the distribution of the three-dimensional contact forces applied on its soft surface. Accurate models for the soft materials and the camera projection, derived via state-of-the-art techniques in the respective domains, are employed to generate a dataset in simulation. A strategy is proposed to train a tailored deep neural network entirely from the simulation data. The resulting learning architecture is directly transferable across multiple tactile sensors without further training and yields accurate predictions on real data, while showing promising generalization capabilities to unseen contact conditions.
Towards Effective Human-AI Collaboration in GUI-Based Interactive Task Learning Agents
Authors: Toby Jia-Jun Li, Jingya Chen, Tom M. Mitchell, Brad A. Myers
Date: 2020-03-05 00:00:00

We argue that a key challenge in enabling usable and useful interactive task learning for intelligent agents is to facilitate effective Human-AI collaboration. We reflect on our past 5 years of efforts on designing, developing and studying the SUGILITE system, discuss the issues on incorporating recent advances in AI with HCI principles in mixed-initiative interactions and multi-modal interactions, and summarize the lessons we learned. Lastly, we identify several challenges and opportunities, and describe our ongoing work
Hadath: From Social Media Mapping to Multi-Resolution Event-Enriched Maps
Authors: Faizan Ur Rehman, Imad Afyouni, Ahmed Lbath, Saleh Basalamah
Date: 2020-03-05 00:00:00

Publicly available data is increasing rapidly, and will continue to grow with the advancement of technologies in sensors, smartphones and the Internet of Things. Data from multiple sources can improve coverage and provide more relevant knowledge about surrounding events and points of Interest. The strength of one source of data can compensate for the shortcomings of another source by providing supplementary information. Maps are also getting popular day-by-day and people are using it to achieve their daily task smoothly and efficiently. Starting from paper maps hundred years ago, multiple type of maps are available with point of interest, real-time traffic update or displaying micro-blogs from social media. In this paper, we introduce Hadath, a system that displays multi-resolution live events of interest from a variety of available data sources. The system has been designed to be able to handle multiple type of inputs by encapsulating incoming unstructured data into generic data packets. System extracts local events of interest from generic data packets and identify their spatio-temporal scope to display such events on a map, so that as a user changes the zoom level, only events of appropriate scope are displayed. This allows us to show live events in correspondence to the scale of view - when viewing at a city scale, we see events of higher significance, while zooming in to a neighbourhood, events of a more local interest are highlighted. The final output creates a unique and dynamic map browsing experience. Finally, to validate our proposed system, we conducted experiments on social media data.
Fuzzy k-Nearest Neighbors with monotonicity constraints: Moving towards the robustness of monotonic noise
Authors: Sergio González, Salvador García, Sheng-Tun Li, Robert John, Francisco Herrera
Date: 2020-03-05 00:00:00

This paper proposes a new model based on Fuzzy k-Nearest Neighbors for classification with monotonic constraints, Monotonic Fuzzy k-NN (MonFkNN). Real-life data-sets often do not comply with monotonic constraints due to class noise. MonFkNN incorporates a new calculation of fuzzy memberships, which increases robustness against monotonic noise without the need for relabeling. Our proposal has been designed to be adaptable to the different needs of the problem being tackled. In several experimental studies, we show significant improvements in accuracy while matching the best degree of monotonicity obtained by comparable methods. We also show that MonFkNN empirically achieves improved performance compared with Monotonic k-NN in the presence of large amounts of class noise.