pwc-archive/papers-with-abstracts huggingface dataset:
Long-Range Indoor Navigation with PRM-RL
Authors: Anthony Francis, Aleksandra Faust, Hao-Tien Lewis Chiang, Jasmine Hsu, J. Chase Kew, Marek Fiser, Tsang-Wei Edward Lee
Date: 2019-02-25 00:00:00
Tasks: Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robot Navigation

Long-range indoor navigation requires guiding robots with noisy sensors and controls through cluttered environments along paths that span a variety of buildings. We achieve this with PRM-RL, a hierarchical robot navigation method in which reinforcement learning agents that map noisy sensors to robot controls learn to solve short-range obstacle avoidance tasks, and then sampling-based planners map where these agents can reliably navigate in simulation; these roadmaps and agents are then deployed on robots, guiding them along the shortest path where the agents are likely to succeed. Here we use Probabilistic Roadmaps (PRMs) as the sampling-based planner, and AutoRL as the reinforcement learning method in the indoor navigation context. We evaluate the method in simulation for kinematic differential drive and kinodynamic car-like robots in several environments, and on differential-drive robots at three physical sites. Our results show PRM-RL with AutoRL is more successful than several baselines, is robust to noise, and can guide robots over hundreds of meters in the face of noise and obstacles in both simulation and on robots, including over 5.8 kilometers of physical robot navigation. Video: https://youtu.be/xN-OWX5gKvQ
S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay
Authors: Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
Date: 2019-02-25 00:00:00
Tasks: Change Detection, Continual Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

We consider the problem of building a state representation model for control, in a continual learning setting. As the environment changes, the aim is to efficiently compress the sensory state's information without losing past knowledge, and then use Reinforcement Learning on the resulting features for efficient policy learning. To this end, we propose S-TRIGGER, a general method for Continual State Representation Learning applicable to Variational Auto-Encoders and its many variants. The method is based on Generative Replay, i.e. the use of generated samples to maintain past knowledge. It comes along with a statistically sound method for environment change detection, which self-triggers the Generative Replay. Our experiments on VAEs show that S-TRIGGER learns state representations that allows fast and high-performing Reinforcement Learning, while avoiding catastrophic forgetting. The resulting system is capable of autonomously learning new information without using past data and with a bounded system size. Code for our experiments is attached in Appendix.
Scalable and Order-robust Continual Learning with Additive Parameter Decomposition
Authors: Jaehong Yoon, Saehoon Kim, Eunho Yang, Sung Ju Hwang
Date: 2019-02-25 00:00:00
Proceeding: ICLR 2020 1
Tasks: Continual Learning, Fairness, Medical Diagnosis, Sensitivity

While recent continual learning methods largely alleviate the catastrophic problem on toy-sized datasets, some issues remain to be tackled to apply them to real-world problem domains. First, a continual learning model should effectively handle catastrophic forgetting and be efficient to train even with a large number of tasks. Secondly, it needs to tackle the problem of order-sensitivity, where the performance of the tasks largely varies based on the order of the task arrival sequence, as it may cause serious problems where fairness plays a critical role (e.g. medical diagnosis). To tackle these practical challenges, we propose a novel continual learning method that is scalable as well as order-robust, which instead of learning a completely shared set of weights, represents the parameters for each task as a sum of task-shared and sparse task-adaptive parameters. With our Additive Parameter Decomposition (APD), the task-adaptive parameters for earlier tasks remain mostly unaffected, where we update them only to reflect the changes made to the task-shared parameters. This decomposition of parameters effectively prevents catastrophic forgetting and order-sensitivity, while being computation- and memory-efficient. Further, we can achieve even better scalability with APD using hierarchical knowledge consolidation, which clusters the task-adaptive parameters to obtain hierarchically shared parameters. We validate our network with APD, APD-Net, on multiple benchmark datasets against state-of-the-art continual learning methods, which it largely outperforms in accuracy, scalability, and order-robustness.
Cooperative Learning of Disjoint Syntax and Semantics
Authors: Serhii Havrylov, Germán Kruszewski, Armand Joulin
Date: 2019-02-25 00:00:00
Proceeding: NAACL 2019 6
Tasks: Domain Generalization, Natural Language Inference, Sentiment Analysis

There has been considerable attention devoted to models that learn to jointly infer an expression's syntactic structure and its semantics. Yet, \citet{NangiaB18} has recently shown that the current best systems fail to learn the correct parsing strategy on mathematical expressions generated from a simple context-free grammar. In this work, we present a recursive model inspired by \newcite{ChoiYL18} that reaches near perfect accuracy on this task. Our model is composed of two separated modules for syntax and semantics. They are cooperatively trained with standard continuous and discrete optimization schemes. Our model does not require any linguistic structure for supervision and its recursive nature allows for out-of-domain generalization with little loss in performance. Additionally, our approach performs competitively on several natural language tasks, such as Natural Language Inference or Sentiment Analysis.
A Review on Automatic License Plate Recognition System
Authors: Satadal Saha
Date: 2019-02-25 00:00:00
Tasks: License Plate Recognition, Segmentation

Automatic License Plate Recognition (ALPR) is a challenging problem to the
research community due to its potential applicability in the diverse
geographical condition over the globe with varying license plate parameters.
Any ALPR system includes three main modules, viz. localization of the license
plate, segmentation of the characters therein and recognition of the segmented
characters. In real life applications where the images are captured over days
and nights in an outdoor environment with varying lighting and weather
conditions, varying pollution level and wind turbulences, localization,
segmentation and recognition become challenging tasks. The tasks become more
complex if the license plate is not in conformity with the standards laid by
corresponding Motor Vehicles Department in terms of various features, e.g. area
and aspect ratio of the license plate, background color, foreground color,
shape, number of lines, font face/ size of characters, spacing between
characters etc. Besides, license plates are often dirty or broken or having
scratches or bent or tilted at its position. All these add to the challenges in
developing an effective ALPR system.
Data augmentation using learned transformations for one-shot medical image segmentation
Authors: Amy Zhao, Guha Balakrishnan, Frédo Durand, John V. Guttag, Adrian V. Dalca
Date: 2019-02-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Anatomy, Data Augmentation, Image Segmentation, Medical Image Segmentation, Semantic Segmentation

Image segmentation is an important task in many medical applications. Methods
based on convolutional neural networks attain state-of-the-art accuracy;
however, they typically rely on supervised training with large labeled
datasets. Labeling medical images requires significant expertise and time, and
typical hand-tuned approaches for data augmentation fail to capture the complex
variations in such images.
  We present an automated data augmentation method for synthesizing labeled
medical images. We demonstrate our method on the task of segmenting magnetic
resonance imaging (MRI) brain scans. Our method requires only a single
segmented scan, and leverages other unlabeled scans in a semi-supervised
approach. We learn a model of transformations from the images, and use the
model along with the labeled example to synthesize additional labeled examples.
Each transformation is comprised of a spatial deformation field and an
intensity change, enabling the synthesis of complex effects such as variations
in anatomy and image acquisition procedures. We show that training a supervised
segmenter with these new examples provides significant improvements over
state-of-the-art methods for one-shot biomedical image segmentation. Our code
is available at https://github.com/xamyzhao/brainstorm.
EAT: a simple and versatile semantic representation format for multi-purpose NLP
Authors: Tommi Gröndahl
Date: 2019-02-25 00:00:00
Tasks: Abstract Meaning Representation, Decoder, Machine Translation, Style Transfer, Text Generation, Unsupervised Machine Translation

Semantic representations are central in many NLP tasks that require human-interpretable data. The conjunctivist framework - primarily developed by Pietroski (2005, 2018) - obtains expressive representations with only a few basic semantic types and relations systematically linked to syntactic positions. While representational simplicity is crucial for computational applications, such findings have not yet had major influence on NLP. We present the first generic semantic representation format for NLP directly based on these insights. We name the format EAT due to its basis in the Event-, Agent-, and Theme arguments in Neo-Davidsonian logical forms. It builds on the idea that similar tripartite argument relations are ubiquitous across categories, and can be constructed from grammatical structure without additional lexical information. We present a detailed exposition of EAT and how it relates to other prevalent formats used in prior work, such as Abstract Meaning Representation (AMR) and Minimal Recursion Semantics (MRS). EAT stands out in two respects: simplicity and versatility. Uniquely, EAT discards semantic metapredicates, and instead represents semantic roles entirely via positional encoding. This is made possible by limiting the number of roles to only three; a major decrease from the many dozens recognized in e.g. AMR and MRS. EAT's simplicity makes it exceptionally versatile in application. First, we show that drastically reducing semantic roles based on EAT benefits text generation from MRS in the test settings of Hajdik et al. (2019). Second, we implement the derivation of EAT from a syntactic parse, and apply this for parallel corpus generation between grammatical classes. Third, we train an encoder-decoder LSTM network to map EAT to English. Finally, we use both the encoder-decoder network and a rule-based alternative to conduct grammatical transformation from EAT-input.
Dual Attention Networks for Visual Reference Resolution in Visual Dialog
Authors: Gi-Cheon Kang, Jaeseo Lim, Byoung-Tak Zhang
Date: 2019-02-25 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: AI Agent, Question Answering, Visual Dialog, Visual Grounding, Visual Question Answering, Visual Question Answering (VQA)

Visual dialog (VisDial) is a task which requires an AI agent to answer a series of questions grounded in an image. Unlike in visual question answering (VQA), the series of questions should be able to capture a temporal context from a dialog history and exploit visually-grounded information. A problem called visual reference resolution involves these challenges, requiring the agent to resolve ambiguous references in a given question and find the references in a given image. In this paper, we propose Dual Attention Networks (DAN) for visual reference resolution. DAN consists of two kinds of attention networks, REFER and FIND. Specifically, REFER module learns latent relationships between a given question and a dialog history by employing a self-attention mechanism. FIND module takes image features and reference-aware representations (i.e., the output of REFER module) as input, and performs visual grounding via bottom-up attention mechanism. We qualitatively and quantitatively evaluate our model on VisDial v1.0 and v0.9 datasets, showing that DAN outperforms the previous state-of-the-art model by a significant margin.
Session-based Social Recommendation via Dynamic Graph Attention Networks
Authors: Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian Tang
Date: 2019-02-25 00:00:00
Tasks: Graph Attention, Recommendation Systems

Online communities such as Facebook and Twitter are enormously popular and
have become an essential part of the daily life of many of their users. Through
these platforms, users can discover and create information that others will
then consume. In that context, recommending relevant information to users
becomes critical for viability. However, recommendation in online communities
is a challenging problem: 1) users' interests are dynamic, and 2) users are
influenced by their friends. Moreover, the influencers may be
context-dependent. That is, different friends may be relied upon for different
topics. Modeling both signals is therefore essential for recommendations.
  We propose a recommender system for online communities based on a
dynamic-graph-attention neural network. We model dynamic user behaviors with a
recurrent neural network, and context-dependent social influence with a
graph-attention neural network, which dynamically infers the influencers based
on users' current interests. The whole model can be efficiently fit on
large-scale data. Experimental results on several real-world data sets
demonstrate the effectiveness of our proposed approach over several competitive
baselines including state-of-the-art models.
Anytime Heuristic for Weighted Matching Through Altruism-Inspired Behavior
Authors: Panayiotis Danassis, Aris Filos-Ratsikas, Boi Faltings
Date: 2019-02-25 00:00:00

We present a novel anytime heuristic (ALMA), inspired by the human principle
of altruism, for solving the assignment problem. ALMA is decentralized,
completely uncoupled, and requires no communication between the participants.
We prove an upper bound on the convergence speed that is polynomial in the
desired number of resources and competing agents per resource; crucially, in
the realistic case where the aforementioned quantities are bounded
independently of the total number of agents/resources, the convergence time
remains constant as the total problem size increases.
  We have evaluated ALMA under three test cases: (i) an anti-coordination
scenario where agents with similar preferences compete over the same set of
actions, (ii) a resource allocation scenario in an urban environment, under a
constant-time constraint, and finally, (iii) an on-line matching scenario using
real passenger-taxi data. In all of the cases, ALMA was able to reach high
social welfare, while being orders of magnitude faster than the centralized,
optimal algorithm. The latter allows our algorithm to scale to realistic
scenarios with hundreds of thousands of agents, e.g., vehicle coordination in
urban environments.
CFM-BD: a distributed rule induction algorithm for building Compact Fuzzy Models in Big Data classification problems
Authors: Mikel Elkano, Jose Sanz, Edurne Barrenechea, Humberto Bustince, Mikel Galar
Date: 2019-02-25 00:00:00
Tasks: General Classification

Interpretability has always been a major concern for fuzzy rule-based
classifiers. The usage of human-readable models allows them to explain the
reasoning behind their predictions and decisions. However, when it comes to Big
Data classification problems, fuzzy rule-based classifiers have not been able
to maintain the good trade-off between accuracy and interpretability that has
characterized these techniques in non-Big Data environments. The most accurate
methods build too complex models composed of a large number of rules and fuzzy
sets, while those approaches focusing on interpretability do not provide
state-of-the-art discrimination capabilities. In this paper, we propose a new
distributed learning algorithm named CFM-BD to construct accurate and compact
fuzzy rule-based classification systems for Big Data. This method has been
specifically designed from scratch for Big Data problems and does not adapt or
extend any existing algorithm. The proposed learning process consists of three
stages: 1) pre-processing based on the probability integral transform theorem;
2) rule induction inspired by CHI-BD and Apriori algorithms; 3) rule selection
by means of a global evolutionary optimization. We conducted a complete
empirical study to test the performance of our approach in terms of accuracy,
complexity, and runtime. The results obtained were compared and contrasted with
four state-of-the-art fuzzy classifiers for Big Data (FBDT, FMDT, Chi-Spark-RS,
and CHI-BD). According to this study, CFM-BD is able to provide competitive
discrimination capabilities using significantly simpler models composed of a
few rules of less than 3 antecedents, employing 5 linguistic labels for all
variables.
Liability, Ethics, and Culture-Aware Behavior Specification using Rulebooks
Authors: Andrea Censi, Konstantin Slutsky, Tichakorn Wongpiromsarn, Dmitry Yershov, Scott Pendleton, James Fu, Emilio Frazzoli
Date: 2019-02-25 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Ethics, Self-Driving Cars

The behavior of self-driving cars must be compatible with an enormous set of
conflicting and ambiguous objectives, from law, from ethics, from the local
culture, and so on. This paper describes a new way to conveniently define the
desired behavior for autonomous agents, which we use on the self-driving cars
developed at nuTonomy. We define a "rulebook" as a pre-ordered set of "rules",
each akin to a violation metric on the possible outcomes ("realizations"). The
rules are partially ordered by priority. The semantics of a rulebook imposes a
pre-order on the set of realizations. We study the compositional properties of
the rulebooks, and we derive which operations we can allow on the rulebooks to
preserve previously-introduced constraints. While we demonstrate the
application of these techniques in the self-driving domain, the methods are
domain-independent.
Efficient Path Prediction for Semi-Supervised and Weakly Supervised Hierarchical Text Classification
Authors: Huiru Xiao, Xin Liu, Yangqiu Song
Date: 2019-02-25 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification, Weakly-supervised Learning

Hierarchical text classification has many real-world applications. However,
labeling a large number of documents is costly. In practice, we can use
semi-supervised learning or weakly supervised learning (e.g., dataless
classification) to reduce the labeling cost. In this paper, we propose a path
cost-sensitive learning algorithm to utilize the structural information and
further make use of unlabeled and weakly-labeled data. We use a generative
model to leverage the large amount of unlabeled data and introduce path
constraints into the learning algorithm to incorporate the structural
information of the class hierarchy. The posterior probabilities of both
unlabeled and weakly labeled data can be incorporated with path-dependent
scores. Since we put a structure-sensitive cost to the learning algorithm to
constrain the classification consistent with the class hierarchy and do not
need to reconstruct the feature vectors for different structures, we can
significantly reduce the computational cost compared to structural output
learning. Experimental results on two hierarchical text classification
benchmarks show that our approach is not only effective but also efficient to
handle the semi-supervised and weakly supervised hierarchical text
classification.
Similarity Measures based on Local Game Trees
Authors: Sabrina Evans, Paolo Turrini
Date: 2019-02-25 00:00:00

We study strategic similarity of game positions in two-player extensive games
of perfect information, by looking at the structure of their local game trees,
with the aim of improving the performance of game playing agents in detecting
forcing continuations. We present a range of measures over the induced game
trees and compare them against benchmark problems in chess, observing a
promising level of accuracy in matching up trap states.
Making History Matter: History-Advantage Sequence Training for Visual Dialog
Authors: Tianhao Yang, Zheng-Jun Zha, Hanwang Zhang
Date: 2019-02-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: Answer Generation, Decoder, Reinforcement Learning, Response Generation, Triplet, Visual Dialog, Visual Reasoning

We study the multi-round response generation in visual dialog, where a
response is generated according to a visually grounded conversational history.
Given a triplet: an image, Q&A history, and current question, all the
prevailing methods follow a codec (i.e., encoder-decoder) fashion in a
supervised learning paradigm: a multimodal encoder encodes the triplet into a
feature vector, which is then fed into the decoder for the current answer
generation, supervised by the ground-truth. However, this conventional
supervised learning does NOT take into account the impact of imperfect history,
violating the conversational nature of visual dialog and thus making the codec
more inclined to learn history bias but not contextual reasoning. To this end,
inspired by the actor-critic policy gradient in reinforcement learning, we
propose a novel training paradigm called History Advantage Sequence Training
(HAST). Specifically, we intentionally impose wrong answers in the history,
obtaining an adverse critic, and see how the historic error impacts the codec's
future behavior by History Advantage-a quantity obtained by subtracting the
adverse critic from the gold reward of ground-truth history. Moreover, to make
the codec more sensitive to the history, we propose a novel attention network
called History-Aware Co-Attention Network (HACAN) which can be effectively
trained by using HAST. Experimental results on three benchmarks: VisDial
v0.9&v1.0 and GuessWhat?!, show that the proposed HAST strategy consistently
outperforms the state-of-the-art supervised counterparts.
Wasserstein-Wasserstein Auto-Encoders
Authors: Shunkang Zhang, Yuan Gao, Yuling Jiao, Jin Liu, Yang Wang, Can Yang
Date: 2019-02-25 00:00:00

To address the challenges in learning deep generative models (e.g.,the
blurriness of variational auto-encoder and the instability of training
generative adversarial networks, we propose a novel deep generative model,
named Wasserstein-Wasserstein auto-encoders (WWAE). We formulate WWAE as
minimization of the penalized optimal transport between the target distribution
and the generated distribution. By noticing that both the prior $P_Z$ and the
aggregated posterior $Q_Z$ of the latent code Z can be well captured by
Gaussians, the proposed WWAE utilizes the closed-form of the squared
Wasserstein-2 distance for two Gaussians in the optimization process. As a
result, WWAE does not suffer from the sampling burden and it is computationally
efficient by leveraging the reparameterization trick. Numerical results
evaluated on multiple benchmark datasets including MNIST, fashion- MNIST and
CelebA show that WWAE learns better latent structures than VAEs and generates
samples of better visual quality and higher FID scores than VAEs and GANs.