pwc-archive/papers-with-abstracts huggingface dataset:
Semi-Supervised Sequence Modeling with Cross-View Training
Authors: Kevin Clark, Minh-Thang Luong, Christopher D. Manning, Quoc V. Le
Date: 2018-09-22 00:00:00
Proceeding: EMNLP 2018 10
Tasks: CCG Supertagging, Dependency Parsing, Machine Translation, Multi-Task Learning, Named Entity Recognition (NER), Part-Of-Speech Tagging, Representation Learning, Sentence, Translation

Unsupervised representation learning algorithms such as word2vec and ELMo
improve the accuracy of many supervised NLP models, mainly because they can
take advantage of large amounts of unlabeled text. However, the supervised
models only learn from task-specific labeled data during the main training
phase. We therefore propose Cross-View Training (CVT), a semi-supervised
learning algorithm that improves the representations of a Bi-LSTM sentence
encoder using a mix of labeled and unlabeled data. On labeled examples,
standard supervised learning is used. On unlabeled examples, CVT teaches
auxiliary prediction modules that see restricted views of the input (e.g., only
part of a sentence) to match the predictions of the full model seeing the whole
input. Since the auxiliary modules and the full model share intermediate
representations, this in turn improves the full model. Moreover, we show that
CVT is particularly effective when combined with multi-task learning. We
evaluate CVT on five sequence tagging tasks, machine translation, and
dependency parsing, achieving state-of-the-art results.
Comment on "All-optical machine learning using diffractive deep neural networks"
Authors: Haiqing Wei, Gang Huang, Xiuqing Wei, Yanlong Sun, Hongbin Wang
Date: 2018-09-22 00:00:00
Tasks: All, BIG-bench Machine Learning

Lin et al. (Reports, 7 September 2018, p. 1004) reported a remarkable
proposal that employs a passive, strictly linear optical setup to perform
pattern classifications. But interpreting the multilayer diffractive setup as a
deep neural network and advocating it as an all-optical deep learning framework
are not well justified and represent a mischaracterization of the system by
overlooking its defining characteristics of perfect linearity and strict
passivity.
Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection
Authors: Vassilis N. Ioannidis, Ahmed S. Zamzam, Georgios B. Giannakis, Nicholas D. Sidiropoulos
Date: 2018-09-22 00:00:00
Tasks: Community Detection, Imputation, Recommendation Systems

Joint analysis of data from multiple information repositories facilitates uncovering the underlying structure in heterogeneous datasets. Single and coupled matrix-tensor factorization (CMTF) has been widely used in this context for imputation-based recommendation from ratings, social network, and other user-item data. When this side information is in the form of item-item correlation matrices or graphs, existing CMTF algorithms may fall short. Alleviating current limitations, we introduce a novel model coined coupled graph-tensor factorization (CGTF) that judiciously accounts for graph-related side information. The CGTF model has the potential to overcome practical challenges, such as missing slabs from the tensor and/or missing rows/columns from the correlation matrices. A novel alternating direction method of multipliers (ADMM) is also developed that recovers the nonnegative factors of CGTF. Our algorithm enjoys closed-form updates that result in reduced computational complexity and allow for convergence claims. A novel direction is further explored by employing the interpretable factors to detect graph communities having the tensor as side information. The resulting community detection approach is successful even when some links in the graphs are missing. Results with real data sets corroborate the merits of the proposed methods relative to state-of-the-art competing factorization techniques in providing recommendations and detecting communities.
Unrestricted Adversarial Examples
Authors: Tom B. Brown, Nicholas Carlini, Chiyuan Zhang, Catherine Olsson, Paul Christiano, Ian Goodfellow
Date: 2018-09-22 00:00:00
Tasks: BIG-bench Machine Learning

We introduce a two-player contest for evaluating the safety and robustness of
machine learning systems, with a large prize pool. Unlike most prior work in ML
robustness, which studies norm-constrained adversaries, we shift our focus to
unconstrained adversaries. Defenders submit machine learning models, and try to
achieve high accuracy and coverage on non-adversarial data while making no
confident mistakes on adversarial inputs. Attackers try to subvert defenses by
finding arbitrary unambiguous inputs where the model assigns an incorrect label
with high confidence. We propose a simple unambiguous dataset ("bird-or-
bicycle") to use as part of this contest. We hope this contest will help to
more comprehensively evaluate the worst-case adversarial risk of machine
learning models.
CPMetric: Deep Siamese Networks for Learning Distances Between Structured Preferences
Authors: Andrea Loreggia, Nicholas Mattei, Francesca Rossi, K. Brent Venable
Date: 2018-09-21 00:00:00
Tasks: Decision Making, Metric Learning

Preference are central to decision making by both machines and humans. Representing, learning, and reasoning with preferences is an important area of study both within computer science and across the sciences. When working with preferences it is necessary to understand and compute the distance between sets of objects, e.g., the preferences of a user and a the descriptions of objects to be recommended. We present CPDist, a novel neural network to address the problem of learning to measure the distance between structured preference representations. We use the popular CP-net formalism to represent preferences and then leverage deep neural networks to learn a recently proposed metric function that is computationally hard to compute directly. CPDist is a novel metric learning approach based on the use of deep siamese networks which learn the Kendal Tau distance between partial orders that are induced by compact preference representations. We find that CPDist is able to learn the distance function with high accuracy and outperform existing approximation algorithms on both the regression and classification task using less computation time. Performance remains good even when CPDist is trained with only a small number of samples compared to the dimension of the solution space, indicating the network generalizes well.
A Meta-Learning Approach for Custom Model Training
Authors: Amir Erfan Eshratifar, Mohammad Saeed Abrishami, David Eigen, Massoud Pedram
Date: 2018-09-21 00:00:00
Tasks: Meta-Learning, model, Transfer Learning

Transfer-learning and meta-learning are two effective methods to apply
knowledge learned from large data sources to new tasks. In few-class, few-shot
target task settings (i.e. when there are only a few classes and training
examples available in the target task), meta-learning approaches that optimize
for future task learning have outperformed the typical transfer approach of
initializing model weights from a pre-trained starting point. But as we
experimentally show, meta-learning algorithms that work well in the few-class
setting do not generalize well in many-shot and many-class cases. In this
paper, we propose a joint training approach that combines both
transfer-learning and meta-learning. Benefiting from the advantages of each,
our method obtains improved generalization performance on unseen target tasks
in both few- and many-class and few- and many-shot scenarios.
Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration
Authors: Ritesh Noothigattu, Djallel Bouneffouf, Nicholas Mattei, Rachita Chandra, Piyush Madan, Kush Varshney, Murray Campbell, Moninder Singh, Francesca Rossi
Date: 2018-09-21 00:00:00
Tasks: Multi-Objective Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Autonomous cyber-physical agents and systems play an increasingly large role
in our lives. To ensure that agents behave in ways aligned with the values of
the societies in which they operate, we must develop techniques that allow
these agents to not only maximize their reward in an environment, but also to
learn and follow the implicit constraints of society. These constraints and
norms can come from any number of sources including regulations, business
process guidelines, laws, ethical principles, social norms, and moral values.
We detail a novel approach that uses inverse reinforcement learning to learn a
set of unspecified constraints from demonstrations of the task, and
reinforcement learning to learn to maximize the environment rewards. More
precisely, we assume that an agent can observe traces of behavior of members of
the society but has no access to the explicit set of constraints that give rise
to the observed behavior. Inverse reinforcement learning is used to learn such
constraints, that are then combined with a possibly orthogonal value function
through the use of a contextual bandit-based orchestrator that picks a
contextually-appropriate choice between the two policies (constraint-based and
environment reward-based) when taking actions. The contextual bandit
orchestrator allows the agent to mix policies in novel ways, taking the best
actions from either a reward maximizing or constrained policy. In addition, the
orchestrator is transparent on which policy is being employed at each time
step. We test our algorithms using a Pac-Man domain and show that the agent is
able to learn to act optimally, act within the demonstrated constraints, and
mix these two functions in complex ways.
Unsupervised Image to Sequence Translation with Canvas-Drawer Networks
Authors: Kevin Frans, Chin-Yi Cheng
Date: 2018-09-21 00:00:00
Tasks: Image Segmentation, Semantic Segmentation, Translation

Encoding images as a series of high-level constructs, such as brush strokes
or discrete shapes, can often be key to both human and machine understanding.
In many cases, however, data is only available in pixel form. We present a
method for generating images directly in a high-level domain (e.g. brush
strokes), without the need for real pairwise data. Specifically, we train a
"canvas" network to imitate the mapping of high-level constructs to pixels,
followed by a high-level "drawing" network which is optimized through this
mapping towards solving a desired image recreation or translation task. We
successfully discover sequential vector representations of symbols, large
sketches, and 3D objects, utilizing only pixel data. We display applications of
our method in image segmentation, and present several ablation studies
comparing various configurations.
Adversarial Recommendation: Attack of the Learned Fake Users
Authors: Konstantina Christakopoulou, Arindam Banerjee
Date: 2018-09-21 00:00:00
Tasks: Recommendation Systems

Can machine learning models for recommendation be easily fooled? While the
question has been answered for hand-engineered fake user profiles, it has not
been explored for machine learned adversarial attacks. This paper attempts to
close this gap.
  We propose a framework for generating fake user profiles which, when
incorporated in the training of a recommendation system, can achieve an
adversarial intent, while remaining indistinguishable from real user profiles.
We formulate this procedure as a repeated general-sum game between two players:
an oblivious recommendation system $R$ and an adversarial fake user generator
$A$ with two goals: (G1) the rating distribution of the fake users needs to be
close to the real users, and (G2) some objective $f_A$ encoding the attack
intent, such as targeting the top-K recommendation quality of $R$ for a subset
of users, needs to be optimized. We propose a learning framework to achieve
both goals, and offer extensive experiments considering multiple types of
attacks highlighting the vulnerability of recommendation systems.
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
Authors: Dongkun Zhang, Lu Lu, Ling Guo, George Em. Karniadakis
Date: 2018-09-21 00:00:00
Tasks: Active Learning, Uncertainty Quantification

Physics-informed neural networks (PINNs) have recently emerged as an
alternative way of solving partial differential equations (PDEs) without the
need of building elaborate grids, instead, using a straightforward
implementation. In particular, in addition to the deep neural network (DNN) for
the solution, a second DNN is considered that represents the residual of the
PDE. The residual is then combined with the mismatch in the given data of the
solution in order to formulate the loss function. This framework is effective
but is lacking uncertainty quantification of the solution due to the inherent
randomness in the data or due to the approximation limitations of the DNN
architecture. Here, we propose a new method with the objective of endowing the
DNN with uncertainty quantification for both sources of uncertainty, i.e., the
parametric uncertainty and the approximation uncertainty. We first account for
the parametric uncertainty when the parameter in the differential equation is
represented as a stochastic process. Multiple DNNs are designed to learn the
modal functions of the arbitrary polynomial chaos (aPC) expansion of its
solution by using stochastic data from sparse sensors. We can then make
predictions from new sensor measurements very efficiently with the trained
DNNs. Moreover, we employ dropout to correct the over-fitting and also to
quantify the uncertainty of DNNs in approximating the modal functions. We then
design an active learning strategy based on the dropout uncertainty to place
new sensors in the domain to improve the predictions of DNNs. Several numerical
tests are conducted for both the forward and the inverse problems to quantify
the effectiveness of PINNs combined with uncertainty quantification. This
NN-aPC new paradigm of physics-informed deep learning with uncertainty
quantification can be readily applied to other types of stochastic PDEs in
multi-dimensions.
Recurrent Flow-Guided Semantic Forecasting
Authors: Adam M. Terwilliger, Garrick Brazil, Xiaoming Liu
Date: 2018-09-21 00:00:00
Tasks: Autonomous Vehicles, Optical Flow Estimation, Prediction

Understanding the world around us and making decisions about the future is a
critical component to human intelligence. As autonomous systems continue to
develop, their ability to reason about the future will be the key to their
success. Semantic anticipation is a relatively under-explored area for which
autonomous vehicles could take advantage of (e.g., forecasting pedestrian
trajectories). Motivated by the need for real-time prediction in autonomous
systems, we propose to decompose the challenging semantic forecasting task into
two subtasks: current frame segmentation and future optical flow prediction.
Through this decomposition, we built an efficient, effective, low overhead
model with three main components: flow prediction network, feature-flow
aggregation LSTM, and end-to-end learnable warp layer. Our proposed method
achieves state-of-the-art accuracy on short-term and moving objects semantic
forecasting while simultaneously reducing model parameters by up to 95% and
increasing efficiency by greater than 40x.
Temporal Interpolation as an Unsupervised Pretraining Task for Optical Flow Estimation
Authors: Jonas Wulff, Michael J. Black
Date: 2018-09-21 00:00:00
Tasks: Optical Flow Estimation, Unsupervised Pre-training

The difficulty of annotating training data is a major obstacle to using CNNs
for low-level tasks in video. Synthetic data often does not generalize to real
videos, while unsupervised methods require heuristic losses. Proxy tasks can
overcome these issues, and start by training a network for a task for which
annotation is easier or which can be trained unsupervised. The trained network
is then fine-tuned for the original task using small amounts of ground truth
data. Here, we investigate frame interpolation as a proxy task for optical
flow. Using real movies, we train a CNN unsupervised for temporal
interpolation. Such a network implicitly estimates motion, but cannot handle
untextured regions. By fine-tuning on small amounts of ground truth flow, the
network can learn to fill in homogeneous regions and compute full optical flow
fields. Using this unsupervised pre-training, our network outperforms similar
architectures that were trained supervised using synthetic optical flow.
onlineSPARC: a Programming Environment for Answer Set Programming
Authors: Elias Marcopoulos, Yuanlin Zhang
Date: 2018-09-21 00:00:00

Recent progress in logic programming (e.g., the development of the Answer Set
Programming paradigm) has made it possible to teach it to general undergraduate
and even middle/high school students. Given the limited exposure of these
students to computer science, the complexity of downloading, installing and
using tools for writing logic programs could be a major barrier for logic
programming to reach a much wider audience. We developed onlineSPARC, an online
answer set programming environment with a self contained file system and a
simple interface. It allows users to type/edit logic programs and perform
several tasks over programs, including asking a query to a program, getting the
answer sets of a program, and producing a drawing/animation based on the answer
sets of a program.
How do you correct run-on sentences it's not as easy as it seems
Authors: Junchao Zheng, Courtney Napoles, Joel Tetreault, Kostiantyn Omelianchuk
Date: 2018-09-21 00:00:00
Proceeding: WS 2018 11
Tasks: BIG-bench Machine Learning, Grammatical Error Correction, Punctuation Restoration, Sentence

Run-on sentences are common grammatical mistakes but little research has
tackled this problem to date. This work introduces two machine learning models
to correct run-on sentences that outperform leading methods for related tasks,
punctuation restoration and whole-sentence grammatical error correction. Due to
the limited annotated data for this error, we experiment with artificially
generating training data from clean newswire text. Our findings suggest
artificial training data is viable for this task. We discuss implications for
correcting run-ons and other types of mistakes that have low coverage in
error-annotated corpora.
Opacity, Obscurity, and the Geometry of Question-Asking
Authors: Christina Boyce-Jacino, Simon DeDeo
Date: 2018-09-21 00:00:00
Tasks: Navigate

Asking questions is a pervasive human activity, but little is understood
about what makes them difficult to answer. An analysis of a pair of large
databases, of New York Times crosswords and questions from the quiz-show
Jeopardy, establishes two orthogonal dimensions of question difficulty:
obscurity (the rarity of the answer) and opacity (the indirectness of question
cues, operationalized with word2vec). The importance of opacity, and the role
of synergistic information in resolving it, suggests that accounts of
difficulty in terms of prior expectations captures only a part of the
question-asking process. A further regression analysis shows the presence of
additional dimensions to question-asking: question complexity, the answer's
local network density, cue intersection, and the presence of signal words. Our
work shows how question-askers can help their interlocutors by using contextual
cues, or, conversely, how a particular kind of unfamiliarity with the domain in
question can make it harder for individuals to learn from others. Taken
together, these results suggest how Bayesian models of question difficulty can
be supplemented by process models and accounts of the heuristics individuals
use to navigate conceptual spaces.
GAPLE: Generalizable Approaching Policy LEarning for Robotic Object Searching in Indoor Environment
Authors: Xin Ye, Zhe Lin, Joon-Young Lee, Jianming Zhang, Shibin Zheng, Yezhou Yang
Date: 2018-09-21 00:00:00
Tasks: Semantic Segmentation, Visual Navigation

We study the problem of learning a generalizable action policy for an
intelligent agent to actively approach an object of interest in an indoor
environment solely from its visual inputs. While scene-driven or
recognition-driven visual navigation has been widely studied, prior efforts
suffer severely from the limited generalization capability. In this paper, we
first argue the object searching task is environment dependent while the
approaching ability is general. To learn a generalizable approaching policy, we
present a novel solution dubbed as GAPLE which adopts two channels of visual
features: depth and semantic segmentation, as the inputs to the policy learning
module. The empirical studies conducted on the House3D dataset as well as on a
physical platform in a real world scenario validate our hypothesis, and we
further provide in-depth qualitative analysis.
Neural Approaches to Conversational AI
Authors: Jianfeng Gao, Michel Galley, Lihong Li
Date: 2018-09-21 00:00:00
Proceeding: ACL 2018 7
Tasks: Question Answering

The present paper surveys neural approaches to conversational AI that have been developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) chatbots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between them and traditional approaches, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies.
Global Weighted Average Pooling Bridges Pixel-level Localization and Image-level Classification
Authors: Suo Qiu
Date: 2018-09-21 00:00:00
Tasks: General Classification, image-classification, Image Classification, object-detection, Object Detection, Transfer Learning

In this work, we first tackle the problem of simultaneous pixel-level
localization and image-level classification with only image-level labels for
fully convolutional network training. We investigate the global pooling method
which plays a vital role in this task. Classical global max pooling and average
pooling methods are hard to indicate the precise regions of objects. Therefore,
we revisit the global weighted average pooling (GWAP) method for this task and
propose the class-agnostic GWAP module and the class-specific GWAP module in
this paper. We evaluate the classification and pixel-level localization ability
on the ILSVRC benchmark dataset. Experimental results show that the proposed
GWAP module can better capture the regions of the foreground objects. We
further explore the knowledge transfer between the image classification task
and the region-based object detection task. We propose a multi-task framework
that combines our class-specific GWAP module with R-FCN. The framework is
trained with few ground truth bounding boxes and large-scale image-level
labels. We evaluate this framework on PASCAL VOC dataset. Experimental results
show that this framework can use the data with only image-level labels to
improve the generalization of the object detection model.
Lexical Bias In Essay Level Prediction
Authors: Georgios Balikas
Date: 2018-09-21 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering, Model Selection, Prediction

Automatically predicting the level of non-native English speakers given their
written essays is an interesting machine learning problem. In this work I
present the system "balikasg" that achieved the state-of-the-art performance in
the CAp 2018 data science challenge among 14 systems. I detail the feature
extraction, feature engineering and model selection steps and I evaluate how
these decisions impact the system's performance. The paper concludes with
remarks for future work.