pwc-archive/papers-with-abstracts huggingface dataset:
Deep Determinantal Point Processes
Authors: Mike Gartrell, Elvis Dohmatob, Jon Alberdi
Date: 2018-11-17 00:00:00
Tasks: Diversity, Point Processes

Determinantal point processes (DPPs) have attracted significant attention as an elegant model that is able to capture the balance between quality and diversity within sets. DPPs are parameterized by a positive semi-definite kernel matrix. While DPPs have substantial expressive power, they are fundamentally limited by the parameterization of the kernel matrix and their inability to capture nonlinear interactions between items within sets. We present the deep DPP model as way to address these limitations, by using a deep feed-forward neural network to learn the kernel matrix. In addition to allowing us to capture nonlinear item interactions, the deep DPP also allows easy incorporation of item metadata into DPP learning. Since the learning target is the DPP kernel matrix, the deep DPP allows us to use existing DPP algorithms for efficient learning, sampling, and prediction. Through an evaluation on several real-world datasets, we show experimentally that the deep DPP can provide a considerable improvement in the predictive performance of DPPs, while also outperforming strong baseline models in many cases.
Dynamic Social Interaction Mechanics CrossAnt
Authors: Samuel Gomes, Carlos Martinho, João Dias
Date: 2018-11-17 00:00:00

Nowadays, big effort is being put to study gamification and how game elements
can be used to engage players. In this scope, we believe there is a growing
need to explore the impact game mechanics have on the players' interactions and
perception. This work focuses on the application of game mechanics to lead
players to achieve certain types of social interaction (we named this type of
mechanics social interaction mechanics). A word matching game called CrossAnt
was modified so that it could dynamically generate different social interaction
mechanics. These mechanics consisted in different key combinations needed to
play the game and were aimed to promote what we think are three important types
of social interactions: cooperation, competition and individual exploration.
Our evaluation consisted on the execution of several sessions where two players
interacted with the game for several levels and had to find for themselves how
to perform the actions needed to succeed. While some of the levels required the
input from both players in order to be completed, others could be completed by
each player independently. Our results show that cooperation was perceived when
both players had to intervene to perform the game actions. However, longer
interactions may still be needed so that the other types of interactions are
promoted.
Representation Mixing for TTS Synthesis
Authors: Kyle Kastner, João Felipe Santos, Yoshua Bengio, Aaron Courville
Date: 2018-11-17 00:00:00

Recent character and phoneme-based parametric TTS systems using deep learning
have shown strong performance in natural speech generation. However, the choice
between character or phoneme input can create serious limitations for practical
deployment, as direct control of pronunciation is crucial in certain cases. We
demonstrate a simple method for combining multiple types of linguistic
information in a single encoder, named representation mixing, enabling flexible
choice between character, phoneme, or mixed representations during inference.
Experiments and user studies on a public audiobook corpus show the efficacy of
our approach.
Robust cross-domain disfluency detection with pattern match networks
Authors: Vicky Zayats, Mari Ostendorf
Date: 2018-11-17 00:00:00
Tasks: Feature Engineering

In this paper we introduce a novel pattern match neural network architecture
that uses neighbor similarity scores as features, eliminating the need for
feature engineering in a disfluency detection task. We evaluate the approach in
disfluency detection for four different speech genres, showing that the
approach is as effective as hand-engineered pattern match features when used on
in-domain data and achieves superior performance in cross-domain scenarios.
Improving Automatic Source Code Summarization via Deep Reinforcement Learning
Authors: Yao Wan, Zhou Zhao, Min Yang, Guandong Xu, Haochao Ying, Jian Wu, Philip S. Yu
Date: 2018-11-17 00:00:00
Tasks: Code Summarization, Decoder, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval, Source Code Summarization

Code summarization provides a high level natural language description of the
function performed by code, as it can benefit the software maintenance, code
categorization and retrieval. To the best of our knowledge, most
state-of-the-art approaches follow an encoder-decoder framework which encodes
the code into a hidden space and then decode it into natural language space,
suffering from two major drawbacks: a) Their encoders only consider the
sequential content of code, ignoring the tree structure which is also critical
for the task of code summarization, b) Their decoders are typically trained to
predict the next word by maximizing the likelihood of next ground-truth word
with previous ground-truth word given. However, it is expected to generate the
entire sequence from scratch at test time. This discrepancy can cause an
\textit{exposure bias} issue, making the learnt decoder suboptimal. In this
paper, we incorporate an abstract syntax tree structure as well as sequential
content of code snippets into a deep reinforcement learning framework (i.e.,
actor-critic network). The actor network provides the confidence of predicting
the next word according to current state. On the other hand, the critic network
evaluates the reward value of all possible extensions of the current state and
can provide global guidance for explorations. We employ an advantage reward
composed of BLEU metric to train both networks. Comprehensive experiments on a
real-world dataset show the effectiveness of our proposed model when compared
with some state-of-the-art methods.
Learning Features and Abstract Actions for Computing Generalized Plans
Authors: Blai Bonet, Guillem Francès, Hector Geffner
Date: 2018-11-17 00:00:00

Generalized planning is concerned with the computation of plans that solve
not one but multiple instances of a planning domain. Recently, it has been
shown that generalized plans can be expressed as mappings of feature values
into actions, and that they can often be computed with fully observable
non-deterministic (FOND) planners. The actions in such plans, however, are not
the actions in the instances themselves, which are not necessarily common to
other instances, but abstract actions that are defined on a set of common
features. The formulation assumes that the features and the abstract actions
are given. In this work, we address this limitation by showing how to learn
them automatically. The resulting account of generalized planning combines
learning and planning in a novel way: a learner, based on a Max SAT
formulation, yields the features and abstract actions from sampled state
transitions, and a FOND planner uses this information, suitably transformed, to
produce the general plans. Correctness guarantees are given and experimental
results on several domains are reported.
Deep Dive into Anonymity: A Large Scale Analysis of Quora Questions
Authors: Binny Mathew, Ritam Dutt, Suman Kalyan Maity, Pawan Goyal, Animesh Mukherjee
Date: 2018-11-17 00:00:00

Anonymity forms an integral and important part of our digital life. It
enables us to express our true selves without the fear of judgment. In this
paper, we investigate the different aspects of anonymity in the social Q&A site
Quora. The choice of Quora is motivated by the fact that this is one of the
rare social Q&A sites that allow users to explicitly post anonymous questions
and such activity in this forum has become normative rather than a taboo.
Through an analysis of 5.1 million questions, we observe that at a global scale
almost no difference manifests between the linguistic structure of the
anonymous and the non-anonymous questions. We find that topical mixing at the
global scale to be the primary reason for the absence. However, the differences
start to feature once we "deep dive" and (topically) cluster the questions and
compare the clusters that have high volumes of anonymous questions with those
that have low volumes of anonymous questions. In particular, we observe that
the choice to post the question as anonymous is dependent on the user's
perception of anonymity and they often choose to speak about depression,
anxiety, social ties and personal issues under the guise of anonymity. We
further perform personality trait analysis and observe that the anonymous group
of users has positive correlation with extraversion, agreeableness, and
negative correlation with openness. Subsequently, to gain further insights, we
build an anonymity grid to identify the differences in the perception on
anonymity of the user posting the question and the community of users answering
it. We also look into the first response time of the questions and observe that
it is lowest for topics which talk about personal and sensitive issues, which
hints toward a higher degree of community support and user engagement.
GroundNet: Monocular Ground Plane Normal Estimation with Geometric Consistency
Authors: Yunze Man, Xinshuo Weng, Xi Li, Kris Kitani
Date: 2018-11-17 00:00:00
Tasks: Line Detection, Segmentation, Semantic Segmentation

We focus on estimating the 3D orientation of the ground plane from a single image. We formulate the problem as an inter-mingled multi-task prediction problem by jointly optimizing for pixel-wise surface normal direction, ground plane segmentation, and depth estimates. Specifically, our proposed model, GroundNet, first estimates the depth and surface normal in two separate streams, from which two ground plane normals are then computed deterministically. To leverage the geometric correlation between depth and normal, we propose to add a consistency loss on top of the computed ground plane normals. In addition, a ground segmentation stream is used to isolate the ground regions so that we can selectively back-propagate parameter updates through only the ground regions in the image. Our method achieves the top-ranked performance on ground plane normal estimation and horizon line detection on the real-world outdoor datasets of ApolloScape and KITTI, improving the performance of previous art by up to 17.7% relatively.
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
Authors: Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones, Irene Chen, Corey Chivers, Adrian Dalca, Sam Finlayson, Madalina Fiterau, Jason Alan Fries, Marzyeh Ghassemi, Mike Hughes, Bruno Jedynak, Jasvinder S. Kandola, Matthew McDermott, Tristan Naumann, Peter Schulam, Farah Shamout, Alexandre Yahi
Date: 2018-11-17 00:00:00
Tasks: BIG-bench Machine Learning

This volume represents the accepted submissions from the Machine Learning for
Health (ML4H) workshop at the conference on Neural Information Processing
Systems (NeurIPS) 2018, held on December 8, 2018 in Montreal, Canada.
Parameter Sharing Reinforcement Learning Architecture for Multi Agent Driving Behaviors
Authors: Meha Kaushik, Phaniteja S, K. Madhava Krishna
Date: 2018-11-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Multi-agent learning provides a potential framework for learning and
simulating traffic behaviors. This paper proposes a novel architecture to learn
multiple driving behaviors in a traffic scenario. The proposed architecture can
learn multiple behaviors independently as well as simultaneously. We take
advantage of the homogeneity of agents and learn in a parameter sharing
paradigm. To further speed up the training process asynchronous updates are
employed into the architecture. While learning different behaviors
simultaneously, the given framework was also able to learn cooperation between
the agents, without any explicit communication. We applied this framework to
learn two important behaviors in driving: 1) Lane-Keeping and 2) Over-Taking.
Results indicate faster convergence and learning of a more generic behavior,
that is scalable to any number of agents. When compared the results with
existing approaches, our results indicate equal and even better performance in
some cases.
Revisiting Image-Language Networks for Open-ended Phrase Detection
Authors: Bryan A. Plummer, Kevin J. Shih, Yichen Li, Ke Xu, Svetlana Lazebnik, Stan Sclaroff, Kate Saenko
Date: 2018-11-17 00:00:00
Tasks: object-detection, Object Detection, Phrase Grounding

Most existing work that grounds natural language phrases in images starts with the assumption that the phrase in question is relevant to the image. In this paper we address a more realistic version of the natural language grounding task where we must both identify whether the phrase is relevant to an image and localize the phrase. This can also be viewed as a generalization of object detection to an open-ended vocabulary, introducing elements of few- and zero-shot detection. We propose an approach for this task that extends Faster R-CNN to relate image regions and phrases. By carefully initializing the classification layers of our network using canonical correlation analysis (CCA), we encourage a solution that is more discerning when reasoning between similar phrases, resulting in over double the performance compared to a naive adaptation on three popular phrase grounding datasets, Flickr30K Entities, ReferIt Game, and Visual Genome, with test-time phrase vocabulary sizes of 5K, 32K, and 159K, respectively.
Classifiers Based on Deep Sparse Coding Architectures are Robust to Deep Learning Transferable Examples
Authors: Jacob M. Springer, Charles S. Strauss, Austin M. Thresher, Edward Kim, Garrett T. Kenyon
Date: 2018-11-17 00:00:00
Tasks: Deep Learning, General Classification

Although deep learning has shown great success in recent years, researchers
have discovered a critical flaw where small, imperceptible changes in the input
to the system can drastically change the output classification. These attacks
are exploitable in nearly all of the existing deep learning classification
frameworks. However, the susceptibility of deep sparse coding models to
adversarial examples has not been examined. Here, we show that classifiers
based on a deep sparse coding model whose classification accuracy is
competitive with a variety of deep neural network models are robust to
adversarial examples that effectively fool those same deep learning models. We
demonstrate both quantitatively and qualitatively that the robustness of deep
sparse coding models to adversarial examples arises from two key properties.
First, because deep sparse coding models learn general features corresponding
to generators of the dataset as a whole, rather than highly discriminative
features for distinguishing specific classes, the resulting classifiers are
less dependent on idiosyncratic features that might be more easily exploited.
Second, because deep sparse coding models utilize fixed point attractor
dynamics with top-down feedback, it is more difficult to find small changes to
the input that drive the resulting representations out of the correct attractor
basin.
A Statistical Approach to Assessing Neural Network Robustness
Authors: Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar
Date: 2018-11-17 00:00:00
Proceeding: ICLR 2019 5

We present a new approach to assessing the robustness of neural networks
based on estimating the proportion of inputs for which a property is violated.
Specifically, we estimate the probability of the event that the property is
violated under an input model. Our approach critically varies from the formal
verification framework in that when the property can be violated, it provides
an informative notion of how robust the network is, rather than just the
conventional assertion that the network is not verifiable. Furthermore, it
provides an ability to scale to larger networks than formal verification
approaches. Though the framework still provides a formal guarantee of
satisfiability whenever it successfully finds one or more violations, these
advantages do come at the cost of only providing a statistical estimate of
unsatisfiability whenever no violation is found. Key to the practical success
of our approach is an adaptation of multi-level splitting, a Monte Carlo
approach for estimating the probability of rare events, to our statistical
robustness framework. We demonstrate that our approach is able to emulate
formal verification procedures on benchmark problems, while scaling to larger
networks and providing reliable additional information in the form of accurate
estimates of the violation probability.
Emergence of linguistic conventions in multi-agent reinforcement learning
Authors: Dorota Lipowska, Adam Lipowski
Date: 2018-11-17 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recently, emergence of signaling conventions, among which language is a prime
example, draws a considerable interdisciplinary interest ranging from game
theory, to robotics to evolutionary linguistics. Such a wide spectrum of
research is based on much different assumptions and methodologies, but
complexity of the problem precludes formulation of a unifying and commonly
accepted explanation. We examine formation of signaling conventions in a
framework of a multi-agent reinforcement learning model. When the network of
interactions between agents is a complete graph or a sufficiently dense random
graph, a global consensus is typically reached with the emerging language being
a nearly unique object-word mapping or containing some synonyms and homonyms.
On finite-dimensional lattices, the model gets trapped in disordered
configurations with a local consensus only. Such a trapping can be avoided by
introducing a population renewal, which in the presence of superlinear
reinforcement restores an ordinary surface-tension driven coarsening and
considerably enhances formation of efficient signaling.
On Human Robot Interaction using Multiple Modes
Authors: Neha Baranwal
Date: 2018-11-17 00:00:00
Tasks: Industrial Robots

Humanoid robots have apparently similar body structure like human beings. Due
to their technical design, they are sharing the same workspace with humans.
They are placed to clean things, to assist old age people, to entertain us and
most importantly to serve us. To be acceptable in the household, they must have
higher level of intelligence than industrial robots and they must be social and
capable of interacting people around it, who are not supposed to be robot
specialist. All these come under the field of human robot interaction (HRI).
There are various modes like speech, gesture, behavior etc. through which human
can interact with robots. To solve all these challenges, a multimodel technique
has been introduced where gesture as well as speech is used as a mode of
interaction.
Recursive Sparse Pseudo-input Gaussian Process SARSA
Authors: John Martin, Brendan Englot
Date: 2018-11-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The class of Gaussian Process (GP) methods for Temporal Difference learning
has shown promise for data-efficient model-free Reinforcement Learning. In this
paper, we consider a recent variant of the GP-SARSA algorithm, called Sparse
Pseudo-input Gaussian Process SARSA (SPGP-SARSA), and derive recursive formulas
for its predictive moments. This extension promotes greater memory efficiency,
since previous computations can be reused and, interestingly, it provides a
technique for updating value estimates on a multiple timescales
A Fast and Greedy Subset-of-Data (SoD) Scheme for Sparsification in Gaussian processes
Authors: Vidhi Lalchand, A. C. Faul
Date: 2018-11-17 00:00:00
Tasks: Gaussian Processes

In their standard form Gaussian processes (GPs) provide a powerful non-parametric framework for regression and classificaton tasks. Their one limiting property is their $\mathcal{O}(N^{3})$ scaling where $N$ is the number of training data points. In this paper we present a framework for GP training with sequential selection of training data points using an intuitive selection metric. The greedy forward selection strategy is devised to target two factors - regions of high predictive uncertainty and underfit. Under this technique the complexity of GP training is reduced to $\mathcal{O}(M^{3})$ where $(M \ll N)$ if $M$ data points (out of $N$) are eventually selected. The sequential nature of the algorithm circumvents the need to invert the covariance matrix of dimension $N \times N$ and enables the use of favourable matrix inverse update identities. We outline the algorithm and sequential updates to the posterior mean and variance. We demonstrate our method on selected one dimensional functions and show that the loss in accuracy due to using a subset of data points is marginal compared to the computational gains.
The Theory and Algorithm of Ergodic Inference
Authors: Yichuan Zhang
Date: 2018-11-17 00:00:00
Tasks: BIG-bench Machine Learning, Variational Inference

Approximate inference algorithm is one of the fundamental research fields in
machine learning. The two dominant theoretical inference frameworks in machine
learning are variational inference (VI) and Markov chain Monte Carlo (MCMC).
However, because of the fundamental limitation in the theory, it is very
challenging to improve existing VI and MCMC methods on both the computational
scalability and statistical efficiency. To overcome this obstacle, we propose a
new theoretical inference framework called ergodic Inference based on the
fundamental property of ergodic transformations. The key contribution of this
work is to establish the theoretical foundation of ergodic inference for the
development of practical algorithms in future work.
Sequential Image-based Attention Network for Inferring Force Estimation without Haptic Sensor
Authors: Hochul Shin, Hyeon Cho, Dongyi Kim, Daekwan Ko, Soochul Lim, Wonjun Hwang
Date: 2018-11-17 00:00:00

Humans can infer approximate interaction force between objects from only vision information because we already have learned it through experiences. Based on this idea, we propose a recurrent convolutional neural network-based method using sequential images for inferring interaction force without using a haptic sensor. For training and validating deep learning methods, we collected a large number of images and corresponding interaction forces through an electronic motor-based device. To concentrate on changing shapes of a target object by the external force in images, we propose a sequential image-based attention module, which learns a salient model from temporal dynamics. The proposed sequential image-based attention module consists of a sequential spatial attention module and a sequential channel attention module, which are extended to exploit multiple sequential images. For gaining better accuracy, we also created a weighted average pooling layer for both spatial and channel attention modules. The extensive experimental results verified that the proposed method successfully infers interaction forces under the various conditions, such as different target materials, illumination changes, and external force directions.