pwc-archive/papers-with-abstracts huggingface dataset:
Restructuring Conversations using Discourse Relations for Zero-shot Abstractive Dialogue Summarization
Authors: Prakhar Ganesh, Saket Dingliwal
Date: 2019-02-05 00:00:00
Tasks: Abstractive Dialogue Summarization, Abstractive Text Summarization, Document Summarization

Dialogue summarization is a challenging problem due to the informal and unstructured nature of conversational data. Recent advances in abstractive summarization have been focused on data-hungry neural models and adapting these models to a new domain requires the availability of domain-specific manually annotated corpus created by linguistic experts. We propose a zero-shot abstractive dialogue summarization method that uses discourse relations to provide structure to conversations, and then uses an out-of-the-box document summarization model to create final summaries. Experiments on the AMI and ICSI meeting corpus, with document summarization models like PGN and BART, shows that our method improves the ROGUE score by up to 3 points, and even performs competitively against other state-of-the-art methods.
Low-latency job scheduling with preemption for the development of deep learning
Authors: Hidehito Yabuuchi, Daisuke Taniwaki, Shingo Omura
Date: 2019-02-05 00:00:00
Tasks: Scheduling

One significant challenge in the job scheduling of computing clusters for the
development of deep learning algorithms is the efficient scheduling of
trial-and-error (TE) job, the type of job in which the users seek to conduct
small-scale experiments while monitoring their processes. Unfortunately, the
existing job schedulers to date do not feature well-balanced scheduling for the
mixture of TE jobs and best-effort (BE) jobs, or they can handle the mixture in
limited situations at most. To fill in this niche, we propose an algorithm that
can significantly reduce the latency of TE jobs in versatile situations without
greatly elongating the slowdown of the BE jobs. Our algorithm efficiently
schedules both TE and BE jobs by selectively preempting the BE jobs that can
be, when the time comes, resumed without much delay. In our simulation study
with synthetic and real workloads, we were able to reduce the 95th percentile
of the slowdown rates for the TE jobs in the standard FIFO strategy by 96.6%,
while compromising the median of the BE slowdown rates by only 18.0% and the
95th percentile by only 23.9%.
A variance modeling framework based on variational autoencoders for speech enhancement
Authors: Simon Leglaive, Laurent Girin, Radu Horaud
Date: 2019-02-05 00:00:00
Tasks: Speech Enhancement

In this paper we address the problem of enhancing speech signals in noisy
mixtures using a source separation approach. We explore the use of neural
networks as an alternative to a popular speech variance model based on
supervised non-negative matrix factorization (NMF). More precisely, we use a
variational autoencoder as a speaker-independent supervised generative speech
model, highlighting the conceptual similarities that this approach shares with
its NMF-based counterpart. In order to be free of generalization issues
regarding the noisy recording environments, we follow the approach of having a
supervised model only for the target speech signal, the noise model being based
on unsupervised NMF. We develop a Monte Carlo expectation-maximization
algorithm for inferring the latent variables in the variational autoencoder and
estimating the unsupervised model parameters. Experiments show that the
proposed method outperforms a semi-supervised NMF baseline and a
state-of-the-art fully supervised deep learning approach.
Robust supervised classification and feature selection using a primal-dual method
Authors: Michel Barlaud, Antonin Chambolle, Jean-Baptiste Caillau
Date: 2019-02-05 00:00:00
Tasks: feature selection, General Classification

This paper deals with supervised classification and feature selection in high dimensional space. A classical approach is to project data on a low dimensional space and classify by minimizing an appropriate quadratic cost. A strict control on sparsity is moreover obtained by adding an $\ell_1$ constraint, here on the matrix of weights used for projecting the data. Tuning the sparsity bound results in selecting the relevant features for supervised classification. It is well known that using a quadratic cost is not robust to outliers. We cope with this problem by using an $\ell_1$ norm both for the constraint and for the loss function. In this case, the criterion is convex but not gradient Lipschitz anymore. Another second issue is that we optimize simultaneously the projection matrix and the centers used for classification. In this paper, we provide a novel tailored constrained primal-dual method to compute jointly selected features and classifiers. Extending our primal-dual method to other criteria is easy provided that efficient projection (on the dual ball for the loss data term) and prox (for the regularization term) algorithms are available. We illustrate such an extension in the case of a Frobenius norm for the loss term. We provide a convergence proof of our primal-dual method, and demonstrate its effectiveness on three datasets (one synthetic, two from biological data) on which we compare $\ell_1$ and $\ell_2$ costs.
Deep backward schemes for high-dimensional nonlinear PDEs
Authors: Côme Huré, Huyên Pham, Xavier Warin
Date: 2019-02-05 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We propose new machine learning schemes for solving high dimensional nonlinear partial differential equations (PDEs). Relying on the classical backward stochastic differential equation (BSDE) representation of PDEs, our algorithms estimate simultaneously the solution and its gradient by deep neural networks. These approximations are performed at each time step from the minimization of loss functions defined recursively by backward induction. The methodology is extended to variational inequalities arising in optimal stopping problems. We analyze the convergence of the deep learning schemes and provide error estimates in terms of the universal approximation of neural networks. Numerical results show that our algorithms give very good results till dimension 50 (and certainly above), for both PDEs and variational inequalities problems. For the PDEs resolution, our results are very similar to those obtained by the recent method in \cite{weinan2017deep} when the latter converges to the right solution or does not diverge. Numerical tests indicate that the proposed methods are not stuck in poor local minimaas it can be the case with the algorithm designed in \cite{weinan2017deep}, and no divergence is experienced. The only limitation seems to be due to the inability of the considered deep neural networks to represent a solution with a too complex structure in high dimension.
Survival Forests under Test: Impact of the Proportional Hazards Assumption on Prognostic and Predictive Forests for ALS Survival
Authors: Natalia Korepanova, Heidi Seibold, Verena Steffen, Torsten Hothorn
Date: 2019-02-05 00:00:00

We investigate the effect of the proportional hazards assumption on
prognostic and predictive models of the survival time of patients suffering
from amyotrophic lateral sclerosis (ALS). We theoretically compare the
underlying model formulations of several variants of survival forests and
implementations thereof, including random forests for survival, conditional
inference forests, Ranger, and survival forests with $L_1$ splitting, with two
novel variants, namely distributional and transformation survival forests.
Theoretical considerations explain the low power of log-rank-based splitting in
detecting patterns in non-proportional hazards situations in survival trees and
corresponding forests. This limitation can potentially be overcome by the
alternative split procedures suggested herein. We empirically investigated this
effect using simulation experiments and a re-analysis of the PRO-ACT database
of ALS survival, giving special emphasis to both prognostic and predictive
models.
PUTWorkbench: Analysing Privacy in AI-intensive Systems
Authors: Saurabh Srivastava, Vinay P. Namboodiri, T. V. Prabhakar
Date: 2019-02-05 00:00:00

AI intensive systems that operate upon user data face the challenge of
balancing data utility with privacy concerns. We propose the idea and present
the prototype of an open-source tool called Privacy Utility Trade-off (PUT)
Workbench which seeks to aid software practitioners to take such crucial
decisions. We pick a simple privacy model that doesn't require any background
knowledge in Data Science and show how even that can achieve significant
results over standard and real-life datasets. The tool and the source code is
made freely available for extensions and usage.
Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits
Authors: Lilian Besson, Emilie Kaufmann, Odalric-Ambrym Maillard, Julien Seznec
Date: 2019-02-05 00:00:00
Tasks: Change Point Detection

We introduce GLR-klUCB, a novel algorithm for the piecewise iid non-stationary bandit problem with bounded rewards. This algorithm combines an efficient bandit algorithm, kl-UCB, with an efficient, parameter-free, changepoint detector, the Bernoulli Generalized Likelihood Ratio Test, for which we provide new theoretical guarantees of independent interest. Unlike previous non-stationary bandit algorithms using a change-point detector, GLR-klUCB does not need to be calibrated based on prior knowledge on the arms' means. We prove that this algorithm can attain a $O(\sqrt{TA \Upsilon_T\log(T)})$ regret in $T$ rounds on some "easy" instances, where A is the number of arms and $\Upsilon_T$ the number of change-points, without prior knowledge of $\Upsilon_T$. In contrast with recently proposed algorithms that are agnostic to $\Upsilon_T$, we perform a numerical study showing that GLR-klUCB is also very efficient in practice, beyond easy instances.
Learning to Learn in Simulation
Authors: Ervin Teng, Bob Iannucci
Date: 2019-02-05 00:00:00
Tasks: Deep Reinforcement Learning, object-detection, Object Detection, Reinforcement Learning

Deep learning often requires the manual collection and annotation of a
training set. On robotic platforms, can we partially automate this task by
training the robot to be curious, i.e., to seek out beneficial training
information in the environment? In this work, we address the problem of
curiosity as it relates to online, real-time, human-in-the-loop training of an
object detection algorithm onboard a drone, where motion is constrained to two
dimensions. We use a 3D simulation environment and deep reinforcement learning
to train a curiosity agent to, in turn, train the object detection model. This
agent could have one of two conflicting objectives: train as quickly as
possible, or train with minimal human input. We outline a reward function that
allows the curiosity agent to learn either of these objectives, while taking
into account some of the physical characteristics of the drone platform on
which it is meant to run. In addition, We show that we can weigh the importance
of achieving these objectives by adjusting a parameter in the reward function.
Relevance Factor VAE: Learning and Identifying Disentangled Factors
Authors: Minyoung Kim, Yuting Wang, Pritish Sahu, Vladimir Pavlovic
Date: 2019-02-05 00:00:00
Tasks: Disentanglement

We propose a novel VAE-based deep auto-encoder model that can learn
disentangled latent representations in a fully unsupervised manner, endowed
with the ability to identify all meaningful sources of variation and their
cardinality. Our model, dubbed Relevance-Factor-VAE, leverages the total
correlation (TC) in the latent space to achieve the disentanglement goal, but
also addresses the key issue of existing approaches which cannot distinguish
between meaningful and nuisance factors of latent variation, often the source
of considerable degradation in disentanglement performance. We tackle this
issue by introducing the so-called relevance indicator variables that can be
automatically learned from data, together with the VAE parameters. Our model
effectively focuses the TC loss onto the relevant factors only by tolerating
large prior KL divergences, a desideratum justified by our semi-parametric
theoretical analysis. Using a suite of disentanglement metrics, including a
newly proposed one, as well as qualitative evidence, we demonstrate that our
model outperforms existing methods across several challenging benchmark
datasets.
Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning
Authors: Shushman Choudhury, Jacob P. Knickerbocker, Mykel J. Kochenderfer
Date: 2019-02-05 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Sequential Decision Making

We introduce the problem of Dynamic Real-time Multimodal Routing (DREAMR), which requires planning and executing routes under uncertainty for an autonomous agent. The agent has access to a time-varying transit vehicle network in which it can use multiple modes of transportation. For instance, a drone can either fly or ride on terrain vehicles for segments of their routes. DREAMR is a difficult problem of sequential decision making under uncertainty with both discrete and continuous variables. We design a novel hierarchical hybrid planning framework to solve the DREAMR problem that exploits its structural decomposability. Our framework consists of a global open-loop planning layer that invokes and monitors a local closed-loop execution layer. Additional abstractions allow efficient and seamless interleaving of planning and execution. We create a large-scale simulation for DREAMR problems, with each scenario having hundreds of transportation routes and thousands of connection points. Our algorithmic framework significantly outperforms a receding horizon control baseline, in terms of elapsed time to reach the destination and energy expended by the agent.
Revisiting a single-stage method for face detection
Authors: Nguyen Van Quang, Hiromasa Fujihara
Date: 2019-02-05 00:00:00
Tasks: Face Detection, GPU

Although accurate, two-stage face detectors usually require more inference
time than single-stage detectors do. This paper proposes a simple yet effective
single-stage model for real-time face detection with a prominently high
accuracy. We build our single-stage model on the top of the ResNet-101 backbone
and analyze some problems with the baseline single-stage detector in order to
design several strategies for reducing the false positive rate. The design
leverages the context information from the deeper layers in order to increase
recall rate while maintaining a low false positive rate. In addition, we reduce
the detection time by an improved inference procedure for decoding outputs
faster. The inference time of a VGA ($640{\times}480$) image was only
approximately 26 ms with a Titan X GPU. The effectiveness of our proposed
method was evaluated on several face detection benchmarks (Wider Face, AFW,
Pascal Face, and FDDB). The experiments show that our method achieved
competitive results on these popular datasets with a faster runtime than the
current best two-stage practices.
Learning to Schedule Communication in Multi-agent Reinforcement Learning
Authors: Daewoo Kim, Sangwoo Moon, David Hostallero, Wan Ju Kang, Taeyoung Lee, Kyunghwan Son, Yung Yi
Date: 2019-02-05 00:00:00
Proceeding: ICLR 2019 5
Tasks: Deep Reinforcement Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

Many real-world reinforcement learning tasks require multiple agents to make
sequential decisions under the agents' interaction, where well-coordinated
actions among the agents are crucial to achieve the target goal better at these
tasks. One way to accelerate the coordination effect is to enable multiple
agents to communicate with each other in a distributed manner and behave as a
group. In this paper, we study a practical scenario when (i) the communication
bandwidth is limited and (ii) the agents share the communication medium so that
only a restricted number of agents are able to simultaneously use the medium,
as in the state-of-the-art wireless networking standards. This calls for a
certain form of communication scheduling. In that regard, we propose a
multi-agent deep reinforcement learning framework, called SchedNet, in which
agents learn how to schedule themselves, how to encode the messages, and how to
select actions based on received messages. SchedNet is capable of deciding
which agents should be entitled to broadcasting their (encoded) messages, by
learning the importance of each agent's partially observed information. We
evaluate SchedNet against multiple baselines under two different applications,
namely, cooperative communication and navigation, and predator-prey. Our
experiments show a non-negligible performance gap between SchedNet and other
mechanisms such as the ones without communication and with vanilla scheduling
methods, e.g., round robin, ranging from 32% to 43%.
SASSE: Scalable and Adaptable 6-DOF Pose Estimation
Authors: Huu Le, Tuan Hoang, Qianggong Zhang, Thanh-Toan Do, Anders Eriksson, Michael Milford
Date: 2019-02-05 00:00:00
Tasks: Benchmarking, Pose Estimation, Visual Localization

Visual localization has become a key enabling component of many place
recognition and SLAM systems. Contemporary research has primarily focused on
improving accuracy and precision-recall type metrics, with relatively little
attention paid to a system's absolute storage scaling characteristics, its
flexibility to adapt to available computational resources, and its longevity
with respect to easily incorporating newly learned or hand-crafted image
descriptors. Most significantly, improvement in one of these aspects typically
comes at the cost of others: for example, a snapshot-based system that achieves
sub-linear storage cost typically provides no metric pose estimation, or, a
highly accurate pose estimation technique is often ossified in adapting to
recent advances in appearance-invariant features. In this paper, we present a
novel 6-DOF localization system that for the first time simultaneously achieves
all the three characteristics: significantly sub-linear storage growth,
agnosticism to image descriptors, and customizability to available storage and
computational resources. The key features of our method are developed based on
a novel adaptation of multiple-label learning, together with effective
dimensional reduction and learning techniques that enable simple and efficient
optimization. We evaluate our system on several large benchmarking datasets and
provide detailed comparisons to state-of-the-art systems. The proposed method
demonstrates competitive accuracy with existing pose estimation methods while
achieving better sub-linear storage scaling, significantly reduced absolute
storage requirements, and faster training and deployment speeds.
An Ensemble SVM-based Approach for Voice Activity Detection
Authors: Jayanta Dey, Md Sanzid Bin Hossain, Mohammad Ariful Haque
Date: 2019-02-05 00:00:00
Tasks: Action Detection, Activity Detection, General Classification, Speaker Recognition, Speech Enhancement

Voice activity detection (VAD), used as the front end of speech enhancement,
speech and speaker recognition algorithms, determines the overall accuracy and
efficiency of the algorithms. Therefore, a VAD with low complexity and high
accuracy is highly desirable for speech processing applications. In this paper,
we propose a novel training method on large dataset for supervised
learning-based VAD system using support vector machine (SVM). Despite of high
classification accuracy of support vector machines (SVM), trivial SVM is not
suitable for classification of large data sets needed for a good VAD system
because of high training complexity. To overcome this problem, a novel
ensemble-based approach using SVM has been proposed in this paper.The
performance of the proposed ensemble structure has been compared with a
feedforward neural network (NN). Although NN performs better than single
SVM-based VAD trained on a small portion of the training data, ensemble SVM
gives accuracy comparable to neural network-based VAD. Ensemble SVM and NN give
88.74% and 86.28% accuracy respectively whereas the stand-alone SVM shows
57.05% accuracy on average on the test dataset.
Learning Hierarchical Interactions at Scale: A Convex Optimization Approach
Authors: Hussein Hazimeh, Rahul Mazumder
Date: 2019-02-05 00:00:00
Tasks: Structured Prediction, Variable Selection

In many learning settings, it is beneficial to augment the main features with pairwise interactions. Such interaction models can be often enhanced by performing variable selection under the so-called strong hierarchy constraint: an interaction is non-zero only if its associated main features are non-zero. Existing convex optimization based algorithms face difficulties in handling problems where the number of main features $p \sim 10^3$ (with total number of features $\sim p^2$). In this paper, we study a convex relaxation which enforces strong hierarchy and develop a highly scalable algorithm based on proximal gradient descent. We introduce novel screening rules that allow for solving the complicated proximal problem in parallel. In addition, we introduce a specialized active-set strategy with gradient screening for avoiding costly gradient computations. The framework can handle problems having dense design matrices, with $p = 50,000$ ($\sim 10^9$ interactions)---instances that are much larger than current state of the art. Experiments on real and synthetic data suggest that our toolkit hierScale outperforms the state of the art in terms of prediction and variable selection and can achieve over a 4900x speed-up.
The Referential Reader: A Recurrent Entity Network for Anaphora Resolution
Authors: Fei Liu, Luke Zettlemoyer, Jacob Eisenstein
Date: 2019-02-05 00:00:00
Proceeding: ACL 2019 7
Tasks: Language Modeling, Language Modelling

We present a new architecture for storing and accessing entity mentions during online text processing. While reading the text, entity references are identified, and may be stored by either updating or overwriting a cell in a fixed-length memory. The update operation implies coreference with the other mentions that are stored in the same cell; the overwrite operation causes these mentions to be forgotten. By encoding the memory operations as differentiable gates, it is possible to train the model end-to-end, using both a supervised anaphora resolution objective as well as a supplementary language modeling objective. Evaluation on a dataset of pronoun-name anaphora demonstrates strong performance with purely incremental text processing.
A Practical Maximum Clique Algorithm for Matching with Pairwise Constraints
Authors: Álvaro Parra, Tat-Jun Chin, Frank Neumann, Tobias Friedrich, Maximilian Katzmann
Date: 2019-02-05 00:00:00
Tasks: Point Cloud Registration

A popular paradigm for 3D point cloud registration is by extracting 3D keypoint correspondences, then estimating the registration function from the correspondences using a robust algorithm. However, many existing 3D keypoint techniques tend to produce large proportions of erroneous correspondences or outliers, which significantly increases the cost of robust estimation. An alternative approach is to directly search for the subset of correspondences that are pairwise consistent, without optimising the registration function. This gives rise to the combinatorial problem of matching with pairwise constraints. In this paper, we propose a very efficient maximum clique algorithm to solve matching with pairwise constraints. Our technique combines tree searching with efficient bounding and pruning based on graph colouring. We demonstrate that, despite the theoretical intractability, many real problem instances can be solved exactly and quickly (seconds to minutes) with our algorithm, which makes our approach an excellent alternative to standard robust techniques for 3D registration.