pwc-archive/papers-with-abstracts huggingface dataset:
The Proximal Robbins-Monro Method
Authors: Panos Toulis, Thibaut Horel, Edoardo M. Airoldi
Date: 2015-10-04 00:00:00
Tasks: parameter estimation, Stochastic Optimization

The need for parameter estimation with massive datasets has reinvigorated interest in stochastic optimization and iterative estimation procedures. Stochastic approximations are at the forefront of this recent development as they yield procedures that are simple, general, and fast. However, standard stochastic approximations are often numerically unstable. Deterministic optimization, on the other hand, increasingly uses proximal updates to achieve numerical stability in a principled manner. A theoretical gap has thus emerged. While standard stochastic approximations are subsumed by the framework of Robbins and Monro (1951), there is no such framework for stochastic approximations with proximal updates. In this paper, we conceptualize a proximal version of the classical Robbins-Monro procedure. Our theoretical analysis demonstrates that the proposed procedure has important stability benefits over the classical Robbins-Monro procedure, while it retains the best known convergence rates. Exact implementations of the proximal Robbins-Monro procedure are challenging, but we show that approximate implementations lead to procedures that are easy to implement, and still dominate classical procedures by achieving numerical stability, practically without tradeoffs. Moreover, approximate proximal Robbins-Monro procedures can be applied even when the objective cannot be calculated analytically, and so they generalize stochastic proximal procedures currently in use.
Improving QED-Tutrix by Automating the Generation of Proofs
Authors: Ludovic Font, Philippe R. Richard, Michel Gagnon
Date: 2018-03-05 00:00:00
Tasks: Automated Theorem Proving

The idea of assisting teachers with technological tools is not new.
Mathematics in general, and geometry in particular, provide interesting
challenges when developing educative softwares, both in the education and
computer science aspects. QED-Tutrix is an intelligent tutor for geometry
offering an interface to help high school students in the resolution of
demonstration problems. It focuses on specific goals: 1) to allow the student
to freely explore the problem and its figure, 2) to accept proofs elements in
any order, 3) to handle a variety of proofs, which can be customized by the
teacher, and 4) to be able to help the student at any step of the resolution of
the problem, if the need arises. The software is also independent from the
intervention of the teacher. QED-Tutrix offers an interesting approach to
geometry education, but is currently crippled by the lengthiness of the process
of implementing new problems, a task that must still be done manually.
Therefore, one of the main focuses of the QED-Tutrix' research team is to ease
the implementation of new problems, by automating the tedious step of finding
all possible proofs for a given problem. This automation must follow
fundamental constraints in order to create problems compatible with QED-Tutrix:
1) readability of the proofs, 2) accessibility at a high school level, and 3)
possibility for the teacher to modify the parameters defining the
"acceptability" of a proof. We present in this paper the result of our
preliminary exploration of possible avenues for this task. Automated theorem
proving in geometry is a widely studied subject, and various provers exist.
However, our constraints are quite specific and some adaptation would be
required to use an existing prover. We have therefore implemented a prototype
of automated prover to suit our needs. The future goal is to compare
performances and usability in our specific use-case between the existing
provers and our implementation.
Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution
Authors: Lanlan Liu, Jia Deng
Date: 2017-01-02 00:00:00
Tasks: Computational Efficiency, image-classification, Image Classification, Reinforcement Learning

We introduce Dynamic Deep Neural Networks (D2NN), a new type of feed-forward
deep neural network that allows selective execution. Given an input, only a
subset of D2NN neurons are executed, and the particular subset is determined by
the D2NN itself. By pruning unnecessary computation depending on input, D2NNs
provide a way to improve computational efficiency. To achieve dynamic selective
execution, a D2NN augments a feed-forward deep neural network (directed acyclic
graph of differentiable modules) with controller modules. Each controller
module is a sub-network whose output is a decision that controls whether other
modules can execute. A D2NN is trained end to end. Both regular and controller
modules in a D2NN are learnable and are jointly trained to optimize both
accuracy and efficiency. Such training is achieved by integrating
backpropagation with reinforcement learning. With extensive experiments of
various D2NN architectures on image classification tasks, we demonstrate that
D2NNs are general and flexible, and can effectively optimize
accuracy-efficiency trade-offs.
Less Is More: Picking Informative Frames for Video Captioning
Authors: Yangyu Chen, Shuhui Wang, Weigang Zhang, Qingming Huang
Date: 2018-03-05 00:00:00
Proceeding: ECCV 2018 9
Tasks: Decoder, Diversity, Reinforcement Learning, Video Captioning

In video captioning task, the best practice has been achieved by
attention-based models which associate salient visual components with sentences
in the video. However, existing study follows a common procedure which includes
a frame-level appearance modeling and motion modeling on equal interval frame
sampling, which may bring about redundant visual information, sensitivity to
content noise and unnecessary computation cost.
  We propose a plug-and-play PickNet to perform informative frame picking in
video captioning. Based on a standard Encoder-Decoder framework, we develop a
reinforcement-learning-based procedure to train the network sequentially, where
the reward of each frame picking action is designed by maximizing visual
diversity and minimizing textual discrepancy. If the candidate is rewarded, it
will be selected and the corresponding latent representation of Encoder-Decoder
will be updated for future trials. This procedure goes on until the end of the
video sequence. Consequently, a compact frame subset can be selected to
represent the visual information and perform video captioning without
performance degradation. Experiment results shows that our model can use 6-8
frames to achieve competitive performance across popular benchmarks.
Deep Continuous Clustering
Authors: Sohil Atul Shah, Vladlen Koltun
Date: 2018-03-05 00:00:00
Proceeding: ICLR 2018 1
Tasks: Clustering, Dimensionality Reduction

Clustering high-dimensional datasets is hard because interpoint distances
become less informative in high-dimensional spaces. We present a clustering
algorithm that performs nonlinear dimensionality reduction and clustering
jointly. The data is embedded into a lower-dimensional space by a deep
autoencoder. The autoencoder is optimized as part of the clustering process.
The resulting network produces clustered data. The presented approach does not
rely on prior knowledge of the number of ground-truth clusters. Joint nonlinear
dimensionality reduction and clustering are formulated as optimization of a
global continuous objective. We thus avoid discrete reconfigurations of the
objective that characterize prior clustering algorithms. Experiments on
datasets from multiple domains demonstrate that the presented algorithm
outperforms state-of-the-art clustering schemes, including recent methods that
use deep networks.
Stochastic Activation Pruning for Robust Adversarial Defense
Authors: Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, Anima Anandkumar
Date: 2018-03-05 00:00:00
Proceeding: ICLR 2018 1
Tasks: Adversarial Defense

Neural networks are known to be vulnerable to adversarial examples. Carefully
chosen perturbations to real images, while imperceptible to humans, induce
misclassification and threaten the reliability of deep learning systems in the
wild. To guard against adversarial examples, we take inspiration from game
theory and cast the problem as a minimax zero-sum game between the adversary
and the model. In general, for such games, the optimal strategy for both
players requires a stochastic policy, also known as a mixed strategy. In this
light, we propose Stochastic Activation Pruning (SAP), a mixed strategy for
adversarial defense. SAP prunes a random subset of activations (preferentially
pruning those with smaller magnitude) and scales up the survivors to
compensate. We can apply SAP to pretrained networks, including adversarially
trained models, without fine-tuning, providing robustness against adversarial
examples. Experiments demonstrate that SAP confers robustness against attacks,
increasing accuracy and preserving calibration.
Hierarchical Modeling and Shrinkage for User Session Length Prediction in Media Streaming
Authors: Antoine Dedieu, Rahul Mazumder, Zhen Zhu, Hossein Vahabi
Date: 2018-03-04 00:00:00

An important metric of users' satisfaction and engagement within on-line
streaming services is the user session length, i.e. the amount of time they
spend on a service continuously without interruption. Being able to predict
this value directly benefits the recommendation and ad pacing contexts in music
and video streaming services. Recent research has shown that predicting the
exact amount of time spent is highly nontrivial due to many external factors
for which a user can end a session, and the lack of predictive covariates. Most
of the other related literature on duration based user engagement has focused
on dwell time for websites, for search and display ads, mainly for post-click
satisfaction prediction or ad ranking.
  In this work we present a novel framework inspired by hierarchical Bayesian
modeling to predict, at the moment of login, the amount of time a user will
spend in the streaming service. The time spent by a user on a platform depends
upon user-specific latent variables which are learned via hierarchical
shrinkage. Our framework enjoys theoretical guarantees and naturally
incorporates flexible parametric/nonparametric models on the covariates,
including models robust to outliers. Our proposal is found to outperform
state-of- the-art estimators in terms of efficiency and predictive performance
on real world public and private datasets.
Skip Connections Eliminate Singularities
Authors: A. Emin Orhan, Xaq Pitkow
Date: 2017-01-31 00:00:00
Proceeding: ICLR 2018 1

Skip connections made the training of very deep networks possible and have
become an indispensable component in a variety of neural architectures. A
completely satisfactory explanation for their success remains elusive. Here, we
present a novel explanation for the benefits of skip connections in training
very deep networks. The difficulty of training deep networks is partly due to
the singularities caused by the non-identifiability of the model. Several such
singularities have been identified in previous works: (i) overlap singularities
caused by the permutation symmetry of nodes in a given layer, (ii) elimination
singularities corresponding to the elimination, i.e. consistent deactivation,
of nodes, (iii) singularities generated by the linear dependence of the nodes.
These singularities cause degenerate manifolds in the loss landscape that slow
down learning. We argue that skip connections eliminate these singularities by
breaking the permutation symmetry of nodes, by reducing the possibility of node
elimination and by making the nodes less linearly dependent. Moreover, for
typical initializations, skip connections move the network away from the
"ghosts" of these singularities and sculpt the landscape around them to
alleviate the learning slow-down. These hypotheses are supported by evidence
from simplified models, as well as from experiments with deep networks trained
on real-world datasets.
Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analytics
Authors: Milad Makkie, Heng Huang, Yu Zhao, Athanasios V. Vasilakos, Tianming Liu
Date: 2017-10-24 00:00:00
Tasks: blind source separation, Dictionary Learning, GPU, Time Series, Time Series Analysis

In recent years, analyzing task-based fMRI (tfMRI) data has become an
essential tool for understanding brain function and networks. However, due to
the sheer size of tfMRI data, its intrinsic complex structure, and lack of
ground truth of underlying neural activities, modeling tfMRI data is hard and
challenging. Previously proposed data-modeling methods including Independent
Component Analysis (ICA) and Sparse Dictionary Learning only provided a weakly
established model based on blind source separation under the strong assumption
that original fMRI signals could be linearly decomposed into time series
components with corresponding spatial maps. Meanwhile, analyzing and learning a
large amount of tfMRI data from a variety of subjects has been shown to be very
demanding but yet challenging even with technological advances in computational
hardware. Given the Convolutional Neural Network (CNN), a robust method for
learning high-level abstractions from low-level data such as tfMRI time series,
in this work we propose a fast and scalable novel framework for distributed
deep Convolutional Autoencoder model. This model aims to both learn the complex
hierarchical structure of the tfMRI data and to leverage the processing power
of multiple GPUs in a distributed fashion. To implement such a model, we have
created an enhanced processing pipeline on the top of Apache Spark and
Tensorflow library, leveraging from a very large cluster of GPU machines.
Experimental data from applying the model on the Human Connectome Project (HCP)
show that the proposed model is efficient and scalable toward tfMRI big data
analytics, thus enabling data-driven extraction of hierarchical neuroscientific
information from massive fMRI big data in the future.
On the Effectiveness of Simple Success-Based Parameter Selection Mechanisms for Two Classical Discrete Black-Box Optimization Benchmark Problems
Authors: Carola Doerr, Markus Wagner
Date: 2018-03-04 00:00:00

Despite significant empirical and theoretically supported evidence that
non-static parameter choices can be strongly beneficial in evolutionary
computation, the question how to best adjust parameter values plays only a
marginal role in contemporary research on discrete black-box optimization. This
has led to the unsatisfactory situation in which feedback-free parameter
selection rules such as the cooling schedule of Simulated Annealing are
predominant in state-of-the-art heuristics, while, at the same time, we
understand very well that such time-dependent selection rules can only perform
worse than adjustment rules that do take into account the evolution of the
optimization process. A number of adaptive and self-adaptive parameter control
strategies have been proposed in the literature, but did not (yet) make their
way to a broader public. A key obstacle seems to lie in their rather complex
update rules.
  The purpose of our work is to demonstrate that high-performing online
parameter selection rules do not have to be very complicated. More precisely,
we experiment with a multiplicative, comparison-based update rule to adjust the
mutation probability of a (1+1)~Evolutionary Algorithm. We show that this
simple self-adjusting rule outperforms the best static unary unbiased black-box
algorithm on LeadingOnes, achieving an almost optimal speedup of about~$18\%$.
DAGs with NO TEARS: Continuous Optimization for Structure Learning
Authors: Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
Date: 2018-03-04 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Causal Discovery

Estimating the structure of directed acyclic graphs (DAGs, also known as
Bayesian networks) is a challenging problem since the search space of DAGs is
combinatorial and scales superexponentially with the number of nodes. Existing
approaches rely on various local heuristics for enforcing the acyclicity
constraint. In this paper, we introduce a fundamentally different strategy: We
formulate the structure learning problem as a purely \emph{continuous}
optimization problem over real matrices that avoids this combinatorial
constraint entirely. This is achieved by a novel characterization of acyclicity
that is not only smooth but also exact. The resulting problem can be
efficiently solved by standard numerical algorithms, which also makes
implementation effortless. The proposed method outperforms existing ones,
without imposing any structural assumptions on the graph such as bounded
treewidth or in-degree. Code implementing the proposed algorithm is open-source
and publicly available at https://github.com/xunzheng/notears.
Egocentric Basketball Motion Planning from a Single First-Person Image
Authors: Gedas Bertasius, Aaron Chan, Jianbo Shi
Date: 2018-03-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: Motion Planning

We present a model that uses a single first-person image to generate an
egocentric basketball motion sequence in the form of a 12D camera configuration
trajectory, which encodes a player's 3D location and 3D head orientation
throughout the sequence. To do this, we first introduce a future convolutional
neural network (CNN) that predicts an initial sequence of 12D camera
configurations, aiming to capture how real players move during a one-on-one
basketball game. We also introduce a goal verifier network, which is trained to
verify that a given camera configuration is consistent with the final goals of
real one-on-one basketball players. Next, we propose an inverse synthesis
procedure to synthesize a refined sequence of 12D camera configurations that
(1) sufficiently matches the initial configurations predicted by the future
CNN, while (2) maximizing the output of the goal verifier network. Finally, by
following the trajectory resulting from the refined camera configuration
sequence, we obtain the complete 12D motion sequence.
  Our model generates realistic basketball motion sequences that capture the
goals of real players, outperforming standard deep learning approaches such as
recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and
generative adversarial networks (GANs).
A real-time decision support system for bridge management based on the rules generalized by CART decision tree and SMO algorithms
Authors: Shadi Abpeykar, Mehdi Ghatee
Date: 2018-03-04 00:00:00
Tasks: Management

Under dynamic conditions on bridges, we need a real-time management. To this
end, this paper presents a rule-based decision support system in which the
necessary rules are extracted from simulation results made by Aimsun traffic
micro-simulation software. Then, these rules are generalized by the aid of
fuzzy rule generation algorithms. Then, they are trained by a set of supervised
and the unsupervised learning algorithms to get an ability to make decision in
real cases. As a pilot case study, Nasr Bridge in Tehran is simulated in Aimsun
and WEKA data mining software is used to execute the learning algorithms. Based
on this experiment, the accuracy of the supervised algorithms to generalize the
rules is greater than 80%. In addition, CART decision tree and sequential
minimal optimization (SMO) provides 100% accuracy for normal data and these
algorithms are so reliable for crisis management on bridge. This means that, it
is possible to use such machine learning methods to manage bridges in the
real-time conditions.
Exploring Novel Game Spaces with Fluidic Games
Authors: Swen E. Gaudl, Mark J. Nelson, Simon Colton, Rob Saunders, Edward J. Powley, Peter Ivey, Blanca Perez Ferrer, Michael Cook
Date: 2018-03-04 00:00:00

With the growing integration of smartphones into our daily lives, and their
increased ease of use, mobile games have become highly popular across all
demographics. People listen to music, play games or read the news while in
transit or bridging gap times. While mobile gaming is gaining popularity,
mobile expression of creativity is still in its early stages. We present here a
new type of mobile app -- fluidic games -- and illustrate our iterative
approach to their design. This new type of app seamlessly integrates
exploration of the design space into the actual user experience of playing the
game, and aims to enrich the user experience. To better illustrate the game
domain and our approach, we discuss one specific fluidic game, which is
available as a commercial product. We also briefly discuss open challenges such
as player support and how generative techniques can aid the exploration of the
game space further.
Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations
Authors: Andreas Rücklé, Steffen Eger, Maxime Peyrard, Iryna Gurevych
Date: 2018-03-04 00:00:00
Tasks: Sentence, Sentence Embedding, Sentence-Embedding, Word Embeddings

Average word embeddings are a common baseline for more sophisticated sentence
embedding techniques. However, they typically fall short of the performances of
more complex models such as InferSent. Here, we generalize the concept of
average word embeddings to power mean word embeddings. We show that the
concatenation of different types of power mean word embeddings considerably
closes the gap to state-of-the-art methods monolingually and substantially
outperforms these more complex techniques cross-lingually. In addition, our
proposed method outperforms different recently proposed baselines such as SIF
and Sent2Vec by a solid margin, thus constituting a much harder-to-beat
monolingual baseline. Our data and code are publicly available.
Recurrent Neural Networks as Weighted Language Recognizers
Authors: Yining Chen, Sorcha Gilroy, Andreas Maletti, Jonathan May, Kevin Knight
Date: 2017-11-15 00:00:00
Proceeding: NAACL 2018 6

We investigate the computational complexity of various problems for simple
recurrent neural networks (RNNs) as formal models for recognizing weighted
languages. We focus on the single-layer, ReLU-activation, rational-weight RNNs
with softmax, which are commonly used in natural language processing
applications. We show that most problems for such RNNs are undecidable,
including consistency, equivalence, minimization, and the determination of the
highest-weighted string. However, for consistent RNNs the last problem becomes
decidable, although the solution length can surpass all computable bounds. If
additionally the string is limited to polynomial length, the problem becomes
NP-complete and APX-hard. In summary, this shows that approximations and
heuristic algorithms are necessary in practical applications of those RNNs.