pwc-archive/papers-with-abstracts huggingface dataset:
SamBaTen: Sampling-based Batch Incremental Tensor Decomposition
Authors: Ekta Gujral, Ravdeep Pasricha, Evangelos E. Papalexakis
Date: 2017-09-03 00:00:00
Tasks: Tensor Decomposition, valid

Tensor decompositions are invaluable tools in analyzing multimodal datasets.
In many real-world scenarios, such datasets are far from being static, to the
contrary they tend to grow over time. For instance, in an online social network
setting, as we observe new interactions over time, our dataset gets updated in
its "time" mode. How can we maintain a valid and accurate tensor decomposition
of such a dynamically evolving multimodal dataset, without having to re-compute
the entire decomposition after every single update? In this paper we introduce
SaMbaTen, a Sampling-based Batch Incremental Tensor Decomposition algorithm,
which incrementally maintains the decomposition given new updates to the tensor
dataset. SaMbaTen is able to scale to datasets that the state-of-the-art in
incremental tensor decomposition is unable to operate on, due to its ability to
effectively summarize the existing tensor and the incoming updates, and perform
all computations in the reduced summary space. We extensively evaluate SaMbaTen
using synthetic and real datasets. Indicatively, SaMbaTen achieves comparable
accuracy to state-of-the-art incremental and non-incremental techniques, while
being 25-30 times faster. Furthermore, SaMbaTen scales to very large sparse and
dense dynamically evolving tensors of dimensions up to 100K x 100K x 100K where
state-of-the-art incremental approaches were not able to operate.
Paraphrasing verbal metonymy through computational methods
Authors: Alberto Morón Hernández
Date: 2017-09-18 00:00:00

Verbal metonymy has received relatively scarce attention in the field of
computational linguistics despite the fact that a model to accurately
paraphrase metonymy has applications both in academia and the technology
sector. The method described in this paper makes use of data from the British
National Corpus in order to create word vectors, find instances of verbal
metonymy and generate potential paraphrases. Two different ways of creating
word vectors are evaluated in this study: Continuous bag of words and
Skip-grams. Skip-grams are found to outperform the Continuous bag of words
approach. Furthermore, the Skip-gram model is found to operate with
better-than-chance accuracy and there is a strong positive relationship (phi
coefficient = 0.61) between the model's classification and human judgement of
the ranked paraphrases. This study lends credence to the viability of modelling
verbal metonymy through computational methods based on distributional
semantics.
Matterport3D: Learning from RGB-D Data in Indoor Environments
Authors: Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Nießner, Manolis Savva, Shuran Song, Andy Zeng, yinda zhang
Date: 2017-09-18 00:00:00
Tasks: General Classification, Scene Understanding, Semantic Segmentation

Access to large, diverse RGB-D datasets is critical for training RGB-D scene
understanding algorithms. However, existing datasets still cover only a limited
number of views or a restricted scale of spaces. In this paper, we introduce
Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views
from 194,400 RGB-D images of 90 building-scale scenes. Annotations are provided
with surface reconstructions, camera poses, and 2D and 3D semantic
segmentations. The precise global alignment and comprehensive, diverse
panoramic set of views over entire buildings enable a variety of supervised and
self-supervised computer vision tasks, including keypoint matching, view
overlap prediction, normal prediction from color, semantic segmentation, and
region classification.
Multi-modal analysis of genetically-related subjects using SIFT descriptors in brain MRI
Authors: Kuldeep Kumar, Laurent Chauvin, Mathew Toews, Olivier Colliot, Christian Desrosiers
Date: 2017-09-18 00:00:00
Tasks: Diffusion  MRI

So far, fingerprinting studies have focused on identifying features from
single-modality MRI data, which capture individual characteristics in terms of
brain structure, function, or white matter microstructure. However, due to the
lack of a framework for comparing across multiple modalities, studies based on
multi-modal data remain elusive. This paper presents a multi-modal analysis of
genetically-related subjects to compare and contrast the information provided
by various MRI modalities. The proposed framework represents MRI scans as bags
of SIFT features, and uses these features in a nearest-neighbor graph to
measure subject similarity. Experiments using the T1/T2-weighted MRI and
diffusion MRI data of 861 Human Connectome Project subjects demonstrate strong
links between the proposed similarity measure and genetic proximity.
White Matter Fiber Segmentation Using Functional Varifolds
Authors: Kuldeep Kumar, Pietro Gori, Benjamin Charlier, Stanley Durrleman, Olivier Colliot, Christian Desrosiers
Date: 2017-09-18 00:00:00
Tasks: Dictionary Learning

The extraction of fibers from dMRI data typically produces a large number of
fibers, it is common to group fibers into bundles. To this end, many
specialized distance measures, such as MCP, have been used for fiber
similarity. However, these distance based approaches require point-wise
correspondence and focus only on the geometry of the fibers. Recent
publications have highlighted that using microstructure measures along fibers
improves tractography analysis. Also, many neurodegenerative diseases impacting
white matter require the study of microstructure measures as well as the white
matter geometry. Motivated by these, we propose to use a novel computational
model for fibers, called functional varifolds, characterized by a metric that
considers both the geometry and microstructure measure (e.g. GFA) along the
fiber pathway. We use it to cluster fibers with a dictionary learning and
sparse coding-based framework, and present a preliminary analysis using HCP
data.
Model-Powered Conditional Independence Test
Authors: Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai
Date: 2017-09-18 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Classification, General Classification, Generalization Bounds, model

We consider the problem of non-parametric Conditional Independence testing
(CI testing) for continuous random variables. Given i.i.d samples from the
joint distribution $f(x,y,z)$ of continuous random vectors $X,Y$ and $Z,$ we
determine whether $X \perp Y | Z$. We approach this by converting the
conditional independence test into a classification problem. This allows us to
harness very powerful classifiers like gradient-boosted trees and deep neural
networks. These models can handle complex probability distributions and allow
us to perform significantly better compared to the prior state of the art, for
high-dimensional CI testing. The main technical challenge in the classification
problem is the need for samples from the conditional product distribution
$f^{CI}(x,y,z) = f(x|z)f(y|z)f(z)$ -- the joint distribution if and only if $X
\perp Y | Z.$ -- when given access only to i.i.d. samples from the true joint
distribution $f(x,y,z)$. To tackle this problem we propose a novel nearest
neighbor bootstrap procedure and theoretically show that our generated samples
are indeed close to $f^{CI}$ in terms of total variational distance. We then
develop theoretical results regarding the generalization bounds for
classification for our problem, which translate into error bounds for CI
testing. We provide a novel analysis of Rademacher type classification bounds
in the presence of non-i.i.d near-independent samples. We empirically validate
the performance of our algorithm on simulated and real datasets and show
performance gains over previous methods.
Iterative Policy Learning in End-to-End Trainable Task-Oriented Neural Dialog Models
Authors: Bing Liu, Ian Lane
Date: 2017-09-18 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we present a deep reinforcement learning (RL) framework for
iterative dialog policy optimization in end-to-end task-oriented dialog
systems. Popular approaches in learning dialog policy with RL include letting a
dialog agent to learn against a user simulator. Building a reliable user
simulator, however, is not trivial, often as difficult as building a good
dialog agent. We address this challenge by jointly optimizing the dialog agent
and the user simulator with deep RL by simulating dialogs between the two
agents. We first bootstrap a basic dialog agent and a basic user simulator by
learning directly from dialog corpora with supervised training. We then improve
them further by letting the two agents to conduct task-oriented dialogs and
iteratively optimizing their policies with deep RL. Both the dialog agent and
the user simulator are designed with neural network models that can be trained
end-to-end. Our experiment results show that the proposed method leads to
promising improvements on task success rate and total task reward comparing to
supervised training and single-agent RL training baseline models.
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
Authors: Qinliang Su, Xuejun Liao, Lawrence Carin
Date: 2017-09-18 00:00:00
Proceeding: NeurIPS 2017 12

We present a probabilistic framework for nonlinearities, based on doubly
truncated Gaussian distributions. By setting the truncation points
appropriately, we are able to generate various types of nonlinearities within a
unified framework, including sigmoid, tanh and ReLU, the most commonly used
nonlinearities in neural networks. The framework readily integrates into
existing stochastic neural networks (with hidden units characterized as random
variables), allowing one for the first time to learn the nonlinearities
alongside model weights in these networks. Extensive experiments demonstrate
the performance improvements brought about by the proposed framework when
integrated with the restricted Boltzmann machine (RBM), temporal RBM and the
truncated Gaussian graphical model (TGGM).
Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players
Authors: Itay Benou, Ronel Veksler, Alon Friedman, Tammy Riklin Raviv
Date: 2017-09-18 00:00:00

We present the concept of fiber-flux density for locally quantifying white
matter (WM) fiber bundles. By combining scalar diffusivity measures (e.g.,
fractional anisotropy) with fiber-flux measurements, we define new local
descriptors called Fiber-Flux Diffusion Density (FFDD) vectors. Applying each
descriptor throughout fiber bundles allows along-tract coupling of a specific
diffusion measure with geometrical properties, such as fiber orientation and
coherence. A key step in the proposed framework is the construction of an FFDD
dissimilarity measure for sub-voxel alignment of fiber bundles, based on the
fast marching method (FMM). The obtained aligned WM tract-profiles enable
meaningful inter-subject comparisons and group-wise statistical analysis. We
demonstrate our method using two different datasets of contact sports players.
Along-tract pairwise comparison as well as group-wise analysis, with respect to
non-player healthy controls, reveal significant and spatially-consistent FFDD
anomalies. Comparing our method with along-tract FA analysis shows improved
sensitivity to subtle structural anomalies in football players over standard FA
measurements.
Semi-Supervised Phoneme Recognition with Recurrent Ladder Networks
Authors: Marian Tietz, Tayfun Alpay, Johannes Twiefel, Stefan Wermter
Date: 2017-06-07 00:00:00
Tasks: Phoneme Recognition

Ladder networks are a notable new concept in the field of semi-supervised
learning by showing state-of-the-art results in image recognition tasks while
being compatible with many existing neural architectures. We present the
recurrent ladder network, a novel modification of the ladder network, for
semi-supervised learning of recurrent neural networks which we evaluate with a
phoneme recognition task on the TIMIT corpus. Our results show that the model
is able to consistently outperform the baseline and achieve fully-supervised
baseline performance with only 75% of all labels which demonstrates that the
model is capable of using unsupervised data as an effective regulariser.
Adaptive Neural Networks for Efficient Inference
Authors: Tolga Bolukbasi, Joseph Wang, Ofer Dekel, Venkatesh Saligrama
Date: 2017-02-25 00:00:00
Proceeding: ICML 2017 8
Tasks: Binary Classification

We present an approach to adaptively utilize deep neural networks in order to
reduce the evaluation time on new examples without loss of accuracy. Rather
than attempting to redesign or approximate existing networks, we propose two
schemes that adaptively utilize networks. We first pose an adaptive network
evaluation scheme, where we learn a system to adaptively choose the components
of a deep network to be evaluated for each example. By allowing examples
correctly classified using early layers of the system to exit, we avoid the
computational time associated with full evaluation of the network. We extend
this to learn a network selection system that adaptively selects the network to
be evaluated for each example. We show that computational time can be
dramatically reduced by exploiting the fact that many examples can be correctly
classified using relatively efficient networks and that complex,
computationally costly networks are only necessary for a small fraction of
examples. We pose a global objective for learning an adaptive early exit or
network selection policy and solve it by reducing the policy learning problem
to a layer-by-layer weighted binary classification problem. Empirically, these
approaches yield dramatic reductions in computational cost, with up to a 2.8x
speedup on state-of-the-art networks from the ImageNet image recognition
challenge with minimal (<1%) loss of top5 accuracy.
GaKCo: a Fast GApped k-mer string Kernel using COunting
Authors: Ritambhara Singh, Arshdeep Sekhon, Kamran Kowsari, Jack Lanchantin, Beilun Wang, Yanjun Qi
Date: 2017-04-24 00:00:00

String Kernel (SK) techniques, especially those using gapped $k$-mers as
features (gk), have obtained great success in classifying sequences like DNA,
protein, and text. However, the state-of-the-art gk-SK runs extremely slow when
we increase the dictionary size ($\Sigma$) or allow more mismatches ($M$). This
is because current gk-SK uses a trie-based algorithm to calculate co-occurrence
of mismatched substrings resulting in a time cost proportional to
$O(\Sigma^{M})$. We propose a \textbf{fast} algorithm for calculating
\underline{Ga}pped $k$-mer \underline{K}ernel using \underline{Co}unting
(GaKCo). GaKCo uses associative arrays to calculate the co-occurrence of
substrings using cumulative counting. This algorithm is fast, scalable to
larger $\Sigma$ and $M$, and naturally parallelizable. We provide a rigorous
asymptotic analysis that compares GaKCo with the state-of-the-art gk-SK.
Theoretically, the time cost of GaKCo is independent of the $\Sigma^{M}$ term
that slows down the trie-based approach. Experimentally, we observe that GaKCo
achieves the same accuracy as the state-of-the-art and outperforms its speed by
factors of 2, 100, and 4, on classifying sequences of DNA (5 datasets), protein
(12 datasets), and character-based English text (2 datasets), respectively.
  GaKCo is shared as an open source tool at
\url{https://github.com/QData/GaKCo-SVM}
Coupled Ensembles of Neural Networks
Authors: Anuvabh Dutt, Denis Pellerin, Georges Quénot
Date: 2017-09-18 00:00:00
Proceeding: ICLR 2018 1

We investigate in this paper the architecture of deep convolutional networks.
Building on existing state of the art models, we propose a reconfiguration of
the model parameters into several parallel branches at the global network
level, with each branch being a standalone CNN. We show that this arrangement
is an efficient way to significantly reduce the number of parameters without
losing performance or to significantly improve the performance with the same
level of performance. The use of branches brings an additional form of
regularization. In addition to the split into parallel branches, we propose a
tighter coupling of these branches by placing the "fuse (averaging) layer"
before the Log-Likelihood and SoftMax layers during training. This gives
another significant performance improvement, the tighter coupling favouring the
learning of better representations, even at the level of the individual
branches. We refer to this branched architecture as "coupled ensembles". The
approach is very generic and can be applied with almost any DCNN architecture.
With coupled ensembles of DenseNet-BC and parameter budget of 25M, we obtain
error rates of 2.92%, 15.68% and 1.50% respectively on CIFAR-10, CIFAR-100 and
SVHN tasks. For the same budget, DenseNet-BC has error rate of 3.46%, 17.18%,
and 1.8% respectively. With ensembles of coupled ensembles, of DenseNet-BC
networks, with 50M total parameters, we obtain error rates of 2.72%, 15.13% and
1.42% respectively on these tasks.
Vehicle Tracking in Wide Area Motion Imagery via Stochastic Progressive Association Across Multiple Frames (SPAAM)
Authors: Ahmed Elliethy, Gaurav Sharma
Date: 2017-09-18 00:00:00

Vehicle tracking in Wide Area Motion Imagery (WAMI) relies on associating
vehicle detections across multiple WAMI frames to form tracks corresponding to
individual vehicles. The temporal window length, i.e., the number $M$ of
sequential frames, over which associations are collectively estimated poses a
trade-off between accuracy and computational complexity. A larger $M$ improves
performance because the increased temporal context enables the use of motion
models and allows occlusions and spurious detections to be handled better. The
number of total hypotheses tracks, on the other hand, grows exponentially with
increasing $M$, making larger values of $M$ computationally challenging to
tackle. In this paper, we introduce SPAAM an iterative approach that
progressively grows $M$ with each iteration to improve estimated tracks by
exploiting the enlarged temporal context while keeping computation manageable
through two novel approaches for pruning association hypotheses. First, guided
by a road network, accurately co-registered to the WAMI frames, we disregard
unlikely associations that do not agree with the road network. Second, as $M$
is progressively enlarged at each iteration, the related increase in
association hypotheses is limited by revisiting only the subset of association
possibilities rendered open by stochastically determined dis-associations for
the previous iteration. The stochastic dis-association at each iteration
maintains each estimated association according to an estimated probability for
confidence, obtained via a probabilistic model. Associations at each iteration
are then estimated globally over the $M$ frames by (approximately) solving a
binary integer programming problem for selecting a set of compatible tracks.
Vehicle tracking results obtained over test WAMI datasets indicate that our
proposed approach provides significant performance improvements over 3
alternatives.
Sequence to Sequence Learning for Event Prediction
Authors: Dai Quoc Nguyen, Dat Quoc Nguyen, Cuong Xuan Chu, Stefan Thater, Manfred Pinkal
Date: 2017-09-18 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Prediction, Sentence

This paper presents an approach to the task of predicting an event
description from a preceding sentence in a text. Our approach explores
sequence-to-sequence learning using a bidirectional multi-layer recurrent
neural network. Our approach substantially outperforms previous work in terms
of the BLEU score on two datasets derived from WikiHow and DeScript
respectively. Since the BLEU score is not easy to interpret as a measure of
event prediction, we complement our study with a second evaluation that
exploits the rich linguistic annotation of gold paraphrase sets of events.
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
Authors: Clément Moulin-Frier, Jordi-Ysard Puigbò, Xerxes D. Arsiwalla, Martì Sanchez-Fibla, Paul F. M. J. Verschure
Date: 2017-04-05 00:00:00
Tasks: Benchmarking, Board Games, valid

In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.
Guided Deep Reinforcement Learning for Swarm Systems
Authors: Maximilian Hüttenrauch, Adrian Šošić, Gerhard Neumann
Date: 2017-09-18 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we investigate how to learn to control a group of cooperative
agents with limited sensing capabilities such as robot swarms. The agents have
only very basic sensor capabilities, yet in a group they can accomplish
sophisticated tasks, such as distributed assembly or search and rescue tasks.
Learning a policy for a group of agents is difficult due to distributed partial
observability of the state. Here, we follow a guided approach where a critic
has central access to the global state during learning, which simplifies the
policy evaluation problem from a reinforcement learning point of view. For
example, we can get the positions of all robots of the swarm using a camera
image of a scene. This camera image is only available to the critic and not to
the control policies of the robots. We follow an actor-critic approach, where
the actors base their decisions only on locally sensed information. In
contrast, the critic is learned based on the true global state. Our algorithm
uses deep reinforcement learning to approximate both the Q-function and the
policy. The performance of the algorithm is evaluated on two tasks with simple
simulated 2D agents: 1) finding and maintaining a certain distance to each
others and 2) locating a target.