pwc-archive/papers-with-abstracts huggingface dataset:
Shallow Parsing Pipeline for Hindi-English Code-Mixed Social Media Text
Authors: Arnav Sharma, Sakshi Gupta, Raveesh Motlani, Piyush Bansal, Manish Srivastava, Radhika Mamidi, Dipti M. Sharma
Date: 2016-04-11 00:00:00

In this study, the problem of shallow parsing of Hindi-English code-mixed
social media text (CSMT) has been addressed. We have annotated the data,
developed a language identifier, a normalizer, a part-of-speech tagger and a
shallow parser. To the best of our knowledge, we are the first to attempt
shallow parsing on CSMT. The pipeline developed has been made available to the
research community with the goal of enabling better text analysis of Hindi
English CSMT. The pipeline is accessible at http://bit.ly/csmt-parser-api .
Conversational flow in Oxford-style debates
Authors: Justine Zhang, Ravi Kumar, Sujith Ravi, Cristian Danescu-Niculescu-Mizil
Date: 2016-04-11 00:00:00
Proceeding: NAACL 2016 6

Public debates are a common platform for presenting and juxtaposing diverging
views on important issues. In this work we propose a methodology for tracking
how ideas flow between participants throughout a debate. We use this approach
in a case study of Oxford-style debates---a competitive format where the winner
is determined by audience votes---and show how the outcome of a debate depends
on aspects of conversational flow. In particular, we find that winners tend to
make better use of a debate's interactive component than losers, by actively
pursuing their opponents' points rather than promoting their own ideas over the
course of the conversation.
Fully-Automatic Synapse Prediction and Validation on a Large Data Set
Authors: Gary B. Huang, Louis K. Scheffer, Stephen M. Plaza
Date: 2016-04-11 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Extracting a connectome from an electron microscopy (EM) data set requires
identification of neurons and determination of synapses between neurons. As
manual extraction of this information is very time-consuming, there has been
extensive research effort to automatically segment the neurons to help guide
and eventually replace manual tracing. Until recently, there has been
comparatively less research on automatically detecting the actual synapses
between neurons. This discrepancy can, in part, be attributed to several
factors: obtaining neuronal shapes is a prerequisite first step in extracting a
connectome, manual tracing is much more time-consuming than annotating
synapses, and neuronal contact area can be used as a proxy for synapses in
determining connections.
  However, recent research has demonstrated that contact area alone is not a
sufficient predictor of synaptic connection. Moreover, as segmentation has
improved, we have observed that synapse annotation is consuming a more
significant fraction of overall reconstruction time. This ratio will only get
worse as segmentation improves, gating overall possible speed-up. Therefore, we
address this problem by developing algorithms that automatically detect
pre-synaptic neurons and their post-synaptic partners. In particular,
pre-synaptic structures are detected using a Deep and Wide Multiscale Recursive
Network, and post-synaptic partners are detected using a MLP with features
conditioned on the local segmentation.
  This work is novel because it requires minimal amount of training, leverages
advances in image segmentation directly, and provides a complete solution for
polyadic synapse detection. We further introduce novel metrics to evaluate our
algorithm on connectomes of meaningful size. These metrics demonstrate that
complete automatic prediction can be used to effectively characterize most
connectivity correctly.
Structural-RNN: Deep Learning on Spatio-Temporal Graphs
Authors: Ashesh Jain, Amir R. Zamir, Silvio Savarese, Ashutosh Saxena
Date: 2015-11-17 00:00:00
Proceeding: CVPR 2016 6
Tasks: Deep Learning, Human Pose Forecasting, Skeleton Based Action Recognition

Deep Recurrent Neural Network architectures, though remarkably capable at
modeling sequences, lack an intuitive high-level spatio-temporal structure.
That is while many problems in computer vision inherently have an underlying
high-level structure and can benefit from it. Spatio-temporal graphs are a
popular tool for imposing such high-level intuitions in the formulation of real
world problems. In this paper, we propose an approach for combining the power
of high-level spatio-temporal graphs and sequence learning success of Recurrent
Neural Networks~(RNNs). We develop a scalable method for casting an arbitrary
spatio-temporal graph as a rich RNN mixture that is feedforward, fully
differentiable, and jointly trainable. The proposed method is generic and
principled as it can be used for transforming any spatio-temporal graph through
employing a certain set of well defined steps. The evaluations of the proposed
approach on a diverse set of problems, ranging from modeling human motion to
object interactions, shows improvement over the state-of-the-art with a large
margin. We expect this method to empower new approaches to problem formulation
through high-level spatio-temporal graphs and Recurrent Neural Networks.
An electronic-game framework for evaluating coevolutionary algorithms
Authors: Karine da Silva Miras de Araújo, Fabrício Olivetti de França
Date: 2016-04-03 00:00:00
Tasks: Evolutionary Algorithms

One of the common artificial intelligence applications in electronic games
consists of making an artificial agent learn how to execute some determined
task successfully in a game environment. One way to perform this task is
through machine learning algorithms capable of learning the sequence of actions
required to win in a given game environment. There are several supervised
learning techniques able to learn the correct answer for a problem through
examples. However, when learning how to play electronic games, the correct
answer might only be known by the end of the game, after all the actions were
already taken. Thus, not being possible to measure the accuracy of each
individual action to be taken at each time step. A way for dealing with this
problem is through Neuroevolution, a method which trains Artificial Neural
Networks using evolutionary algorithms. In this article, we introduce a
framework for testing optimization algorithms with artificial agent controllers
in electronic games, called EvoMan, which is inspired in the action-platformer
game Mega Man II. The environment can be configured to run in different
experiment modes, as single evolution, coevolution and others. To demonstrate
some challenges regarding the proposed platform, as initial experiments we
applied Neuroevolution using Genetic Algorithms and the NEAT algorithm, in the
context of competitively coevolving two distinct agents in this game.
No Regret Bound for Extreme Bandits
Authors: Robert Nishihara, David Lopez-Paz, Léon Bottou
Date: 2015-08-12 00:00:00
Tasks: Hyperparameter Optimization

Algorithms for hyperparameter optimization abound, all of which work well
under different and often unverifiable assumptions. Motivated by the general
challenge of sequentially choosing which algorithm to use, we study the more
specific task of choosing among distributions to use for random hyperparameter
optimization. This work is naturally framed in the extreme bandit setting,
which deals with sequentially choosing which distribution from a collection to
sample in order to minimize (maximize) the single best cost (reward). Whereas
the distributions in the standard bandit setting are primarily characterized by
their means, a number of subtleties arise when we care about the minimal cost
as opposed to the average cost. For example, there may not be a well-defined
"best" distribution as there is in the standard bandit setting. The best
distribution depends on the rewards that have been obtained and on the
remaining time horizon. Whereas in the standard bandit setting, it is sensible
to compare policies with an oracle which plays the single best arm, in the
extreme bandit setting, there are multiple sensible oracle models. We define a
sensible notion of "extreme regret" in the extreme bandit setting, which
parallels the concept of regret in the standard bandit setting. We then prove
that no policy can asymptotically achieve no extreme regret.
Learning Global Features for Coreference Resolution
Authors: Sam Wiseman, Alexander M. Rush, Stuart M. Shieber
Date: 2016-04-11 00:00:00
Proceeding: NAACL 2016 6
Tasks: Attribute, coreference-resolution, Coreference Resolution, Prediction

There is compelling evidence that coreference prediction would benefit from
modeling global information about entity-clusters. Yet, state-of-the-art
performance can be achieved with systems treating each mention prediction
independently, which we attribute to the inherent difficulty of crafting
informative cluster-level features. We instead propose to use recurrent neural
networks (RNNs) to learn latent, global representations of entity clusters
directly from their mentions. We show that such representations are especially
useful for the prediction of pronominal mentions, and can be incorporated into
an end-to-end coreference system that outperforms the state of the art without
requiring any additional search.
M3: Scaling Up Machine Learning via Memory Mapping
Authors: Dezhi Fang, Duen Horng Chau
Date: 2016-04-11 00:00:00
Tasks: BIG-bench Machine Learning, Graph Mining, regression

To process data that do not fit in RAM, conventional wisdom would suggest
using distributed approaches. However, recent research has demonstrated virtual
memory's strong potential in scaling up graph mining algorithms on a single
machine. We propose to use a similar approach for general machine learning. We
contribute: (1) our latest finding that memory mapping is also a feasible
technique for scaling up general machine learning algorithms like logistic
regression and k-means, when data fits in or exceeds RAM (we tested datasets up
to 190GB); (2) an approach, called M3, that enables existing machine learning
algorithms to work with out-of-core datasets through memory mapping, achieving
a speed that is significantly faster than a 4-instance Spark cluster, and
comparable to an 8-instance cluster.
In the mood: the dynamics of collective sentiments on Twitter
Authors: Nathaniel Charlton, Colin Singleton, Danica Vukadinović Greetham
Date: 2016-04-11 00:00:00

We study the relationship between the sentiment levels of Twitter users and
the evolving network structure that the users created by @-mentioning each
other. We use a large dataset of tweets to which we apply three sentiment
scoring algorithms, including the open source SentiStrength program.
Specifically we make three contributions. Firstly we find that people who have
potentially the largest communication reach (according to a dynamic centrality
measure) use sentiment differently than the average user: for example they use
positive sentiment more often and negative sentiment less often. Secondly we
find that when we follow structurally stable Twitter communities over a period
of months, their sentiment levels are also stable, and sudden changes in
community sentiment from one day to the next can in most cases be traced to
external events affecting the community. Thirdly, based on our findings, we
create and calibrate a simple agent-based model that is capable of reproducing
measures of emotive response comparable to those obtained from our empirical
dataset.
Semi-supervised learning of local structured output predictors
Authors: Xin Du
Date: 2016-04-11 00:00:00
Tasks: Sentence, Structured Prediction

In this paper, we study the problem of semi-supervised structured output
prediction, which aims to learn predictors for structured outputs, such as
sequences, tree nodes, vectors, etc., from a set of data points of both
input-output pairs and single inputs without outputs. The traditional methods
to solve this problem usually learns one single predictor for all the data
points, and ignores the variety of the different data points. Different parts
of the data set may have different local distributions, and requires different
optimal local predictors. To overcome this disadvantage of existing methods, we
propose to learn different local predictors for neighborhoods of different data
points, and the missing structured outputs simultaneously. In the neighborhood
of each data point, we proposed to learn a linear predictor by minimizing both
the complexity of the predictor and the upper bound of the structured
prediction loss. The minimization is conducted by gradient descent algorithms.
Experiments over four benchmark data sets, including DDSM mammography medical
images, SUN natural image data set, Cora research paper data set, and Spanish
news wire article sentence data set, show the advantages of the proposed
method.
Graph Connectivity in Noisy Sparse Subspace Clustering
Authors: Yining Wang, Yu-Xiang Wang, Aarti Singh
Date: 2015-04-04 00:00:00
Tasks: Clustering

Subspace clustering is the problem of clustering data points into a union of
low-dimensional linear/affine subspaces. It is the mathematical abstraction of
many important problems in computer vision, image processing and machine
learning. A line of recent work (4, 19, 24, 20) provided strong theoretical
guarantee for sparse subspace clustering (4), the state-of-the-art algorithm
for subspace clustering, on both noiseless and noisy data sets. It was shown
that under mild conditions, with high probability no two points from different
subspaces are clustered together. Such guarantee, however, is not sufficient
for the clustering to be correct, due to the notorious "graph connectivity
problem" (15). In this paper, we investigate the graph connectivity problem for
noisy sparse subspace clustering and show that a simple post-processing
procedure is capable of delivering consistent clustering under certain "general
position" or "restricted eigenvalue" assumptions. We also show that our
condition is almost tight with adversarial noise perturbation by constructing a
counter-example. These results provide the first exact clustering guarantee of
noisy SSC for subspaces of dimension greater then 3.
CP-mtML: Coupled Projection multi-task Metric Learning for Large Scale Face Retrieval
Authors: Binod Bhattarai, Gaurav Sharma, Frederic Jurie
Date: 2016-04-11 00:00:00
Proceeding: CVPR 2016 6
Tasks: Face Image Retrieval, Image Retrieval, Metric Learning, Multi-Task Learning, Retrieval

We propose a novel Coupled Projection multi-task Metric Learning (CP-mtML)
method for large scale face retrieval. In contrast to previous works which were
limited to low dimensional features and small datasets, the proposed method
scales to large datasets with high dimensional face descriptors. It utilises
pairwise (dis-)similarity constraints as supervision and hence does not require
exhaustive class annotation for every training image. While, traditionally,
multi-task learning methods have been validated on same dataset but different
tasks, we work on the more challenging setting with heterogeneous datasets and
different tasks. We show empirical validation on multiple face image datasets
of different facial traits, e.g. identity, age and expression. We use classic
Local Binary Pattern (LBP) descriptors along with the recent Deep Convolutional
Neural Network (CNN) features. The experiments clearly demonstrate the
scalability and improved performance of the proposed method on the tasks of
identity and age based face image retrieval compared to competitive existing
methods, on the standard datasets and with the presence of a million distractor
face images.
Learning to Assign Orientations to Feature Points
Authors: Kwang Moo Yi, Yannick Verdie, Pascal Fua, Vincent Lepetit
Date: 2015-11-13 00:00:00
Proceeding: CVPR 2016 6

We show how to train a Convolutional Neural Network to assign a canonical
orientation to feature points given an image patch centered on the feature
point. Our method improves feature point matching upon the state-of-the art and
can be used in conjunction with any existing rotation sensitive descriptors. To
avoid the tedious and almost impossible task of finding a target orientation to
learn, we propose to use Siamese networks which implicitly find the optimal
orientations during training. We also propose a new type of activation function
for Neural Networks that generalizes the popular ReLU, maxout, and PReLU
activation functions. This novel activation performs better for our task. We
validate the effectiveness of our method extensively with four existing
datasets, including two non-planar datasets, as well as our own dataset. We
show that we outperform the state-of-the-art without the need of retraining for
each dataset.
Kernel-based Sensor Fusion with Application to Audio-Visual Voice Activity Detection
Authors: David Dov, Ronen Talmon, Israel Cohen
Date: 2016-04-11 00:00:00
Tasks: Action Detection, Activity Detection, Sensor Fusion

In this paper, we address the problem of multiple view data fusion in the
presence of noise and interferences. Recent studies have approached this
problem using kernel methods, by relying particularly on a product of kernels
constructed separately for each view. From a graph theory point of view, we
analyze this fusion approach in a discrete setting. More specifically, based on
a statistical model for the connectivity between data points, we propose an
algorithm for the selection of the kernel bandwidth, a parameter, which, as we
show, has important implications on the robustness of this fusion approach to
interferences. Then, we consider the fusion of audio-visual speech signals
measured by a single microphone and by a video camera pointed to the face of
the speaker. Specifically, we address the task of voice activity detection,
i.e., the detection of speech and non-speech segments, in the presence of
structured interferences such as keyboard taps and office noise. We propose an
algorithm for voice activity detection based on the audio-visual signal.
Simulation results show that the proposed algorithm outperforms competing
fusion and voice activity detection approaches. In addition, we demonstrate
that a proper selection of the kernel bandwidth indeed leads to improved
performance.
Optical Flow with Semantic Segmentation and Localized Layers
Authors: Laura Sevilla-Lara, Deqing Sun, Varun Jampani, Michael J. Black
Date: 2016-03-12 00:00:00
Proceeding: CVPR 2016 6
Tasks: Optical Flow Estimation, Scene Segmentation, Segmentation, Semantic Segmentation

Existing optical flow methods make generic, spatially homogeneous,
assumptions about the spatial structure of the flow. In reality, optical flow
varies across an image depending on object class. Simply put, different objects
move differently. Here we exploit recent advances in static semantic scene
segmentation to segment the image into objects of different types. We define
different models of image motion in these regions depending on the type of
object. For example, we model the motion on roads with homographies, vegetation
with spatially smooth flow, and independently moving objects like cars and
planes with affine motion plus deviations. We then pose the flow estimation
problem using a novel formulation of localized layers, which addresses
limitations of traditional layered models for dealing with complex scene
motion. Our semantic flow method achieves the lowest error of any published
monocular method in the KITTI-2015 flow benchmark and produces qualitatively
better flow and segmentation than recent top methods on a wide range of natural
videos.
Statistics of RGBD Images
Authors: Dan Rosenbaum, Yair Weiss
Date: 2016-04-11 00:00:00

Cameras that can measure the depth of each pixel in addition to its color
have become easily available and are used in many consumer products worldwide.
Often the depth channel is captured at lower quality compared to the RGB
channels and different algorithms have been proposed to improve the quality of
the D channel given the RGB channels. Typically these approaches work by
assuming that edges in RGB are correlated with edges in D.
  In this paper we approach this problem from the standpoint of natural image
statistics. We obtain examples of high quality RGBD images from a computer
graphics generated movie (MPI-Sintel) and we use these examples to compare
different probabilistic generative models of RGBD image patches. We then use
the generative models together with a degradation model and obtain a Bayes
Least Squares (BLS) estimator of the D channel given the RGB channels. Our
results show that learned generative models outperform the state-of-the-art in
improving the quality of depth channels given the color channels in natural
images even when training is performed on artificially generated images.
Sparse Coding for Alpha Matting
Authors: Jubin Johnson, Ehsan Shahrian Varnousfaderani, Hisham Cholakkal, Deepu Rajan
Date: 2016-04-11 00:00:00
Tasks: Image Matting, Video Matting

Existing color sampling based alpha matting methods use the compositing
equation to estimate alpha at a pixel from pairs of foreground (F) and
background (B) samples. The quality of the matte depends on the selected (F,B)
pairs. In this paper, the matting problem is reinterpreted as a sparse coding
of pixel features, wherein the sum of the codes gives the estimate of the alpha
matte from a set of unpaired F and B samples. A non-parametric probabilistic
segmentation provides a certainty measure on the pixel belonging to foreground
or background, based on which a dictionary is formed for use in sparse coding.
By removing the restriction to conform to (F,B) pairs, this method allows for
better alpha estimation from multiple F and B samples. The same framework is
extended to videos, where the requirement of temporal coherence is handled
effectively. Here, the dictionary is formed by samples from multiple frames. A
multi-frame graph model, as opposed to a single image as for image matting, is
proposed that can be solved efficiently in closed form. Quantitative and
qualitative evaluations on a benchmark dataset are provided to show that the
proposed method outperforms current state-of-the-art in image and video
matting.
A statistical learning strategy for closed-loop control of fluid flows
Authors: Florimond Guéniat, Lionel Mathelin, M. Yousuff Hussaini
Date: 2016-04-11 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This work discusses a closed-loop control strategy for complex systems
utilizing scarce and streaming data. A discrete embedding space is first built
using hash functions applied to the sensor measurements from which a Markov
process model is derived, approximating the complex system's dynamics. A
control strategy is then learned using reinforcement learning once rewards
relevant with respect to the control objective are identified. This method is
designed for experimental configurations, requiring no computations nor prior
knowledge of the system, and enjoys intrinsic robustness. It is illustrated on
two systems: the control of the transitions of a Lorenz 63 dynamical system,
and the control of the drag of a cylinder flow. The method is shown to perform
well.