pwc-archive/papers-with-abstracts huggingface dataset:
Off The Beaten Lane: AI Challenges In MOBAs Beyond Player Control
Authors: Michael Cook, Adam Summerville, Simon Colton
Date: 2017-06-09 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

MOBAs represent a huge segment of online gaming and are growing as both an
eSport and a casual genre. The natural starting point for AI researchers
interested in MOBAs is to develop an AI to play the game better than a human -
but MOBAs have many more challenges besides adversarial AI. In this paper we
introduce the reader to the wider context of MOBA culture, propose a range of
challenges faced by the community today, and posit concrete AI projects that
can be undertaken to begin solving them.
Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization
Authors: Rameswar Panda, Amit K. Roy-Chowdhury
Date: 2017-06-09 00:00:00
Tasks: Video Summarization

Most traditional video summarization methods are designed to generate
effective summaries for single-view videos, and thus they cannot fully exploit
the complicated intra and inter-view correlations in summarizing multi-view
videos in a camera network. In this paper, with the aim of summarizing
multi-view videos, we introduce a novel unsupervised framework via joint
embedding and sparse representative selection. The objective function is
two-fold. The first is to capture the multi-view correlations via an embedding,
which helps in extracting a diverse set of representatives. The second is to
use a `2;1- norm to model the sparsity while selecting representative shots for
the summary. We propose to jointly optimize both of the objectives, such that
embedding can not only characterize the correlations, but also indicate the
requirements of sparse representative selection. We present an efficient
alternating algorithm based on half-quadratic minimization to solve the
proposed non-smooth and non-convex objective with convergence analysis. A key
advantage of the proposed approach with respect to the state-of-the-art is that
it can summarize multi-view videos without assuming any prior
correspondences/alignment between them, e.g., uncalibrated camera networks.
Rigorous experiments on several multi-view datasets demonstrate that our
approach clearly outperforms the state-of-the-art methods.
Collaborative Summarization of Topic-Related Videos
Authors: Rameswar Panda, Amit K. Roy-Chowdhury
Date: 2017-06-09 00:00:00
Proceeding: CVPR 2017 7
Tasks: Attribute, Information Retrieval, Retrieval

Large collections of videos are grouped into clusters by a topic keyword,
such as Eiffel Tower or Surfing, with many important visual concepts repeating
across them. Such a topically close set of videos have mutual influence on each
other, which could be used to summarize one of them by exploiting information
from others in the set. We build on this intuition to develop a novel approach
to extract a summary that simultaneously captures both important
particularities arising in the given video, as well as, generalities identified
from the set of videos. The topic-related videos provide visual context to
identify the important parts of the video being summarized. We achieve this by
developing a collaborative sparse optimization method which can be efficiently
solved by a half-quadratic minimization algorithm. Our work builds upon the
idea of collaborative techniques from information retrieval and natural
language processing, which typically use the attributes of other similar
objects to predict the attribute of a given object. Experiments on two
challenging and diverse datasets well demonstrate the efficacy of our approach
over state-of-the-art methods.
Unsupervised Adaptive Re-identification in Open World Dynamic Camera Networks
Authors: Rameswar Panda, Amran Bhuiyan, Vittorio Murino, Amit K. Roy-Chowdhury
Date: 2017-06-09 00:00:00
Proceeding: CVPR 2017 7
Tasks: Person Re-Identification

Person re-identification is an open and challenging problem in computer
vision. Existing approaches have concentrated on either designing the best
feature representation or learning optimal matching metrics in a static setting
where the number of cameras are fixed in a network. Most approaches have
neglected the dynamic and open world nature of the re-identification problem,
where a new camera may be temporarily inserted into an existing system to get
additional information. To address such a novel and very practical problem, we
propose an unsupervised adaptation scheme for re-identification models in a
dynamic camera network. First, we formulate a domain perceptive
re-identification method based on geodesic flow kernel that can effectively
find the best source camera (already installed) to adapt with a newly
introduced target camera, without requiring a very expensive training phase.
Second, we introduce a transitive inference algorithm for re-identification
that can exploit the information from best source camera to improve the
accuracy across other camera pairs in a network of multiple cameras. Extensive
experiments on four benchmark datasets demonstrate that the proposed approach
significantly outperforms the state-of-the-art unsupervised learning based
alternatives whilst being extremely efficient to compute.
Fast-Slow Recurrent Neural Networks
Authors: Asier Mujika, Florian Meier, Angelika Steger
Date: 2017-05-24 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Language Modeling, Language Modelling, Machine Translation, speech-recognition, Speech Recognition

Processing sequential data of variable length is a major challenge in a wide
range of applications, such as speech recognition, language modeling,
generative image modeling and machine translation. Here, we address this
challenge by proposing a novel recurrent neural network (RNN) architecture, the
Fast-Slow RNN (FS-RNN). The FS-RNN incorporates the strengths of both
multiscale RNNs and deep transition RNNs as it processes sequential data on
different timescales and learns complex transition functions from one time step
to the next. We evaluate the FS-RNN on two character level language modeling
data sets, Penn Treebank and Hutter Prize Wikipedia, where we improve state of
the art results to $1.19$ and $1.25$ bits-per-character (BPC), respectively. In
addition, an ensemble of two FS-RNNs achieves $1.20$ BPC on Hutter Prize
Wikipedia outperforming the best known compression algorithm with respect to
the BPC measure. We also present an empirical investigation of the learning and
network dynamics of the FS-RNN, which explains the improved performance
compared to other RNN architectures. Our approach is general as any kind of RNN
cell is a possible building block for the FS-RNN architecture, and thus can be
flexibly applied to different tasks.
Radial Velocity Retrieval for Multichannel SAR Moving Targets with Time-Space Doppler De-ambiguity
Authors: Jia Xu, Zu-Zhen Huang, Zhi-Rui Wang, Li Xiao, Xiang-Gen Xia, Teng Long
Date: 2016-10-01 00:00:00
Tasks: Retrieval

In this paper, with respect to multichannel synthetic aperture radars (SAR),
we first formulate the problems of Doppler ambiguities on the radial velocity
(RV) estimation of a ground moving target in range-compressed domain,
range-Doppler domain and image domain, respectively. It is revealed that in
these problems, a cascaded time-space Doppler ambiguity (CTSDA) may encounter,
i.e., time domain Doppler ambiguity (TDDA) in each channel arises first and
then spatial domain Doppler ambiguity (SDDA) among multi-channels arises
second. Accordingly, the multichannel SAR systems with different parameters are
investigated in three different cases with diverse Doppler ambiguity
properties, and a multi-frequency SAR is then proposed to obtain the RV
estimation by solving the ambiguity problem based on Chinese remainder theorem
(CRT). In the first two cases, the ambiguity problem can be solved by the
existing closed-form robust CRT. In the third case, it is found that the
problem is different from the conventional CRT problems and we call it a double
remaindering problem in this paper. We then propose a sufficient condition
under which the double remaindering problem, i.e., the CTSDA, can also be
solved by the closed-form robust CRT. When the sufficient condition is not
satisfied for a multi-channel SAR, a searching based method is proposed.
Finally, some results of numerical experiments are provided to demonstrate the
effectiveness of the proposed methods.
Towards balanced clustering - part 1 (preliminaries)
Authors: Mark Sh. Levin
Date: 2017-06-09 00:00:00
Tasks: ARC, Clustering

The article contains a preliminary glance at balanced clustering problems.
Basic balanced structures and combinatorial balanced problems are briefly
described. A special attention is targeted to various balance/unbalance indices
(including some new versions of the indices): by cluster cardinality, by
cluster weights, by inter-cluster edge/arc weights, by cluster element
structure (for element multi-type clustering). Further, versions of
optimization clustering problems are suggested (including multicriteria problem
formulations). Illustrative numerical examples describe calculation of balance
indices and element multi-type balance clustering problems (including example
for design of student teams).
Decoupling Learning Rules from Representations
Authors: Philip S. Thomas, Christoph Dann, Emma Brunskill
Date: 2017-06-09 00:00:00
Tasks: Reinforcement Learning

In the artificial intelligence field, learning often corresponds to changing
the parameters of a parameterized function. A learning rule is an algorithm or
mathematical expression that specifies precisely how the parameters should be
changed. When creating an artificial intelligence system, we must make two
decisions: what representation should be used (i.e., what parameterized
function should be used) and what learning rule should be used to search
through the resulting set of representable functions. Using most learning
rules, these two decisions are coupled in a subtle (and often unintentional)
way. That is, using the same learning rule with two different representations
that can represent the same sets of functions can result in two different
outcomes. After arguing that this coupling is undesirable, particularly when
using artificial neural networks, we present a method for partially decoupling
these two decisions for a broad class of learning rules that span unsupervised
learning, reinforcement learning, and supervised learning.
Catalyst Acceleration for Gradient-Based Non-Convex Optimization
Authors: Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy, Julien Mairal, Zaid Harchaoui
Date: 2017-03-31 00:00:00

We introduce a generic scheme to solve nonconvex optimization problems using
gradient-based algorithms originally designed for minimizing convex functions.
Even though these methods may originally require convexity to operate, the
proposed approach allows one to use them on weakly convex objectives, which
covers a large class of non-convex functions typically appearing in machine
learning and signal processing. In general, the scheme is guaranteed to produce
a stationary point with a worst-case efficiency typical of first-order methods,
and when the objective turns out to be convex, it automatically accelerates in
the sense of Nesterov and achieves near-optimal convergence rate in function
values. These properties are achieved without assuming any knowledge about the
convexity of the objective, by automatically adapting to the unknown weak
convexity constant. We conclude the paper by showing promising experimental
results obtained by applying our approach to incremental algorithms such as
SVRG and SAGA for sparse matrix factorization and for learning neural networks.
Learning Hierarchical Features from Generative Models
Authors: Shengjia Zhao, Jiaming Song, Stefano Ermon
Date: 2017-02-27 00:00:00

Deep neural networks have been shown to be very successful at learning
feature hierarchies in supervised learning tasks. Generative models, on the
other hand, have benefited less from hierarchical models with multiple layers
of latent variables. In this paper, we prove that hierarchical latent variable
models do not take advantage of the hierarchical structure when trained with
existing variational methods, and provide some limitations on the kind of
features existing models can learn. Finally we propose an alternative
architecture that do not suffer from these limitations. Our model is able to
learn highly interpretable and disentangled hierarchical features on several
natural image datasets with no task specific regularization or prior knowledge.
Driver Identification Using Automobile Sensor Data from a Single Turn
Authors: David Hallac, Abhijit Sharang, Rainer Stahlmann, Andreas Lamprecht, Markus Huber, Martin Roehder, Rok Sosic, Jure Leskovec
Date: 2017-06-09 00:00:00
Tasks: Driver Identification, Navigate, Time Series Analysis, Time Series Classification

As automotive electronics continue to advance, cars are becoming more and
more reliant on sensors to perform everyday driving operations. These sensors
are omnipresent and help the car navigate, reduce accidents, and provide
comfortable rides. However, they can also be used to learn about the drivers
themselves. In this paper, we propose a method to predict, from sensor data
collected at a single turn, the identity of a driver out of a given set of
individuals. We cast the problem in terms of time series classification, where
our dataset contains sensor readings at one turn, repeated several times by
multiple drivers. We build a classifier to find unique patterns in each
individual's driving style, which are visible in the data even on such a short
road segment. To test our approach, we analyze a new dataset collected by AUDI
AG and Audi Electronics Venture, where a fleet of test vehicles was equipped
with automotive data loggers storing all sensor readings on real roads. We show
that turns are particularly well-suited for detecting variations across
drivers, especially when compared to straightaways. We then focus on the 12
most frequently made turns in the dataset, which include rural, urban, highway
on-ramps, and more, obtaining accurate identification results and learning
useful insights about driver behavior in a variety of settings.
Boundary-based MWE segmentation with text partitioning
Authors: Jake Ryland Williams
Date: 2016-08-05 00:00:00
Tasks: Chunking, Segmentation

This work presents a fine-grained, text-chunking algorithm designed for the
task of multiword expressions (MWEs) segmentation. As a lexical class, MWEs
include a wide variety of idioms, whose automatic identification are a
necessity for the handling of colloquial language. This algorithm's core
novelty is its use of non-word tokens, i.e., boundaries, in a bottom-up
strategy. Leveraging boundaries refines token-level information, forging
high-level performance from relatively basic data. The generality of this
model's feature space allows for its application across languages and domains.
Experiments spanning 19 different languages exhibit a broadly-applicable,
state-of-the-art model. Evaluation against recent shared-task data places text
partitioning as the overall, best performing MWE segmentation algorithm,
covering all MWE classes and multiple English domains (including user-generated
text). This performance, coupled with a non-combinatorial, fast-running design,
produces an ideal combination for implementations at scale, which are
facilitated through the release of open-source software.
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch-Prox
Authors: Jialei Wang, Weiran Wang, Nathan Srebro
Date: 2017-02-21 00:00:00
Tasks: Stochastic Optimization

We present and analyze an approach for distributed stochastic optimization
which is statistically optimal and achieves near-linear speedups (up to
logarithmic factors). Our approach allows a communication-memory tradeoff, with
either logarithmic communication but linear memory, or polynomial communication
and a corresponding polynomial reduction in required memory. This
communication-memory tradeoff is achieved through minibatch-prox iterations
(minibatch passive-aggressive updates), where a subproblem on a minibatch is
solved at each iteration. We provide a novel analysis for such a minibatch-prox
procedure which achieves the statistical optimal rate regardless of minibatch
size and smoothness, thus significantly improving on prior work.
Associative Embedding: End-to-End Learning for Joint Detection and Grouping
Authors: Alejandro Newell, Zhiao Huang, Jia Deng
Date: 2016-11-16 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: 2D Human Pose Estimation, Instance Segmentation, Keypoint Detection, Multi-Person Pose Estimation, Pose Estimation, Segmentation

We introduce associative embedding, a novel method for supervising
convolutional neural networks for the task of detection and grouping. A number
of computer vision problems can be framed in this manner including multi-person
pose estimation, instance segmentation, and multi-object tracking. Usually the
grouping of detections is achieved with multi-stage pipelines, instead we
propose an approach that teaches a network to simultaneously output detections
and group assignments. This technique can be easily integrated into any
state-of-the-art network architecture that produces pixel-wise predictions. We
show how to apply this method to both multi-person pose estimation and instance
segmentation and report state-of-the-art performance for multi-person pose on
the MPII and MS-COCO datasets.
Manifold Regularized Slow Feature Analysis for Dynamic Texture Recognition
Authors: Jie Miao, Xiangmin Xu, Xiaofen Xing, DaCheng Tao
Date: 2017-06-09 00:00:00
Tasks: Dynamic Texture Recognition, Scene Recognition

Dynamic textures exist in various forms, e.g., fire, smoke, and traffic jams,
but recognizing dynamic texture is challenging due to the complex temporal
variations. In this paper, we present a novel approach stemmed from slow
feature analysis (SFA) for dynamic texture recognition. SFA extracts slowly
varying features from fast varying signals. Fortunately, SFA is capable to
leach invariant representations from dynamic textures. However, complex
temporal variations require high-level semantic representations to fully
achieve temporal slowness, and thus it is impractical to learn a high-level
representation from dynamic textures directly by SFA. In order to learn a
robust low-level feature to resolve the complexity of dynamic textures, we
propose manifold regularized SFA (MR-SFA) by exploring the neighbor
relationship of the initial state of each temporal transition and retaining the
locality of their variations. Therefore, the learned features are not only
slowly varying, but also partly predictable. MR-SFA for dynamic texture
recognition is proposed in the following steps: 1) learning feature extraction
functions as convolution filters by MR-SFA, 2) extracting local features by
convolution and pooling, and 3) employing Fisher vectors to form a video-level
representation for classification. Experimental results on dynamic texture and
dynamic scene recognition datasets validate the effectiveness of the proposed
approach.
Symmetry Learning for Function Approximation in Reinforcement Learning
Authors: Anuj Mahajan, Theja Tulabandhula
Date: 2017-06-09 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper we explore methods to exploit symmetries for ensuring sample
efficiency in reinforcement learning (RL), this problem deserves ever
increasing attention with the recent advances in the use of deep networks for
complex RL tasks which require large amount of training data. We introduce a
novel method to detect symmetries using reward trails observed during episodic
experience and prove its completeness. We also provide a framework to
incorporate the discovered symmetries for functional approximation. Finally we
show that the use of potential based reward shaping is especially effective for
our symmetry exploitation mechanism. Experiments on various classical problems
show that our method improves the learning performance significantly by
utilizing symmetry information.
Predicting with Distributions
Authors: Michael Kearns, Zhiwei Steven Wu
Date: 2016-06-03 00:00:00
Tasks: General Classification, PAC learning

We consider a new learning model in which a joint distribution over vector
pairs $(x,y)$ is determined by an unknown function $c(x)$ that maps input
vectors $x$ not to individual outputs, but to entire {\em distributions\/} over
output vectors $y$. Our main results take the form of rather general reductions
from our model to algorithms for PAC learning the function class and the
distribution class separately, and show that virtually every such combination
yields an efficient algorithm in our model. Our methods include a randomized
reduction to classification noise and an application of Le Cam's method to
obtain robust learning algorithms.
Binary Paragraph Vectors
Authors: Karol Grzegorczyk, Marcin Kurdziel
Date: 2016-11-03 00:00:00
Proceeding: WS 2017 8
Tasks: Information Retrieval, Retrieval, Transfer Learning

Recently Le & Mikolov described two log-linear models, called Paragraph
Vector, that can be used to learn state-of-the-art distributed representations
of documents. Inspired by this work, we present Binary Paragraph Vector models:
simple neural networks that learn short binary codes for fast information
retrieval. We show that binary paragraph vectors outperform autoencoder-based
binary codes, despite using fewer bits. We also evaluate their precision in
transfer learning settings, where binary codes are inferred for documents
unrelated to the training corpus. Results from these experiments indicate that
binary paragraph vectors can capture semantics relevant for various
domain-specific documents. Finally, we present a model that simultaneously
learns short binary codes and longer, real-valued representations. This model
can be used to rapidly retrieve a short list of highly relevant documents from
a large document collection.
Phase-error estimation and image reconstruction from digital-holography data using a Bayesian framework
Authors: Casey J. Pellizzari, Mark F. Spencer, Charles A. Bouman
Date: 2017-06-09 00:00:00
Tasks: Image Reconstruction

The estimation of phase errors from digital-holography data is critical for
applications such as imaging or wave-front sensing. Conventional techniques
require multiple i.i.d. data and perform poorly in the presence of high noise
or large phase errors. In this paper we propose a method to estimate
isoplanatic phase errors from a single data realization. We develop a
model-based iterative reconstruction algorithm which computes the maximum a
posteriori estimate of the phase and the speckle-free object reflectance. Using
simulated data, we show that the algorithm is robust against high noise and
strong phase errors.
Somoclu: An Efficient Parallel Library for Self-Organizing Maps
Authors: Peter Wittek, Shi Chao Gao, Ik Soo Lim, Li Zhao
Date: 2013-05-07 00:00:00

Somoclu is a massively parallel tool for training self-organizing maps on
large data sets written in C++. It builds on OpenMP for multicore execution,
and on MPI for distributing the workload across the nodes in a cluster. It is
also able to boost training by using CUDA if graphics processing units are
available. A sparse kernel is included, which is useful for high-dimensional
but sparse data, such as the vector spaces common in text mining workflows.
Python, R and MATLAB interfaces facilitate interactive use. Apart from fast
execution, memory use is highly optimized, enabling training large emergent
maps even on a single computer.