pwc-archive/papers-with-abstracts huggingface dataset:
Human-Machine Inference Networks For Smart Decision Making: Opportunities and Challenges
Authors: Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney
Date: 2018-01-29 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

The emerging paradigm of Human-Machine Inference Networks (HuMaINs) combines
complementary cognitive strengths of humans and machines in an intelligent
manner to tackle various inference tasks and achieves higher performance than
either humans or machines by themselves. While inference performance
optimization techniques for human-only or sensor-only networks are quite
mature, HuMaINs require novel signal processing and machine learning solutions.
In this paper, we present an overview of the HuMaINs architecture with a focus
on three main issues that include architecture design, inference algorithms
including security/privacy challenges, and application areas/use cases.
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
Authors: Albert Gatt, Emiel Krahmer
Date: 2017-03-29 00:00:00
Tasks: nlg evaluation, Survey, Text Generation

This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.
Deep Reinforcement Learning using Capsules in Advanced Game Environments
Authors: Per-Arne Andersen
Date: 2018-01-29 00:00:00
Tasks: Deep Reinforcement Learning, General Classification, image-classification, Image Classification, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement Learning (RL) is a research area that has blossomed
tremendously in recent years and has shown remarkable potential for artificial
intelligence based opponents in computer games. This success is primarily due
to vast capabilities of Convolutional Neural Networks (ConvNet), enabling
algorithms to extract useful information from noisy environments. Capsule
Network (CapsNet) is a recent introduction to the Deep Learning algorithm group
and has only barely begun to be explored. The network is an architecture for
image classification, with superior performance for classification of the MNIST
dataset. CapsNets have not been explored beyond image classification.
  This thesis introduces the use of CapsNet for Q-Learning based game
algorithms. To successfully apply CapsNet in advanced game play, three main
contributions follow. First, the introduction of four new game environments as
frameworks for RL research with increasing complexity, namely Flash RL, Deep
Line Wars, Deep RTS, and Deep Maze. These environments fill the gap between
relatively simple and more complex game environments available for RL research
and are in the thesis used to test and explore the CapsNet behavior.
  Second, the thesis introduces a generative modeling approach to produce
artificial training data for use in Deep Learning models including CapsNets. We
empirically show that conditional generative modeling can successfully generate
game data of sufficient quality to train a Deep Q-Network well.
  Third, we show that CapsNet is a reliable architecture for Deep Q-Learning
based algorithms for game AI. A capsule is a group of neurons that determine
the presence of objects in the data and is in the literature shown to increase
the robustness of training and predictions while lowering the amount training
data needed. It should, therefore, be ideally suited for game plays.
Deep Learning Approach for Very Similar Objects Recognition Application on Chihuahua and Muffin Problem
Authors: Enkhtogtokh Togootogtokh, Amarzaya Amartuvshin
Date: 2018-01-29 00:00:00
Tasks: BIG-bench Machine Learning, Small Data Image Classification, Transfer Learning

We address the problem to tackle the very similar objects like Chihuahua or
muffin problem to recognize at least in human vision level. Our regular deep
structured machine learning still does not solve it. We saw many times for
about year in our community the problem. Today we proposed the state-of-the-art
solution for it. Our approach is quite tricky to get the very high accuracy. We
propose the deep transfer learning method which could be tackled all this type
of problems not limited to just Chihuahua or muffin problem. It is the best
method to train with small data set not like require huge amount data.
End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding
Authors: Effrosyni Mavroudi, Divya Bhaskara, Shahin Sefati, Haider Ali, René Vidal
Date: 2018-01-29 00:00:00
Tasks: Action Classification, Action Segmentation, Time Series, Time Series Analysis

Fine-grained action segmentation and recognition is an important yet
challenging task. Given a long, untrimmed sequence of kinematic data, the task
is to classify the action at each time frame and segment the time series into
the correct sequence of actions. In this paper, we propose a novel framework
that combines a temporal Conditional Random Field (CRF) model with a powerful
frame-level representation based on discriminative sparse coding. We introduce
an end-to-end algorithm for jointly learning the weights of the CRF model,
which include action classification and action transition costs, as well as an
overcomplete dictionary of mid-level action primitives. This results in a CRF
model that is driven by sparse coding features obtained using a discriminative
dictionary that is shared among different actions and adapted to the task of
structured output learning. We evaluate our method on three surgical tasks
using kinematic data from the JIGSAWS dataset, as well as on a food preparation
task using accelerometer data from the 50 Salads dataset. Our results show that
the proposed method performs on par or better than state-of-the-art methods.
Playing FPS Games with Deep Reinforcement Learning
Authors: Guillaume Lample, Devendra Singh Chaplot
Date: 2016-09-18 00:00:00
Tasks: Deep Reinforcement Learning, FPS Games, Game of Doom, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Advances in deep reinforcement learning have allowed autonomous agents to
perform well on Atari games, often outperforming humans, using only raw pixels
to make their decisions. However, most of these games take place in 2D
environments that are fully observable to the agent. In this paper, we present
the first architecture to tackle 3D environments in first-person shooter games,
that involve partially observable states. Typically, deep reinforcement
learning methods only utilize visual input for training. We present a method to
augment these models to exploit game feature information such as the presence
of enemies or items, during the training phase. Our model is trained to
simultaneously learn these features along with minimizing a Q-learning
objective, which is shown to dramatically improve the training speed and
performance of our agent. Our architecture is also modularized to allow
different models to be independently trained for different phases of the game.
We show that the proposed architecture substantially outperforms built-in AI
agents of the game as well as humans in deathmatch scenarios.
A new primal-dual algorithm for minimizing the sum of three functions with a linear operator
Authors: Ming Yan
Date: 2016-11-29 00:00:00

In this paper, we propose a new primal-dual algorithm for minimizing $f(x) +
g(x) + h(Ax)$, where $f$, $g$, and $h$ are proper lower semi-continuous convex
functions, $f$ is differentiable with a Lipschitz continuous gradient, and $A$
is a bounded linear operator. The proposed algorithm has some famous
primal-dual algorithms for minimizing the sum of two functions as special
cases. E.g., it reduces to the Chambolle-Pock algorithm when $f = 0$ and the
proximal alternating predictor-corrector when $g = 0$. For the general convex
case, we prove the convergence of this new algorithm in terms of the distance
to a fixed point by showing that the iteration is a nonexpansive operator. In
addition, we prove the $O(1/k)$ ergodic convergence rate in the primal-dual
gap. With additional assumptions, we derive the linear convergence rate in
terms of the distance to the fixed point. Comparing to other primal-dual
algorithms for solving the same problem, this algorithm extends the range of
acceptable parameters to ensure its convergence and has a smaller per-iteration
cost. The numerical experiments show the efficiency of this algorithm.
Scale-Space Anisotropic Total Variation for Limited Angle Tomography
Authors: Yixing Huang, Oliver Taubmann, Xiaolin Huang, Viktor Haase, Guenter Lauritsch, Andreas Maier
Date: 2017-12-19 00:00:00

This paper addresses streak reduction in limited angle tomography. Although
the iterative reweighted total variation (wTV) algorithm reduces small streaks
well, it is rather inept at eliminating large ones since total variation (TV)
regularization is scale-dependent and may regard these streaks as homogeneous
areas. Hence, the main purpose of this paper is to reduce streak artifacts at
various scales. We propose the scale-space anisotropic total variation (ssaTV)
algorithm in two different implementations. The first implementation (ssaTV-1)
utilizes an anisotropic gradient-like operator which uses 2s neighboring pixels
along the streaks' normal direction at each scale s. The second implementation
(ssaTV-2) makes use of anisotropic down-sampling and up-sampling operations,
similarly oriented along the streaks' normal direction, to apply TV
regularization at various scales. Experiments on numerical and clinical data
demonstrate that both ssaTV algorithms reduce streak artifacts more effectively
and efficiently than wTV, particularly when using multiple scales.
Multichannel Sound Event Detection Using 3D Convolutional Neural Networks for Learning Inter-channel Features
Authors: Sharath Adavanne, Archontis Politis, Tuomas Virtanen
Date: 2018-01-29 00:00:00
Tasks: Event Detection, Sound Event Detection

In this paper, we propose a stacked convolutional and recurrent neural
network (CRNN) with a 3D convolutional neural network (CNN) in the first layer
for the multichannel sound event detection (SED) task. The 3D CNN enables the
network to simultaneously learn the inter- and intra-channel features from the
input multichannel audio. In order to evaluate the proposed method,
multichannel audio datasets with different number of overlapping sound sources
are synthesized. Each of this dataset has a four-channel first-order Ambisonic,
binaural, and single-channel versions, on which the performance of SED using
the proposed method are compared to study the potential of SED using
multichannel audio. A similar study is also done with the binaural and
single-channel versions of the real-life recording TUT-SED 2017 development
dataset. The proposed method learns to recognize overlapping sound events from
multichannel features faster and performs better SED with a fewer number of
training epochs. The results show that on using multichannel Ambisonic audio in
place of single-channel audio we improve the overall F-score by 7.5%, overall
error rate by 10% and recognize 15.6% more sound events in time frames with
four overlapping sound sources.
Building Deep Networks on Grassmann Manifolds
Authors: Zhiwu Huang, Jiqing Wu, Luc van Gool
Date: 2016-11-17 00:00:00

Learning representations on Grassmann manifolds is popular in quite a few
visual recognition tasks. In order to enable deep learning on Grassmann
manifolds, this paper proposes a deep network architecture by generalizing the
Euclidean network paradigm to Grassmann manifolds. In particular, we design
full rank mapping layers to transform input Grassmannian data to more desirable
ones, exploit re-orthonormalization layers to normalize the resulting matrices,
study projection pooling layers to reduce the model complexity in the
Grassmannian context, and devise projection mapping layers to respect
Grassmannian geometry and meanwhile achieve Euclidean forms for regular output
layers. To train the Grassmann networks, we exploit a stochastic gradient
descent setting on manifolds of the connection weights, and study a matrix
generalization of backpropagation to update the structured data. The
evaluations on three visual recognition tasks show that our Grassmann networks
have clear advantages over existing Grassmann learning methods, and achieve
results comparable with state-of-the-art approaches.
Learning-based Image Reconstruction via Parallel Proximal Algorithm
Authors: Emrah Bostan, Ulugbek S. Kamilov, Laura Waller
Date: 2018-01-29 00:00:00
Tasks: Image Reconstruction

In the past decade, sparsity-driven regularization has led to advancement of
image reconstruction algorithms. Traditionally, such regularizers rely on
analytical models of sparsity (e.g. total variation (TV)). However, more recent
methods are increasingly centered around data-driven arguments inspired by deep
learning. In this letter, we propose to generalize TV regularization by
replacing the l1-penalty with an alternative prior that is trainable.
Specifically, our method learns the prior via extending the recently proposed
fast parallel proximal algorithm (FPPA) to incorporate data-adaptive proximal
operators. The proposed framework does not require additional inner iterations
for evaluating the proximal mappings of the corresponding learned prior.
Moreover, our formalism ensures that the training and reconstruction processes
share the same algorithmic structure, making the end-to-end implementation
intuitive. As an example, we demonstrate our algorithm on the problem of
deconvolution in a fluorescence microscope.
Improving Active Learning in Systematic Reviews
Authors: Gaurav Singh, James Thomas, John Shawe-Taylor
Date: 2018-01-29 00:00:00
Tasks: Active Learning

Systematic reviews are essential to summarizing the results of different
clinical and social science studies. The first step in a systematic review task
is to identify all the studies relevant to the review. The task of identifying
relevant studies for a given systematic review is usually performed manually,
and as a result, involves substantial amounts of expensive human resource.
Lately, there have been some attempts to reduce this manual effort using active
learning. In this work, we build upon some such existing techniques, and
validate by experimenting on a larger and comprehensive dataset than has been
attempted until now. Our experiments provide insights on the use of different
feature extraction models for different disciplines. More importantly, we
identify that a naive active learning based screening process is biased in
favour of selecting similar documents. We aimed to improve the performance of
the screening process using a novel active learning algorithm with success.
Additionally, we propose a mechanism to choose the best feature extraction
method for a given review.
Neural system identification for large populations separating "what" and "where"
Authors: David A. Klindt, Alexander S. Ecker, Thomas Euler, Matthias Bethge
Date: 2017-11-07 00:00:00
Proceeding: NeurIPS 2017

Neuroscientists classify neurons into different types that perform similar
computations at different locations in the visual field. Traditional methods
for neural system identification do not capitalize on this separation of 'what'
and 'where'. Learning deep convolutional feature spaces that are shared among
many neurons provides an exciting path forward, but the architectural design
needs to account for data limitations: While new experimental techniques enable
recordings from thousands of neurons, experimental time is limited so that one
can sample only a small fraction of each neuron's response space. Here, we show
that a major bottleneck for fitting convolutional neural networks (CNNs) to
neural data is the estimation of the individual receptive field locations, a
problem that has been scratched only at the surface thus far. We propose a CNN
architecture with a sparse readout layer factorizing the spatial (where) and
feature (what) dimensions. Our network scales well to thousands of neurons and
short recordings and can be trained end-to-end. We evaluate this architecture
on ground-truth data to explore the challenges and limitations of CNN-based
system identification. Moreover, we show that our network model outperforms
current state-of-the art system identification models of mouse primary visual
cortex.
Histogram of Oriented Depth Gradients for Action Recognition
Authors: Nachwa Abou Bakr, James Crowley
Date: 2018-01-29 00:00:00
Tasks: Action Recognition, Temporal Action Localization

In this paper, we report on experiments with the use of local measures for
depth motion for visual action recognition from MPEG encoded RGBD video
sequences. We show that such measures can be combined with local space-time
video descriptors for appearance to provide a computationally efficient method
for recognition of actions. Fisher vectors are used for encoding and
concatenating a depth descriptor with existing RGB local descriptors. We then
employ a linear SVM for recognizing manipulation actions using such vectors. We
evaluate the effectiveness of such measures by comparison to the
state-of-the-art using two recent datasets for action recognition in kitchen
environments.
Predicting Visual Features from Text for Image and Video Caption Retrieval
Authors: Jianfeng Dong, Xirong Li, Cees G. M. Snoek
Date: 2017-09-05 00:00:00
Tasks: Retrieval, Sentence, Video Description

This paper strives to find amidst a set of sentences the one best describing
the content of a given image or video. Different from existing works, which
rely on a joint subspace for their image and video caption retrieval, we
propose to do so in a visual space exclusively. Apart from this conceptual
novelty, we contribute \emph{Word2VisualVec}, a deep neural network
architecture that learns to predict a visual feature representation from
textual input. Example captions are encoded into a textual embedding based on
multi-scale sentence vectorization and further transferred into a deep visual
feature of choice via a simple multi-layer perceptron. We further generalize
Word2VisualVec for video caption retrieval, by predicting from text both 3-D
convolutional neural network features as well as a visual-audio representation.
Experiments on Flickr8k, Flickr30k, the Microsoft Video Description dataset and
the very recent NIST TrecVid challenge for video caption retrieval detail
Word2VisualVec's properties, its benefit over textual embeddings, the potential
for multimodal query composition and its state-of-the-art results.
DeepSIC: Deep Semantic Image Compression
Authors: Sihui Luo, Yezhou Yang, Mingli Song
Date: 2018-01-29 00:00:00
Tasks: Benchmarking, Image Compression, Object Recognition

Incorporating semantic information into the codecs during image compression
can significantly reduce the repetitive computation of fundamental semantic
analysis (such as object recognition) in client-side applications. The same
practice also enable the compressed code to carry the image semantic
information during storage and transmission. In this paper, we propose a
concept called Deep Semantic Image Compression (DeepSIC) and put forward two
novel architectures that aim to reconstruct the compressed image and generate
corresponding semantic representations at the same time. The first architecture
performs semantic analysis in the encoding process by reserving a portion of
the bits from the compressed code to store the semantic representations. The
second performs semantic analysis in the decoding step with the feature maps
that are embedded in the compressed code. In both architectures, the feature
maps are shared by the compression and the semantic analytics modules. To
validate our approaches, we conduct experiments on the publicly available
benchmarking datasets and achieve promising results. We also provide a thorough
analysis of the advantages and disadvantages of the proposed technique.
Using deep Q-learning to understand the tax evasion behavior of risk-averse firms
Authors: Nikolaos D. Goumagias, Dimitrios Hristu-Varsakelis, Yannis M. Assael
Date: 2018-01-29 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, Reinforcement Learning

Designing tax policies that are effective in curbing tax evasion and maximize
state revenues requires a rigorous understanding of taxpayer behavior. This
work explores the problem of determining the strategy a self-interested,
risk-averse tax entity is expected to follow, as it "navigates" - in the
context of a Markov Decision Process - a government-controlled tax environment
that includes random audits, penalties and occasional tax amnesties. Although
simplified versions of this problem have been previously explored, the mere
assumption of risk-aversion (as opposed to risk-neutrality) raises the
complexity of finding the optimal policy well beyond the reach of analytical
techniques. Here, we obtain approximate solutions via a combination of
Q-learning and recent advances in Deep Reinforcement Learning. By doing so, we
i) determine the tax evasion behavior expected of the taxpayer entity, ii)
calculate the degree of risk aversion of the "average" entity given empirical
estimates of tax evasion, and iii) evaluate sample tax policies, in terms of
expected revenues. Our model can be useful as a testbed for "in-vitro" testing
of tax policies, while our results lead to various policy recommendations.
Online Representation Learning with Single and Multi-layer Hebbian Networks for Image Classification
Authors: Yanis Bahroun, Andrea Soltoggio
Date: 2017-02-21 00:00:00
Tasks: General Classification, image-classification, Image Classification, Representation Learning

Unsupervised learning permits the development of algorithms that are able to
adapt to a variety of different data sets using the same underlying rules
thanks to the autonomous discovery of discriminating features during training.
Recently, a new class of Hebbian-like and local unsupervised learning rules for
neural networks have been developed that minimise a similarity matching
cost-function. These have been shown to perform sparse representation learning.
This study tests the effectiveness of one such learning rule for learning
features from images. The rule implemented is derived from a nonnegative
classical multidimensional scaling cost-function, and is applied to both single
and multi-layer architectures. The features learned by the algorithm are then
used as input to an SVM to test their effectiveness in classification on the
established CIFAR-10 image dataset. The algorithm performs well in comparison
to other unsupervised learning algorithms and multi-layer networks, thus
suggesting its validity in the design of a new class of compact, online
learning networks.