pwc-archive/papers-with-abstracts huggingface dataset:
Long Short-Term Memory based Convolutional Recurrent Neural Networks for Large Vocabulary Speech Recognition
Authors: Xiangang Li, Xihong Wu
Date: 2016-10-11 00:00:00
Tasks: speech-recognition, Speech Recognition

Long short-term memory (LSTM) recurrent neural networks (RNNs) have been
shown to give state-of-the-art performance on many speech recognition tasks, as
they are able to provide the learned dynamically changing contextual window of
all sequence history. On the other hand, the convolutional neural networks
(CNNs) have brought significant improvements to deep feed-forward neural
networks (FFNNs), as they are able to better reduce spectral variation in the
input signal. In this paper, a network architecture called as convolutional
recurrent neural network (CRNN) is proposed by combining the CNN and LSTM RNN.
In the proposed CRNNs, each speech frame, without adjacent context frames, is
organized as a number of local feature patches along the frequency axis, and
then a LSTM network is performed on each feature patch along the time axis. We
train and compare FFNNs, LSTM RNNs and the proposed LSTM CRNNs at various
number of configurations. Experimental results show that the LSTM CRNNs can
exceed state-of-the-art speech recognition performance.
Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation
Authors: Andrea F. Daniele, Mohit Bansal, Matthew R. Walter
Date: 2016-10-11 00:00:00
Tasks: Machine Translation, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Translation

Modern robotics applications that involve human-robot interaction require
robots to be able to communicate with humans seamlessly and effectively.
Natural language provides a flexible and efficient medium through which robots
can exchange information with their human partners. Significant advancements
have been made in developing robots capable of interpreting free-form
instructions, but less attention has been devoted to endowing robots with the
ability to generate natural language. We propose a navigational guide model
that enables robots to generate natural language instructions that allow humans
to navigate a priori unknown environments. We first decide which information to
share with the user according to their preferences, using a policy trained from
human demonstrations via inverse reinforcement learning. We then "translate"
this information into a natural language instruction using a neural
sequence-to-sequence model that learns to generate free-form instructions from
natural language corpora. We evaluate our method on a benchmark route
instruction dataset and achieve a BLEU score of 72.18% when compared to
human-generated reference instructions. We additionally conduct navigation
experiments with human participants that demonstrate that our method generates
instructions that people follow as accurately and easily as those produced by
humans.
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Authors: Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang
Date: 2016-06-16 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Articles, Question Answering, Reading Comprehension, regression

We present the Stanford Question Answering Dataset (SQuAD), a new reading
comprehension dataset consisting of 100,000+ questions posed by crowdworkers on
a set of Wikipedia articles, where the answer to each question is a segment of
text from the corresponding reading passage. We analyze the dataset to
understand the types of reasoning required to answer the questions, leaning
heavily on dependency and constituency trees. We build a strong logistic
regression model, which achieves an F1 score of 51.0%, a significant
improvement over a simple baseline (20%). However, human performance (86.8%) is
much higher, indicating that the dataset presents a good challenge problem for
future research.
  The dataset is freely available at https://stanford-qa.com
Multiple Instance Learning Convolutional Neural Networks for Object Recognition
Authors: Miao Sun, Tony X. Han, Ming-Chang Liu, Ahmad Khodayari-Rostamabad
Date: 2016-10-11 00:00:00
Tasks: Data Augmentation, Multiple Instance Learning, Object, Object Recognition, speech-recognition, Speech Recognition

Convolutional Neural Networks (CNN) have demon- strated its successful
applications in computer vision, speech recognition, and natural language
processing. For object recog- nition, CNNs might be limited by its strict label
requirement and an implicit assumption that images are supposed to be target-
object-dominated for optimal solutions. However, the labeling procedure,
necessitating laying out the locations of target ob- jects, is very tedious,
making high-quality large-scale dataset prohibitively expensive. Data
augmentation schemes are widely used when deep networks suffer the insufficient
training data problem. All the images produced through data augmentation share
the same label, which may be problematic since not all data augmentation
methods are label-preserving. In this paper, we propose a weakly supervised CNN
framework named Multiple Instance Learning Convolutional Neural Networks
(MILCNN) to solve this problem. We apply MILCNN framework to object recognition
and report state-of-the-art performance on three benchmark datasets: CIFAR10,
CIFAR100 and ILSVRC2015 classification dataset.
Latent Model Ensemble with Auto-localization
Authors: Miao Sun, Tony X. Han, Xun Xu, Ming-Chang Liu, Ahmad Khodayari-Rostamabad
Date: 2016-04-15 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, model, object-detection, Object Detection

Deep Convolutional Neural Networks (CNN) have exhibited superior performance
in many visual recognition tasks including image classification, object
detection, and scene label- ing, due to their large learning capacity and
resistance to overfit. For the image classification task, most of the current
deep CNN- based approaches take the whole size-normalized image as input and
have achieved quite promising results. Compared with the previously dominating
approaches based on feature extraction, pooling, and classification, the deep
CNN-based approaches mainly rely on the learning capability of deep CNN to
achieve superior results: the burden of minimizing intra-class variation while
maximizing inter-class difference is entirely dependent on the implicit feature
learning component of deep CNN; we rely upon the implicitly learned filters and
pooling component to select the discriminative regions, which correspond to the
activated neurons. However, if the irrelevant regions constitute a large
portion of the image of interest, the classification performance of the deep
CNN, which takes the whole image as input, can be heavily affected. To solve
this issue, we propose a novel latent CNN framework, which treats the most
discriminate region as a latent variable. We can jointly learn the global CNN
with the latent CNN to avoid the aforementioned big irrelevant region issue,
and our experimental results show the evident advantage of the proposed latent
CNN over traditional deep CNN: latent CNN outperforms the state-of-the-art
performance of deep CNN on standard benchmark datasets including the CIFAR-10,
CIFAR- 100, MNIST and PASCAL VOC 2007 Classification dataset.
PCG-Based Game Design Patterns
Authors: Michael Cook, Mirjam Eladhari, Andy Nealen, Mike Treanor, Eddy Boxerman, Alex Jaffe, Paul Sottosanti, Steve Swink
Date: 2016-10-11 00:00:00
Tasks: Game Design

People enjoy encounters with generative software, but rarely are they
encouraged to interact with, understand or engage with it. In this paper we
define the term 'PCG-based game', and explain how this concept follows on from
the idea of an AI-based game. We look at existing examples of games which
foreground their AI, put forward a methodology for designing PCG-based games,
describe some example case study designs for PCG-based games, and describe
lessons learned during this process of sketching and developing ideas.
Tangled Splines
Authors: Aditya Tatu
Date: 2016-10-10 00:00:00
Tasks: Object Recognition

Extracting shape information from object bound- aries is a well studied
problem in vision, and has found tremen- dous use in applications like object
recognition. Conversely, studying the space of shapes represented by curves
satisfying certain constraints is also intriguing. In this paper, we model and
analyze the space of shapes represented by a 3D curve (space curve) formed by
connecting n pieces of quarter of a unit circle. Such a space curve is what we
call a Tangle, the name coming from a toy built on the same principle. We
provide two models for the shape space of n-link open and closed tangles, and
we show that tangles are a subset of trigonometric splines of a certain order.
We give algorithms for curve approximation using open/closed tangles, computing
geodesics on these shape spaces, and to find the deformation that takes one
given tangle to another given tangle, i.e., the Log map. The algorithms
provided yield tangles upto a small and acceptable tolerance, as shown by the
results given in the paper.
Language classification from bilingual word embedding graphs
Authors: Steffen Eger, Armin Hoenen, Alexander Mehler
Date: 2016-07-18 00:00:00
Proceeding: COLING 2016 12
Tasks: Classification, General Classification, Word Embeddings

We study the role of the second language in bilingual word embeddings in
monolingual semantic evaluation tasks. We find strongly and weakly positive
correlations between down-stream task performance and second language
similarity to the target language. Additionally, we show how bilingual word
embeddings can be employed for the task of semantic language classification and
that joint semantic spaces vary in meaningful ways across second languages. Our
results support the hypothesis that semantic language similarity is influenced
by both structural similarity as well as geography/contact.
Polysemous codes
Authors: Matthijs Douze, Hervé Jégou, Florent Perronnin
Date: 2016-09-07 00:00:00
Tasks: Quantization

This paper considers the problem of approximate nearest neighbor search in
the compressed domain. We introduce polysemous codes, which offer both the
distance estimation quality of product quantization and the efficient
comparison of binary codes with Hamming distance. Their design is inspired by
algorithms introduced in the 90's to construct channel-optimized vector
quantizers. At search time, this dual interpretation accelerates the search.
Most of the indexed vectors are filtered out with Hamming distance, letting
only a fraction of the vectors to be ranked with an asymmetric distance
estimator.
  The method is complementary with a coarse partitioning of the feature space
such as the inverted multi-index. This is shown by our experiments performed on
several public benchmarks such as the BIGANN dataset comprising one billion
vectors, for which we report state-of-the-art results for query times below
0.3\,millisecond per core. Last but not least, our approach allows the
approximate computation of the k-NN graph associated with the Yahoo Flickr
Creative Commons 100M, described by CNN image descriptors, in less than 8 hours
on a single machine.
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
Authors: Vardan Papyan, Yaniv Romano, Michael Elad
Date: 2016-07-27 00:00:00
Tasks: Attribute

Convolutional neural networks (CNN) have led to many state-of-the-art results
spanning through various fields. However, a clear and profound theoretical
understanding of the forward pass, the core algorithm of CNN, is still lacking.
In parallel, within the wide field of sparse approximation, Convolutional
Sparse Coding (CSC) has gained increasing attention in recent years. A
theoretical study of this model was recently conducted, establishing it as a
reliable and stable alternative to the commonly practiced patch-based
processing. Herein, we propose a novel multi-layer model, ML-CSC, in which
signals are assumed to emerge from a cascade of CSC layers. This is shown to be
tightly connected to CNN, so much so that the forward pass of the CNN is in
fact the thresholding pursuit serving the ML-CSC model. This connection brings
a fresh view to CNN, as we are able to attribute to this architecture
theoretical claims such as uniqueness of the representations throughout the
network, and their stable estimation, all guaranteed under simple local
sparsity conditions. Lastly, identifying the weaknesses in the above pursuit
scheme, we propose an alternative to the forward pass, which is connected to
deconvolutional, recurrent and residual networks, and has better theoretical
guarantees.
Limits to Verification and Validation of Agentic Behavior
Authors: David J. Jilk
Date: 2016-04-23 00:00:00

Verification and validation of agentic behavior have been suggested as
important research priorities in efforts to reduce risks associated with the
creation of general artificial intelligence (Russell et al 2015). In this paper
we question the appropriateness of using language of certainty with respect to
efforts to manage that risk. We begin by establishing a very general formalism
to characterize agentic behavior and to describe standards of acceptable
behavior. We show that determination of whether an agent meets any particular
standard is not computable. We discuss the extent of the burden associated with
verification by manual proof and by automated behavioral governance. We show
that to ensure decidability of the behavioral standard itself, one must further
limit the capabilities of the agent. We then demonstrate that if our concerns
relate to outcomes in the physical world, attempts at validation are futile.
Finally, we show that layered architectures aimed at making these challenges
tractable mistakenly equate intentions with actions or outcomes, thereby
failing to provide any guarantees. We conclude with a discussion of why
language of certainty should be eradicated from the conversation about the
safety of general artificial intelligence.
Leveraging Recurrent Neural Networks for Multimodal Recognition of Social Norm Violation in Dialog
Authors: Tiancheng Zhao, Ran Zhao, Zhao Meng, Justine Cassell
Date: 2016-10-10 00:00:00

Social norms are shared rules that govern and facilitate social interaction.
Violating such social norms via teasing and insults may serve to upend power
imbalances or, on the contrary reinforce solidarity and rapport in
conversation, rapport which is highly situated and context-dependent. In this
work, we investigate the task of automatically identifying the phenomena of
social norm violation in discourse. Towards this goal, we leverage the power of
recurrent neural networks and multimodal information present in the
interaction, and propose a predictive model to recognize social norm violation.
Using long-term temporal and contextual information, our model achieves an F1
score of 0.705. Implications of our work regarding developing a social-aware
agent are discussed.
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
Authors: Alex Kendall, Vijay Badrinarayanan, Roberto Cipolla
Date: 2015-11-09 00:00:00
Tasks: Decision Making, Decoder, Low-Light Image Enhancement, Scene Understanding, Segmentation, Semantic Segmentation

We present a deep learning framework for probabilistic pixel-wise semantic
segmentation, which we term Bayesian SegNet. Semantic segmentation is an
important tool for visual scene understanding and a meaningful measure of
uncertainty is essential for decision making. Our contribution is a practical
system which is able to predict pixel-wise class labels with a measure of model
uncertainty. We achieve this by Monte Carlo sampling with dropout at test time
to generate a posterior distribution of pixel class labels. In addition, we
show that modelling uncertainty improves segmentation performance by 2-3%
across a number of state of the art architectures such as SegNet, FCN and
Dilation Network, with no additional parametrisation. We also observe a
significant improvement in performance for smaller datasets where modelling
uncertainty is more effective. We benchmark Bayesian SegNet on the indoor SUN
Scene Understanding and outdoor CamVid driving scenes datasets.
Supervised Term Weighting Metrics for Sentiment Analysis in Short Text
Authors: Hussam Hamdan, Patrice Bellot, Frederic Bechet
Date: 2016-10-10 00:00:00
Tasks: General Classification, Information Retrieval, Retrieval, Sentiment Analysis, text-classification, Text Classification

Term weighting metrics assign weights to terms in order to discriminate the
important terms from the less crucial ones. Due to this characteristic, these
metrics have attracted growing attention in text classification and recently in
sentiment analysis. Using the weights given by such metrics could lead to more
accurate document representation which may improve the performance of the
classification. While previous studies have focused on proposing or comparing
different weighting metrics at two-classes document level sentiment analysis,
this study propose to analyse the results given by each metric in order to find
out the characteristics of good and bad weighting metrics. Therefore we present
an empirical study of fifteen global supervised weighting metrics with four
local weighting metrics adopted from information retrieval, we also give an
analysis to understand the behavior of each metric by observing and analysing
how each metric distributes the terms and deduce some characteristics which may
distinguish the good and bad metrics. The evaluation has been done using
Support Vector Machine on three different datasets: Twitter, restaurant and
laptop reviews.
Godseed: Benevolent or Malevolent?
Authors: Eray Özkural
Date: 2014-02-01 00:00:00
Tasks: AI Agent, Cultural Vocal Bursts Intensity Prediction

It is hypothesized by some thinkers that benign looking AI objectives may
result in powerful AI drives that may pose an existential risk to human
society. We analyze this scenario and find the underlying assumptions to be
unlikely. We examine the alternative scenario of what happens when universal
goals that are not human-centric are used for designing AI agents. We follow a
design approach that tries to exclude malevolent motivations from AI agents,
however, we see that objectives that seem benevolent may pose significant risk.
We consider the following meta-rules: preserve and pervade life and culture,
maximize the number of free minds, maximize intelligence, maximize wisdom,
maximize energy production, behave like human, seek pleasure, accelerate
evolution, survive, maximize control, and maximize capital. We also discuss
various solution approaches for benevolent behavior including selfless goals,
hybrid designs, Darwinism, universal constraints, semi-autonomy, and
generalization of robot laws. A "prime directive" for AI may help in
formulating an encompassing constraint for avoiding malicious behavior. We
hypothesize that social instincts for autonomous robots may be effective such
as attachment learning. We mention multiple beneficial scenarios for an
advanced semi-autonomous AGI agent in the near future including space
exploration, automation of industries, state functions, and cities. We conclude
that a beneficial AI agent with intelligence beyond human-level is possible and
has many practical use cases.
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Authors: Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla
Date: 2015-11-02 00:00:00
Tasks: Crowd Counting, Decoder, General Classification, Image Segmentation, Lesion Segmentation, Medical Image Segmentation, Real-Time Semantic Segmentation, Scene Segmentation, Scene Understanding, Segmentation, Semantic Segmentation, Thermal Image Segmentation

We present a novel and practical deep fully convolutional neural network
architecture for semantic pixel-wise segmentation termed SegNet. This core
trainable segmentation engine consists of an encoder network, a corresponding
decoder network followed by a pixel-wise classification layer. The architecture
of the encoder network is topologically identical to the 13 convolutional
layers in the VGG16 network. The role of the decoder network is to map the low
resolution encoder feature maps to full input resolution feature maps for
pixel-wise classification. The novelty of SegNet lies is in the manner in which
the decoder upsamples its lower resolution input feature map(s). Specifically,
the decoder uses pooling indices computed in the max-pooling step of the
corresponding encoder to perform non-linear upsampling. This eliminates the
need for learning to upsample. The upsampled maps are sparse and are then
convolved with trainable filters to produce dense feature maps. We compare our
proposed architecture with the widely adopted FCN and also with the well known
DeepLab-LargeFOV, DeconvNet architectures. This comparison reveals the memory
versus accuracy trade-off involved in achieving good segmentation performance.
  SegNet was primarily motivated by scene understanding applications. Hence, it
is designed to be efficient both in terms of memory and computational time
during inference. It is also significantly smaller in the number of trainable
parameters than other competing architectures. We also performed a controlled
benchmark of SegNet and other architectures on both road scenes and SUN RGB-D
indoor scene segmentation tasks. We show that SegNet provides good performance
with competitive inference time and more efficient inference memory-wise as
compared to other architectures. We also provide a Caffe implementation of
SegNet and a web demo at http://mi.eng.cam.ac.uk/projects/segnet/.
A Canonical Semi-Deterministic Transducer
Authors: Achilles Beros, Colin de la Higuera
Date: 2014-05-10 00:00:00
Tasks: Translation

We prove the existence of a canonical form for semi-deterministic transducers
with incomparable sets of output strings. Based on this, we develop an
algorithm which learns semi-deterministic transducers given access to
translation queries. We also prove that there is no learning algorithm for
semi-deterministic transducers that uses only domain knowledge.
Dynamic Metric Learning from Pairwise Comparisons
Authors: Kristjan Greenewald, Stephen Kelley, Alfred Hero III
Date: 2016-10-10 00:00:00
Tasks: Clustering, Metric Learning, Retrieval

Recent work in distance metric learning has focused on learning
transformations of data that best align with specified pairwise similarity and
dissimilarity constraints, often supplied by a human observer. The learned
transformations lead to improved retrieval, classification, and clustering
algorithms due to the better adapted distance or similarity measures. Here, we
address the problem of learning these transformations when the underlying
constraint generation process is nonstationary. This nonstationarity can be due
to changes in either the ground-truth clustering used to generate constraints
or changes in the feature subspaces in which the class structure is apparent.
We propose Online Convex Ensemble StrongLy Adaptive Dynamic Learning (OCELAD),
a general adaptive, online approach for learning and tracking optimal metrics
as they change over time that is highly robust to a variety of nonstationary
behaviors in the changing metric. We apply the OCELAD framework to an ensemble
of online learners. Specifically, we create a retro-initialized composite
objective mirror descent (COMID) ensemble (RICE) consisting of a set of
parallel COMID learners with different learning rates, demonstrate RICE-OCELAD
on both real and synthetic data sets and show significant performance
improvements relative to previously proposed batch and online distance metric
learning algorithms.