pwc-archive/papers-with-abstracts huggingface dataset:
Radiometric Scene Decomposition: Scene Reflectance, Illumination, and Geometry from RGB-D Images
Authors: Stephen Lombardi, Ko Nishino
Date: 2016-04-05 00:00:00
Tasks: Scene Understanding

Recovering the radiometric properties of a scene (i.e., the reflectance,
illumination, and geometry) is a long-sought ability of computer vision that
can provide invaluable information for a wide range of applications.
Deciphering the radiometric ingredients from the appearance of a real-world
scene, as opposed to a single isolated object, is particularly challenging as
it generally consists of various objects with different material compositions
exhibiting complex reflectance and light interactions that are also part of the
illumination. We introduce the first method for radiometric scene decomposition
that handles those intricacies. We use RGB-D images to bootstrap geometry
recovery and simultaneously recover the complex reflectance and natural
illumination while refining the noisy initial geometry and segmenting the scene
into different material regions. Most important, we handle real-world scenes
consisting of multiple objects of unknown materials, which necessitates the
modeling of spatially-varying complex reflectance, natural illumination,
texture, interreflection and shadows. We systematically evaluate the
effectiveness of our method on synthetic scenes and demonstrate its application
to real-world scenes. The results show that rich radiometric information can be
recovered from RGB-D images and demonstrate a new role RGB-D sensors can play
for general scene understanding tasks.
Bounded Optimal Exploration in MDP
Authors: Kenji Kawaguchi
Date: 2016-04-05 00:00:00

Within the framework of probably approximately correct Markov decision
processes (PAC-MDP), much theoretical work has focused on methods to attain
near optimality after a relatively long period of learning and exploration.
However, practical concerns require the attainment of satisfactory behavior
within a short period of time. In this paper, we relax the PAC-MDP conditions
to reconcile theoretically driven exploration methods and practical needs. We
propose simple algorithms for discrete and continuous state spaces, and
illustrate the benefits of our proposed relaxation via theoretical analyses and
numerical examples. Our algorithms also maintain anytime error bounds and
average loss bounds. Our approach accommodates both Bayesian and non-Bayesian
methods.
Bayesian Optimization with Exponential Convergence
Authors: Kenji Kawaguchi, Leslie Pack Kaelbling, Tomás Lozano-Pérez
Date: 2016-04-05 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Bayesian Optimization, global-optimization

This paper presents a Bayesian optimization method with exponential
convergence without the need of auxiliary optimization and without the
delta-cover sampling. Most Bayesian optimization methods require auxiliary
optimization: an additional non-convex global optimization problem, which can
be time-consuming and hard to implement in practice. Also, the existing
Bayesian optimization method with exponential convergence requires access to
the delta-cover sampling, which was considered to be impractical. Our approach
eliminates both requirements and achieves an exponential convergence rate.
Marr Revisited: 2D-3D Alignment via Surface Normal Prediction
Authors: Aayush Bansal, Bryan Russell, Abhinav Gupta
Date: 2016-04-05 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object, Pose Prediction, Prediction, Retrieval

We introduce an approach that leverages surface normal predictions, along
with appearance cues, to retrieve 3D models for objects depicted in 2D still
images from a large CAD object library. Critical to the success of our approach
is the ability to recover accurate surface normals for objects in the depicted
scene. We introduce a skip-network model built on the pre-trained Oxford VGG
convolutional neural network (CNN) for surface normal prediction. Our model
achieves state-of-the-art accuracy on the NYUv2 RGB-D dataset for surface
normal prediction, and recovers fine object detail compared to previous
methods. Furthermore, we develop a two-stream network over the input image and
predicted surface normals that jointly learns pose and style for CAD model
retrieval. When using the predicted surface normals, our two-stream network
matches prior work using surface normals computed from RGB-D images on the task
of pose prediction, and achieves state of the art when using RGB-D input.
Finally, our two-stream network allows us to retrieve CAD models that better
match the style and pose of a depicted object compared with baseline
approaches.
A Latent Variable Recurrent Neural Network for Discourse Relation Language Models
Authors: Yangfeng Ji, Gholamreza Haffari, Jacob Eisenstein
Date: 2016-03-07 00:00:00
Tasks: Classification, Dialog Act Classification, General Classification, Implicit Discourse Relation Classification, Language Modeling, Language Modelling, Relation, Relation Classification

This paper presents a novel latent variable recurrent neural network
architecture for jointly modeling sequences of words and (possibly latent)
discourse relations between adjacent sentences. A recurrent neural network
generates individual words, thus reaping the benefits of
discriminatively-trained vector representations. The discourse relations are
represented with a latent variable, which can be predicted or marginalized,
depending on the task. The resulting model can therefore employ a training
objective that includes not only discourse relation classification, but also
word prediction. As a result, it outperforms state-of-the-art alternatives for
two tasks: implicit discourse relation classification in the Penn Discourse
Treebank, and dialog act classification in the Switchboard corpus. Furthermore,
by marginalizing over latent discourse relations at test time, we obtain a
discourse informed language model, which improves over a strong LSTM baseline.
Towards Label Imbalance in Multi-label Classification with Many Labels
Authors: Li Li, Houfeng Wang
Date: 2016-04-05 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Learning

In multi-label classification, an instance may be associated with a set of
labels simultaneously. Recently, the research on multi-label classification has
largely shifted its focus to the other end of the spectrum where the number of
labels is assumed to be extremely large. The existing works focus on how to
design scalable algorithms that offer fast training procedures and have a small
memory footprint. However they ignore and even compound another challenge - the
label imbalance problem. To address this drawback, we propose a novel
Representation-based Multi-label Learning with Sampling (RMLS) approach. To the
best of our knowledge, we are the first to tackle the imbalance problem in
multi-label classification with many labels. Our experimentations with
real-world datasets demonstrate the effectiveness of the proposed approach.
RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy
Authors: Guntis Barzdins, Didzis Gosko
Date: 2016-04-05 00:00:00
Proceeding: SEMEVAL 2016 6
Tasks: AMR Parsing, Sentence, Translation

Two extensions to the AMR smatch scoring script are presented. The first
extension com-bines the smatch scoring script with the C6.0 rule-based
classifier to produce a human-readable report on the error patterns frequency
observed in the scored AMR graphs. This first extension results in 4% gain over
the state-of-art CAMR baseline parser by adding to it a manually crafted
wrapper fixing the identified CAMR parser errors. The second extension combines
a per-sentence smatch with an en-semble method for selecting the best AMR graph
among the set of AMR graphs for the same sentence. This second modification
au-tomatically yields further 0.4% gain when ap-plied to outputs of two
nondeterministic AMR parsers: a CAMR+wrapper parser and a novel character-level
neural translation AMR parser. For AMR parsing task the character-level neural
translation attains surprising 7% gain over the carefully optimized word-level
neural translation. Overall, we achieve smatch F1=62% on the SemEval-2016
official scor-ing set and F1=67% on the LDC2015E86 test set.
Feature extraction using Latent Dirichlet Allocation and Neural Networks: A case study on movie synopses
Authors: Despoina Christou
Date: 2016-04-05 00:00:00
Tasks: Collaborative Filtering, Dimensionality Reduction

Feature extraction has gained increasing attention in the field of machine
learning, as in order to detect patterns, extract information, or predict
future observations from big data, the urge of informative features is crucial.
The process of extracting features is highly linked to dimensionality reduction
as it implies the transformation of the data from a sparse high-dimensional
space, to higher level meaningful abstractions. This dissertation employs
Neural Networks for distributed paragraph representations, and Latent Dirichlet
Allocation to capture higher level features of paragraph vectors. Although
Neural Networks for distributed paragraph representations are considered the
state of the art for extracting paragraph vectors, we show that a quick topic
analysis model such as Latent Dirichlet Allocation can provide meaningful
features too. We evaluate the two methods on the CMU Movie Summary Corpus, a
collection of 25,203 movie plot summaries extracted from Wikipedia. Finally,
for both approaches, we use K-Nearest Neighbors to discover similar movies, and
plot the projected representations using T-Distributed Stochastic Neighbor
Embedding to depict the context similarities. These similarities, expressed as
movie distances, can be used for movies recommendation. The recommended movies
of this approach are compared with the recommended movies from IMDB, which use
a collaborative filtering recommendation approach, to show that our two models
could constitute either an alternative or a supplementary recommendation
approach.
Learning Multiscale Features Directly From Waveforms
Authors: Zhenyao Zhu, Jesse H. Engel, Awni Hannun
Date: 2016-03-31 00:00:00
Tasks: speech-recognition, Speech Recognition

Deep learning has dramatically improved the performance of speech recognition
systems through learning hierarchies of features optimized for the task at
hand. However, true end-to-end learning, where features are learned directly
from waveforms, has only recently reached the performance of hand-tailored
representations based on the Fourier transform. In this paper, we detail an
approach to use convolutional filters to push past the inherent tradeoff of
temporal and frequency resolution that exists for spectral representations. At
increased computational cost, we show that increasing temporal resolution via
reduced stride and increasing frequency resolution via additional filters
delivers significant performance improvements. Further, we find more efficient
representations by simultaneously learning at multiple scales, leading to an
overall decrease in word error rate on a difficult internal speech test set by
20.7% relative to networks with the same number of parameters trained on
spectrograms.
Comparative Deep Learning of Hybrid Representations for Image Recommendations
Authors: Chenyi Lei, Dong Liu, Weiping Li, Zheng-Jun Zha, Houqiang Li
Date: 2016-04-05 00:00:00
Proceeding: CVPR 2016 6
Tasks: Deep Learning

In many image-related tasks, learning expressive and discriminative
representations of images is essential, and deep learning has been studied for
automating the learning of such representations. Some user-centric tasks, such
as image recommendations, call for effective representations of not only images
but also preferences and intents of users over images. Such representations are
termed \emph{hybrid} and addressed via a deep learning approach in this paper.
We design a dual-net deep network, in which the two sub-networks map input
images and preferences of users into a same latent semantic space, and then the
distances between images and users in the latent space are calculated to make
decisions. We further propose a comparative deep learning (CDL) method to train
the deep network, using a pair of images compared against one user to learn the
pattern of their relative distances. The CDL embraces much more training data
than naive deep learning, and thus achieves superior performance than the
latter, with no cost of increasing network complexity. Experimental results
with real-world data sets for image recommendations have shown the proposed
dual-net network and CDL greatly outperform other state-of-the-art image
recommendation solutions.
Mental Lexicon Growth Modelling Reveals the Multiplexity of the English Language
Authors: Massimo Stella, Markus Brede
Date: 2016-04-05 00:00:00

In this work we extend previous analyses of linguistic networks by adopting a
multi-layer network framework for modelling the human mental lexicon, i.e. an
abstract mental repository where words and concepts are stored together with
their linguistic patterns. Across a three-layer linguistic multiplex, we model
English words as nodes and connect them according to (i) phonological
similarities, (ii) synonym relationships and (iii) free word associations. Our
main aim is to exploit this multi-layered structure to explore the influence of
phonological and semantic relationships on lexicon assembly over time. We
propose a model of lexicon growth which is driven by the phonological layer:
words are suggested according to different orderings of insertion (e.g. shorter
word length, highest frequency, semantic multiplex features) and accepted or
rejected subject to constraints. We then measure times of network assembly and
compare these to empirical data about the age of acquisition of words. In
agreement with empirical studies in psycholinguistics, our results provide
quantitative evidence for the hypothesis that word acquisition is driven by
features at multiple levels of organisation within language.
A new TAG Formalism for Tamil and Parser Analytics
Authors: Vijay Krishna Menon, S. Rajendran, M. Anand Kumar, K. P. Soman
Date: 2016-04-05 00:00:00
Tasks: MORPH, TAG

Tree adjoining grammar (TAG) is specifically suited for morph rich and
agglutinated languages like Tamil due to its psycho linguistic features and
parse time dependency and morph resolution. Though TAG and LTAG formalisms have
been known for about 3 decades, efforts on designing TAG Syntax for Tamil have
not been entirely successful due to the complexity of its specification and the
rich morphology of Tamil language. In this paper we present a minimalistic TAG
for Tamil without much morphological considerations and also introduce a parser
implementation with some obvious variations from the XTAG system
Character-Level Neural Translation for Multilingual Media Monitoring in the SUMMA Project
Authors: Guntis Barzdins, Steve Renals, Didzis Gosko
Date: 2016-04-05 00:00:00
Proceeding: LREC 2016 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Clustering, Machine Translation, Multi-Task Learning, Segmentation, speech-recognition, Speech Recognition, Translation

The paper steps outside the comfort-zone of the traditional NLP tasks like
automatic speech recognition (ASR) and machine translation (MT) to addresses
two novel problems arising in the automated multilingual news monitoring:
segmentation of the TV and radio program ASR transcripts into individual
stories, and clustering of the individual stories coming from various sources
and languages into storylines. Storyline clustering of stories covering the
same events is an essential task for inquisitorial media monitoring. We address
these two problems jointly by engaging the low-dimensional semantic
representation capabilities of the sequence to sequence neural translation
models. To enable joint multi-task learning for multilingual neural translation
of morphologically rich languages we replace the attention mechanism with the
sliding-window mechanism and operate the sequence to sequence neural
translation model on the character-level rather than on the word-level. The
story segmentation and storyline clustering problem is tackled by examining the
low-dimensional vectors produced as a side-product of the neural translation
process. The results of this paper describe a novel approach to the automatic
story segmentation and storyline clustering problem.
Improving Trajectory Modelling for DNN-based Speech Synthesis by using Stacked Bottleneck Features and Minimum Generation Error Training
Authors: Zhizheng Wu, Simon King
Date: 2016-02-22 00:00:00
Tasks: Speech Synthesis

We propose two novel techniques --- stacking bottleneck features and minimum
generation error training criterion --- to improve the performance of deep
neural network (DNN)-based speech synthesis. The techniques address the related
issues of frame-by-frame independence and ignorance of the relationship between
static and dynamic features, within current typical DNN-based synthesis
frameworks. Stacking bottleneck features, which are an acoustically--informed
linguistic representation, provides an efficient way to include more detailed
linguistic context at the input. The minimum generation error training
criterion minimises overall output trajectory error across an utterance, rather
than minimising the error per frame independently, and thus takes into account
the interaction between static and dynamic features. The two techniques can be
easily combined to further improve performance. We present both objective and
subjective results that demonstrate the effectiveness of the proposed
techniques. The subjective results show that combining the two techniques leads
to significantly more natural synthetic speech than from conventional DNN or
long short-term memory (LSTM) recurrent neural network (RNN) systems.
Learning to Generate Posters of Scientific Papers
Authors: Yu-ting Qiang, Yanwei Fu, Yanwen Guo, Zhi-Hua Zhou, Leonid Sigal
Date: 2016-04-05 00:00:00

Researchers often summarize their work in the form of posters. Posters
provide a coherent and efficient way to convey core ideas from scientific
papers. Generating a good scientific poster, however, is a complex and time
consuming cognitive task, since such posters need to be readable, informative,
and visually aesthetic. In this paper, for the first time, we study the
challenging problem of learning to generate posters from scientific papers. To
this end, a data-driven framework, that utilizes graphical models, is proposed.
Specifically, given content to display, the key elements of a good poster,
including panel layout and attributes of each panel, are learned and inferred
from data. Then, given inferred layout and attributes, composition of graphical
elements within each panel is synthesized. To learn and validate our model, we
collect and make public a Poster-Paper dataset, which consists of scientific
papers and corresponding posters with exhaustively labelled panels and
attributes. Qualitative and quantitative results indicate the effectiveness of
our approach.
Label Distribution Learning
Authors: Xin Geng
Date: 2014-08-26 00:00:00
Tasks: Clustering, Multi-Label Learning

Although multi-label learning can deal with many problems with label
ambiguity, it does not fit some real applications well where the overall
distribution of the importance of the labels matters. This paper proposes a
novel learning paradigm named \emph{label distribution learning} (LDL) for such
kind of applications. The label distribution covers a certain number of labels,
representing the degree to which each label describes the instance. LDL is a
more general learning framework which includes both single-label and
multi-label learning as its special cases. This paper proposes six working LDL
algorithms in three ways: problem transformation, algorithm adaptation, and
specialized algorithm design. In order to compare the performance of the LDL
algorithms, six representative and diverse evaluation measures are selected via
a clustering analysis, and the first batch of label distribution datasets are
collected and made publicly available. Experimental results on one artificial
and fifteen real-world datasets show clear advantages of the specialized
algorithms, which indicates the importance of special design for the
characteristics of the LDL problem.
Transfer Prototype-based Fuzzy Clustering
Authors: Zhaohong Deng, Yizhang Jiang, Fu-Lai Chung, Hisao Ishibuchi, Kup-Sze Choi, Shitong Wang
Date: 2014-09-19 00:00:00
Tasks: Clustering, Transfer Learning

The traditional prototype based clustering methods, such as the well-known
fuzzy c-mean (FCM) algorithm, usually need sufficient data to find a good
clustering partition. If the available data is limited or scarce, most of the
existing prototype based clustering algorithms will no longer be effective.
While the data for the current clustering task may be scarce, there is usually
some useful knowledge available in the related scenes/domains. In this study,
the concept of transfer learning is applied to prototype based fuzzy clustering
(PFC). Specifically, the idea of leveraging knowledge from the source domain is
exploited to develop a set of transfer prototype based fuzzy clustering (TPFC)
algorithms. Three prototype based fuzzy clustering algorithms, namely, FCM,
fuzzy k-plane clustering (FKPC) and fuzzy subspace clustering (FSC), have been
chosen to incorporate with knowledge leveraging mechanism to develop the
corresponding transfer clustering algorithms. Novel objective functions are
proposed to integrate the knowledge of source domain with the data of target
domain for clustering in the target domain. The proposed algorithms have been
validated on different synthetic and real-world datasets and the results
demonstrate their effectiveness when compared with both the original prototype
based fuzzy clustering algorithms and the related clustering algorithms like
multi-task clustering and co-clustering.
Dueling Network Architectures for Deep Reinforcement Learning
Authors: Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas
Date: 2015-11-20 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years there have been many successes of using deep representations
in reinforcement learning. Still, many of these applications use conventional
architectures, such as convolutional networks, LSTMs, or auto-encoders. In this
paper, we present a new neural network architecture for model-free
reinforcement learning. Our dueling network represents two separate estimators:
one for the state value function and one for the state-dependent action
advantage function. The main benefit of this factoring is to generalize
learning across actions without imposing any change to the underlying
reinforcement learning algorithm. Our results show that this architecture leads
to better policy evaluation in the presence of many similar-valued actions.
Moreover, the dueling architecture enables our RL agent to outperform the
state-of-the-art on the Atari 2600 domain.
Modeling Relational Information in Question-Answer Pairs with Convolutional Neural Networks
Authors: Aliaksei Severyn, Alessandro Moschitti
Date: 2016-04-05 00:00:00
Tasks: Sentence

In this paper, we propose convolutional neural networks for learning an
optimal representation of question and answer sentences. Their main aspect is
the use of relational information given by the matches between words from the
two members of the pair. The matches are encoded as embeddings with additional
parameters (dimensions), which are tuned by the network. These allows for
better capturing interactions between questions and answers, resulting in a
significant boost in accuracy. We test our models on two widely used answer
sentence selection benchmarks. The results clearly show the effectiveness of
our relational information, which allows our relatively simple network to
approach the state of the art.