pwc-archive/papers-with-abstracts huggingface dataset:
Scene recognition with CNNs: objects, scales and dataset bias
Authors: Luis Herranz, Shuqiang Jiang, Xiang-Yang Li
Date: 2018-01-21 00:00:00
Proceeding: CVPR 2016 6
Tasks: Scene Recognition

Since scenes are composed in part of objects, accurate recognition of scenes
requires knowledge about both scenes and objects. In this paper we address two
related problems: 1) scale induced dataset bias in multi-scale convolutional
neural network (CNN) architectures, and 2) how to combine effectively
scene-centric and object-centric knowledge (i.e. Places and ImageNet) in CNNs.
An earlier attempt, Hybrid-CNN, showed that incorporating ImageNet did not help
much. Here we propose an alternative method taking the scale into account,
resulting in significant recognition gains. By analyzing the response of
ImageNet-CNNs and Places-CNNs at different scales we find that both operate in
different scale ranges, so using the same network for all the scales induces
dataset bias resulting in limited performance. Thus, adapting the feature
extractor to each particular scale (i.e. scale-specific CNNs) is crucial to
improve recognition, since the objects in the scenes have their specific range
of scales. Experimental results show that the recognition accuracy highly
depends on the scale, and that simple yet carefully chosen multi-scale
combinations of ImageNet-CNNs and Places-CNNs, can push the state-of-the-art
recognition accuracy in SUN397 up to 66.26% (and even 70.17% with deeper
architectures, comparable to human performance).
Detecting Volcano Deformation in InSAR using Deep learning
Authors: N. Anantrasirichai, F. Albino, P. Hill, D. Bull, J. Biggs
Date: 2018-01-21 00:00:00
Tasks: Deep Learning, Event Detection

Globally 800 million people live within 100 km of a volcano and currently
1500 volcanoes are considered active, but half of these have no ground-based
monitoring. Alternatively, satellite radar (InSAR) can be employed to observe
volcanic ground deformation, which has shown a significant statistical link to
eruptions. Modern satellites provide large coverage with high resolution
signals, leading to huge amounts of data. The explosion in data has brought
major challenges associated with timely dissemination of information and
distinguishing volcano deformation patterns from noise, which currently relies
on manual inspection. Moreover, volcano observatories still lack expertise to
exploit satellite datasets, particularly in developing countries. This paper
presents a novel approach to detect volcanic ground deformation automatically
from wrapped-phase InSAR images. Convolutional neural networks (CNN) are
employed to detect unusual patterns within the radar data.
Review: Metaheuristic Search-Based Fuzzy Clustering Algorithms
Authors: Waleed Alomoush, Ayat Alrosan
Date: 2018-01-21 00:00:00
Tasks: Clustering

Fuzzy clustering is a famous unsupervised learning method used to collecting
similar data elements within cluster according to some similarity measurement.
But, clustering algorithms suffer from some drawbacks. Among the main weakness
including, selecting the initial cluster centres and the appropriate clusters
number is normally unknown. These weaknesses are considered the most
challenging tasks in clustering algorithms. This paper introduces a
comprehensive review of metahueristic search to solve fuzzy clustering
algorithms problems.
Avoiding Discrimination through Causal Reasoning
Authors: Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf
Date: 2017-06-08 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Attribute, Fairness

Recent work on fairness in machine learning has focused on various
statistical discrimination criteria and how they trade off. Most of these
criteria are observational: They depend only on the joint distribution of
predictor, protected attribute, features, and outcome. While convenient to work
with, observational criteria have severe inherent limitations that prevent them
from resolving matters of fairness conclusively.
  Going beyond observational criteria, we frame the problem of discrimination
based on protected attributes in the language of causal reasoning. This
viewpoint shifts attention from "What is the right fairness criterion?" to
"What do we want to assume about the causal data generating process?" Through
the lens of causality, we make several contributions. First, we crisply
articulate why and when observational criteria fail, thus formalizing what was
before a matter of opinion. Second, our approach exposes previously ignored
subtleties and why they are fundamental to the problem. Finally, we put forward
natural causal non-discrimination criteria and develop algorithms that satisfy
them.
Dense Recurrent Neural Networks for Scene Labeling
Authors: Heng Fan, Haibin Ling
Date: 2018-01-21 00:00:00
Tasks: Scene Labeling

Recently recurrent neural networks (RNNs) have demonstrated the ability to
improve scene labeling through capturing long-range dependencies among image
units. In this paper, we propose dense RNNs for scene labeling by exploring
various long-range semantic dependencies among image units. In comparison with
existing RNN based approaches, our dense RNNs are able to capture richer
contextual dependencies for each image unit via dense connections between each
pair of image units, which significantly enhances their discriminative power.
Besides, to select relevant and meanwhile restrain irrelevant dependencies for
each unit from dense connections, we introduce an attention model into dense
RNNs. The attention model enables automatically assigning more importance to
helpful dependencies while less weight to unconcerned dependencies. Integrating
with convolutional neural networks (CNNs), our method achieves state-of-the-art
performances on the PASCAL Context, MIT ADE20K and SiftFlow benchmarks.
Neural Multi-task Learning in Automated Assessment
Authors: Ronan Cummins, Marek Rei
Date: 2018-01-21 00:00:00
Tasks: Automated Essay Scoring, BIG-bench Machine Learning, Grammatical Error Detection, Multi-Task Learning

Grammatical error detection and automated essay scoring are two tasks in the
area of automated assessment. Traditionally these tasks have been treated
independently with different machine learning models and features used for each
task. In this paper, we develop a multi-task neural network model that jointly
optimises for both tasks, and in particular we show that neural automated essay
scoring can be significantly improved. We show that while the essay score
provides little evidence to inform grammatical error detection, the essay score
is highly influenced by error detection.
A Survey of Word Embeddings Evaluation Methods
Authors: Amir Bakarov
Date: 2018-01-21 00:00:00
Tasks: Embeddings Evaluation, Survey, Word Embeddings

Word embeddings are real-valued word representations able to capture lexical
semantics and trained on natural language corpora. Models proposing these
representations have gained popularity in the recent years, but the issue of
the most adequate evaluation method still remains open. This paper presents an
extensive overview of the field of word embeddings evaluation, highlighting
main problems and proposing a typology of approaches to evaluation, summarizing
16 intrinsic methods and 12 extrinsic methods. I describe both widely-used and
experimental methods, systematize information about evaluation datasets and
discuss some key challenges.
Decoupled Learning for Factorial Marked Temporal Point Processes
Authors: Weichang Wu, Junchi Yan, Xiaokang Yang, Hongyuan Zha
Date: 2018-01-21 00:00:00
Tasks: Point Processes

This paper introduces the factorial marked temporal point process model and
presents efficient learning methods. In conventional (multi-dimensional) marked
temporal point process models, event is often encoded by a single discrete
variable i.e. a marker. In this paper, we describe the factorial marked point
processes whereby time-stamped event is factored into multiple markers.
Accordingly the size of the infectivity matrix modeling the effect between
pairwise markers is in power order w.r.t. the number of the discrete marker
space. We propose a decoupled learning method with two learning procedures: i)
directly solving the model based on two techniques: Alternating Direction
Method of Multipliers and Fast Iterative Shrinkage-Thresholding Algorithm; ii)
involving a reformulation that transforms the original problem into a Logistic
Regression model for more efficient learning. Moreover, a sparse group
regularizer is added to identify the key profile features and event labels.
Empirical results on real world datasets demonstrate the efficiency of our
decoupled and reformulated method. The source code is available online.
Curvature-based Comparison of Two Neural Networks
Authors: Tao Yu, Huan Long, John E. Hopcroft
Date: 2018-01-21 00:00:00
Tasks: Vocal Bursts Valence Prediction

In this paper we show the similarities and differences of two deep neural
networks by comparing the manifolds composed of activation vectors in each
fully connected layer of them. The main contribution of this paper includes 1)
a new data generating algorithm which is crucial for determining the dimension
of manifolds; 2) a systematic strategy to compare manifolds. Especially, we
take Riemann curvature and sectional curvature as part of criterion, which can
reflect the intrinsic geometric properties of manifolds. Some interesting
results and phenomenon are given, which help in specifying the similarities and
differences between the features extracted by two networks and demystifying the
intrinsic mechanism of deep neural networks.
Depth CNNs for RGB-D scene recognition: learning from scratch better than transferring from RGB-CNNs
Authors: Xinhang Song, Luis Herranz, Shuqiang Jiang
Date: 2018-01-21 00:00:00
Tasks: Scene Recognition

Scene recognition with RGB images has been extensively studied and has
reached very remarkable recognition levels, thanks to convolutional neural
networks (CNN) and large scene datasets. In contrast, current RGB-D scene data
is much more limited, so often leverages RGB large datasets, by transferring
pretrained RGB CNN models and fine-tuning with the target RGB-D dataset.
However, we show that this approach has the limitation of hardly reaching
bottom layers, which is key to learn modality-specific features. In contrast,
we focus on the bottom layers, and propose an alternative strategy to learn
depth features combining local weakly supervised training from patches followed
by global fine tuning with images. This strategy is capable of learning very
discriminative depth-specific features with limited depth images, without
resorting to Places-CNN. In addition we propose a modified CNN architecture to
further match the complexity of the model and the amount of data available. For
RGB-D scene recognition, depth and RGB features are combined by projecting them
in a common space and further leaning a multilayer classifier, which is jointly
optimized in an end-to-end network. Our framework achieves state-of-the-art
accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data.
Attentive Recurrent Tensor Model for Community Question Answering
Authors: Gaurav Bhatt, Shivam Sharma, Balasubramanian Raman
Date: 2018-01-21 00:00:00
Tasks: Answer Selection, Community Question Answering, L2 Regularization, model, Question Answering, Sentence

A major challenge to the problem of community question answering is the
lexical and semantic gap between the sentence representations. Some solutions
to minimize this gap includes the introduction of extra parameters to deep
models or augmenting the external handcrafted features. In this paper, we
propose a novel attentive recurrent tensor network for solving the lexical and
semantic gap in community question answering. We introduce token-level and
phrase-level attention strategy that maps input sequences to the output using
trainable parameters. Further, we use the tensor parameters to introduce a
3-way interaction between question, answer and external features in vector
space. We introduce simplified tensor matrices with L2 regularization that
results in smooth optimization during training. The proposed model achieves
state-of-the-art performance on the task of answer sentence selection (TrecQA
and WikiQA datasets) while outperforming the current state-of-the-art on the
tasks of best answer selection (Yahoo! L4) and answer triggering task (WikiQA).
Decoupled Learning for Conditional Adversarial Networks
Authors: Zhifei Zhang, Yang song, Hairong Qi
Date: 2018-01-21 00:00:00
Tasks: Image Generation

Incorporating encoding-decoding nets with adversarial nets has been widely
adopted in image generation tasks. We observe that the state-of-the-art
achievements were obtained by carefully balancing the reconstruction loss and
adversarial loss, and such balance shifts with different network structures,
datasets, and training strategies. Empirical studies have demonstrated that an
inappropriate weight between the two losses may cause instability, and it is
tricky to search for the optimal setting, especially when lacking prior
knowledge on the data and network.
  This paper gives the first attempt to relax the need of manual balancing by
proposing the concept of \textit{decoupled learning}, where a novel network
structure is designed that explicitly disentangles the backpropagation paths of
the two losses.
  Experimental results demonstrate the effectiveness, robustness, and
generality of the proposed method. The other contribution of the paper is the
design of a new evaluation metric to measure the image quality of generative
models. We propose the so-called \textit{normalized relative discriminative
score} (NRDS), which introduces the idea of relative comparison, rather than
providing absolute estimates like existing metrics.
A PDE-based log-agnostic illumination correction algorithm
Authors: U. A. Nnolim
Date: 2017-12-30 00:00:00
Tasks: Image Enhancement

This report presents the results of a partial differential equation
(PDE)-based image enhancement algorithm, for dynamic range compression and
illumination correction in the absence of the logarithmic function. The
proposed algorithm combines forward and reverse flows in a PDE-based
formulation. The experimental results are compared with algorithms from the
literature and indicate comparable performance in most cases.
Fair Inference On Outcomes
Authors: Razieh Nabi, Ilya Shpitser
Date: 2017-05-29 00:00:00
Tasks: Fairness

In this paper, we consider the problem of fair statistical inference
involving outcome variables. Examples include classification and regression
problems, and estimating treatment effects in randomized trials or
observational data. The issue of fairness arises in such problems where some
covariates or treatments are "sensitive," in the sense of having potential of
creating discrimination. In this paper, we argue that the presence of
discrimination can be formalized in a sensible way as the presence of an effect
of a sensitive covariate on the outcome along certain causal pathways, a view
which generalizes (Pearl, 2009). A fair outcome model can then be learned by
solving a constrained optimization problem. We discuss a number of
complications that arise in classical statistical inference due to this view
and provide workarounds based on recent work in causal and semi-parametric
inference.
Paraphrase Generation with Deep Reinforcement Learning
Authors: Zichao Li, Xin Jiang, Lifeng Shang, Hang Li
Date: 2017-11-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Deep Reinforcement Learning, Paraphrase Generation, Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence

Automatic generation of paraphrases from a given sentence is an important yet
challenging task in natural language processing (NLP), and plays a key role in
a number of applications such as question answering, search, and dialogue. In
this paper, we present a deep reinforcement learning approach to paraphrase
generation. Specifically, we propose a new framework for the task, which
consists of a \textit{generator} and an \textit{evaluator}, both of which are
learned from data. The generator, built as a sequence-to-sequence learning
model, can produce paraphrases given a sentence. The evaluator, constructed as
a deep matching model, can judge whether two sentences are paraphrases of each
other. The generator is first trained by deep learning and then further
fine-tuned by reinforcement learning in which the reward is given by the
evaluator. For the learning of the evaluator, we propose two methods based on
supervised learning and inverse reinforcement learning respectively, depending
on the type of available training data. Empirical study shows that the learned
evaluator can guide the generator to produce more accurate paraphrases.
Experimental results demonstrate the proposed models (the generators)
outperform the state-of-the-art methods in paraphrase generation in both
automatic evaluation and human evaluation.
Deep joint rain and haze removal from single images
Authors: Liang Shen, Zihan Yue, Quan Chen, Fan Feng, Jie Ma
Date: 2018-01-21 00:00:00
Tasks: Rain Removal

Rain removal from a single image is a challenge which has been studied for a
long time. In this paper, a novel convolutional neural network based on wavelet
and dark channel is proposed. On one hand, we think that rain streaks
correspond to high frequency component of the image. Therefore, haar wavelet
transform is a good choice to separate the rain streaks and background to some
extent. More specifically, the LL subband of a rain image is more inclined to
express the background information, while LH, HL, HH subband tend to represent
the rain streaks and the edges. On the other hand, the accumulation of rain
streaks from long distance makes the rain image look like haze veil. We extract
dark channel of rain image as a feature map in network. By increasing this
mapping between the dark channel of input and output images, we achieve haze
removal in an indirect way. All of the parameters are optimized by
back-propagation. Experiments on both synthetic and real- world datasets reveal
that our method outperforms other state-of- the-art methods from a qualitative
and quantitative perspective.
Subspace Perspective on Canonical Correlation Analysis: Dimension Reduction and Minimax Rates
Authors: Zhuang Ma, Xiao-Dong Li
Date: 2016-05-12 00:00:00
Tasks: Dimensionality Reduction, Prediction

Canonical correlation analysis (CCA) is a fundamental statistical tool for
exploring the correlation structure between two sets of random variables. In
this paper, motivated by recent success of applying CCA to learn low
dimensional representations of high dimensional objects, we propose to quantify
the estimation loss of CCA by the excess prediction loss defined through a
prediction-after-dimension-reduction framework. Such framework suggests viewing
CCA estimation as estimating the subspaces spanned by the canonical variates.
Interestedly, the proposed error metrics derived from the excess prediction
loss turn out to be closely related to the principal angles between the
subspaces spanned by the population and sample canonical variates respectively.
  We characterize the non-asymptotic minimax rates under the proposed metrics,
especially the dependency of the minimax rates on the key quantities including
the dimensions, the condition number of the covariance matrices, the canonical
correlations and the eigen-gap, with minimal assumptions on the joint
covariance matrix. To the best of our knowledge, this is the first finite
sample result that captures the effect of the canonical correlations on the
minimax rates.
Denoising Prior Driven Deep Neural Network for Image Restoration
Authors: Weisheng Dong, Peiyao Wang, Wotao Yin, Guangming Shi, Fangfang Wu, Xiaotong Lu
Date: 2018-01-21 00:00:00
Tasks: Deblurring, Denoising, Image Denoising, Image Restoration, Super-Resolution

Deep neural networks (DNNs) have shown very promising results for various image restoration (IR) tasks. However, the design of network architectures remains a major challenging for achieving further improvements. While most existing DNN-based methods solve the IR problems by directly mapping low quality images to desirable high-quality images, the observation models characterizing the image degradation processes have been largely ignored. In this paper, we first propose a denoising-based IR algorithm, whose iterative steps can be computed efficiently. Then, the iterative process is unfolded into a deep neural network, which is composed of multiple denoisers modules interleaved with back-projection (BP) modules that ensure the observation consistencies. A convolutional neural network (CNN) based denoiser that can exploit the multi-scale redundancies of natural images is proposed. As such, the proposed network not only exploits the powerful denoising ability of DNNs, but also leverages the prior of the observation model. Through end-to-end training, both the denoisers and the BP modules can be jointly optimized. Experimental results on several IR tasks, e.g., image denoisig, super-resolution and deblurring show that the proposed method can lead to very competitive and often state-of-the-art results on several IR tasks, including image denoising, deblurring and super-resolution.
Knowledge Representation for High-Level Norms and Violation Inference in Logic Programming
Authors: Babatunde Opeoluwa Akinkunmi, Moyin Florence Babalola
Date: 2018-01-20 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Most of the knowledge Representation formalisms developed for representing
prescriptive norms can be categorized as either suitable for representing
either low level or high level norms.We argue that low level norm
representations do not advance the cause of autonomy in agents in the sense
that it is not the agent itself that determines the normative position it
should be at a particular time, on the account of a more general rule. In other
words an agent on some external system for a nitty gritty prescriptions of its
obligations and prohibitions. On the other hand, high level norms which have an
explicit description of a norm's precondition and have some form of
implication, do not as they exist in the literature do not support generalized
inferences about violation like low level norm representations do. This paper
presents a logical formalism for the representation of high level norms in open
societies that enable violation inferences that detail the situation in which
the norm violation took place and the identity of the norm violation. Norms are
formalized as logic programs whose heads specify what an agent is obliged or
permitted to do when a situation arises and within what time constraint of the
situation.Each norm is also assigned an identity using some reification scheme.
The body of each logic program describes the nature of the situation in which
the agent is expected to act or desist from acting. This kind of violation is
novel in the literature.