pwc-archive/papers-with-abstracts huggingface dataset:
Quantum aspects of high dimensional formal representation of conceptual spaces
Authors: Ishwarya M S, Aswani Kumar Cherukuri
Date: 2018-06-29 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Human cognition is a complex process facilitated by the intricate
architecture of human brain. However, human cognition is often reduced to
quantum theory based events in principle because of their correlative
conjectures for the purpose of analysis for reciprocal understanding. In this
paper, we begin our analysis of human cognition via formal methods and proceed
towards quantum theories. Human cognition often violate classic probabilities
on which formal representation of conceptual spaces are built. Further,
geometric representation of conceptual spaces proposed by Gardenfors discusses
the underlying content but lacks a systematic approach (Gardenfors, 2000; Kitto
et. al, 2012). However, the aforementioned views are not contradictory but
different perspective with a gap towards sufficient understanding of human
cognitive process. A comprehensive and systematic approach to model a
relatively complex scenario can be addressed by vector space approach of
conceptual spaces as discussed in literature. In this research, we have
proposed an approach that uses both formal representation and Gardenfors
geometric approach. The proposed model of high dimensional formal
representation of conceptual space is mathematically analysed and inferred to
exhibit quantum aspects. Also, the proposed model achieves cognition, in
particular, consciousness. We have demonstrated this process of achieving
consciousness with a constructive learning scenario. We have also proposed an
algorithm for conceptual scaling of a real world scenario under different
quality dimensions to obtain a conceptual scale.
Knowledge-Based Distant Regularization in Learning Probabilistic Models
Authors: Naoya Takeishi, Kosuke Akimoto
Date: 2018-06-29 00:00:00
Tasks: Inductive Bias, Knowledge Graph Embeddings

Exploiting the appropriate inductive bias based on the knowledge of data is
essential for achieving good performance in statistical machine learning. In
practice, however, the domain knowledge of interest often provides information
on the relationship of data attributes only distantly, which hinders direct
utilization of such domain knowledge in popular regularization methods. In this
paper, we propose the knowledge-based distant regularization framework, in
which we utilize the distant information encoded in a knowledge graph for
regularization of probabilistic model estimation. In particular, we propose to
impose prior distributions on model parameters specified by knowledge graph
embeddings. As an instance of the proposed framework, we present the factor
analysis model with the knowledge-based distant regularization. We show the
results of preliminary experiments on the improvement of the generalization
capability of such model.
A flexible model for training action localization with varying levels of supervision
Authors: Guilhem Chéron, Jean-Baptiste Alayrac, Ivan Laptev, Cordelia Schmid
Date: 2018-06-29 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Action Detection, Action Localization, Clustering

Spatio-temporal action detection in videos is typically addressed in a
fully-supervised setup with manual annotation of training videos required at
every frame. Since such annotation is extremely tedious and prohibits
scalability, there is a clear need to minimize the amount of manual
supervision. In this work we propose a unifying framework that can handle and
combine varying types of less-demanding weak supervision. Our model is based on
discriminative clustering and integrates different types of supervision as
constraints on the optimization. We investigate applications of such a model to
training setups with alternative supervisory signals ranging from video-level
class labels to the full per-frame annotation of action bounding boxes.
Experiments on the challenging UCF101-24 and DALY datasets demonstrate
competitive performance of our method at a fraction of supervision used by
previous methods. The flexibility of our model enables joint learning from data
with different levels of annotation. Experimental results demonstrate a
significant gain by adding a few fully supervised examples to otherwise weakly
labeled videos.
Unsupervised Detection and Explanation of Latent-class Contextual Anomalies
Authors: Jacob Kauffmann, Grégoire Montavon, Luiz Alberto Lima, Shinichi Nakajima, Klaus-Robert Müller, Nico Görnitz
Date: 2018-06-29 00:00:00

Detecting and explaining anomalies is a challenging effort. This holds
especially true when data exhibits strong dependencies and single measurements
need to be assessed and analyzed in their respective context. In this work, we
consider scenarios where measurements are non-i.i.d, i.e. where samples are
dependent on corresponding discrete latent variables which are connected
through some given dependency structure, the contextual information. Our
contribution is twofold: (i) Building atop of support vector data description
(SVDD), we derive a method able to cope with latent-class dependency structure
that can still be optimized efficiently. We further show that our approach
neatly generalizes vanilla SVDD as well as k-means and conditional random
fields (CRF) and provide a corresponding probabilistic interpretation. (ii) In
unsupervised scenarios where it is not possible to quantify the accuracy of an
anomaly detector, having an human-interpretable solution is the key to success.
Based on deep Taylor decomposition and a reformulation of our trained anomaly
detector as a neural network, we are able to backpropagate predictions to
pixel-domain and thus identify features and regions of high relevance. We
demonstrate the usefulness of our novel approach on toy data with known
spatio-temporal structure and successfully validate on synthetic as well as
real world off-shore data from the oil industry.
Bias in Semantic and Discourse Interpretation
Authors: Nicholas Asher, Soumya Paul
Date: 2018-06-29 00:00:00

In this paper, we show how game-theoretic work on conversation combined with
a theory of discourse structure provides a framework for studying interpretive
bias. Interpretive bias is an essential feature of learning and understanding
but also something that can be used to pervert or subvert the truth. The
framework we develop here provides tools for understanding and analyzing the
range of interpretive biases and the factors that contribute to them.
Fake News Identification on Twitter with Hybrid CNN and RNN Models
Authors: Oluwaseun Ajao, Deepayan Bhowmik, Shahrzad Zargari
Date: 2018-06-29 00:00:00

The problem associated with the propagation of fake news continues to grow at
an alarming scale. This trend has generated much interest from politics to
academia and industry alike. We propose a framework that detects and classifies
fake news messages from Twitter posts using hybrid of convolutional neural
networks and long-short term recurrent neural network models. The proposed work
using this deep learning approach achieves 82% accuracy. Our approach
intuitively identifies relevant features associated with fake news stories
without previous knowledge of the domain.
Hyperspectral Image Dataset for Benchmarking on Salient Object Detection
Authors: Nevrez Imamoglu, Yu Oishi, Xiaoqiang Zhang, Guanqun Ding, Yuming Fang, Toru Kouyama, Ryosuke Nakamura
Date: 2018-06-29 00:00:00
Tasks: Benchmarking, Object, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

Many works have been done on salient object detection using supervised or
unsupervised approaches on colour images. Recently, a few studies demonstrated
that efficient salient object detection can also be implemented by using
spectral features in visible spectrum of hyperspectral images from natural
scenes. However, these models on hyperspectral salient object detection were
tested with a very few number of data selected from various online public
dataset, which are not specifically created for object detection purposes.
Therefore, here, we aim to contribute to the field by releasing a hyperspectral
salient object detection dataset with a collection of 60 hyperspectral images
with their respective ground-truth binary images and representative rendered
colour images (sRGB). We took several aspects in consideration during the data
collection such as variation in object size, number of objects,
foreground-background contrast, object position on the image, and etc. Then, we
prepared ground truth binary images for each hyperspectral data, where salient
objects are labelled on the images. Finally, we did performance evaluation
using Area Under Curve (AUC) metric on some existing hyperspectral saliency
detection models in literature.
Guaranteed Deterministic Bounds on the Total Variation Distance between Univariate Mixtures
Authors: Frank Nielsen, Ke Sun
Date: 2018-06-29 00:00:00
Tasks: Two-sample testing

The total variation distance is a core statistical distance between
probability measures that satisfies the metric axioms, with value always
falling in $[0,1]$. This distance plays a fundamental role in machine learning
and signal processing: It is a member of the broader class of $f$-divergences,
and it is related to the probability of error in Bayesian hypothesis testing.
Since the total variation distance does not admit closed-form expressions for
statistical mixtures (like Gaussian mixture models), one often has to rely in
practice on costly numerical integrations or on fast Monte Carlo approximations
that however do not guarantee deterministic lower and upper bounds. In this
work, we consider two methods for bounding the total variation of univariate
mixture models: The first method is based on the information monotonicity
property of the total variation to design guaranteed nested deterministic lower
bounds. The second method relies on computing the geometric lower and upper
envelopes of weighted mixture components to derive deterministic bounds based
on density ratio. We demonstrate the tightness of our bounds in a series of
experiments on Gaussian, Gamma and Rayleigh mixture models.
Excavate Condition-invariant Space by Intrinsic Encoder
Authors: Jian Xu, Chunheng Wang, Cunzhao Shi, Baihua Xiao
Date: 2018-06-29 00:00:00

As the human, we can recognize the places across a wide range of changing
environmental conditions such as those caused by weathers, seasons, and
day-night cycles. We excavate and memorize the stable semantic structure of
different places and scenes. For example, we can recognize tree whether the
bare tree in winter or lush tree in summer. Therefore, the intrinsic features
that are corresponding to specific semantic contents and condition-invariant of
appearance changes can be employed to improve the performance of long-term
place recognition significantly.
  In this paper, we propose a novel intrinsic encoder that excavates the
condition-invariant latent space of different places under drastic appearance
changes. Our method excavates the space of intrinsic structure and semantic
information by proposed self-supervised encoder loss. Different from previous
learning based place recognition methods that need paired training data of each
place with appearance changes, we employ the weakly-supervised strategy to
utilize unpaired set-based training data of different environmental conditions.
  We conduct comprehensive experiments and show that our semi-supervised
intrinsic encoder achieves remarkable performance for place recognition under
drastic appearance changes. The proposed intrinsic encoder outperforms the
state-of-the-art image-level place recognition methods on standard benchmark
Nordland.
Dependence in Propositional Logic: Formula-Formula Dependence and Formula Forgetting -- Application to Belief Update and Conservative Extension
Authors: Liangda Fang, Hai Wan, Xianqiao Liu, Biqing Fang, Zhaorong Lai
Date: 2018-06-29 00:00:00

Dependence is an important concept for many tasks in artificial intelligence. A task can be executed more efficiently by discarding something independent from the task. In this paper, we propose two novel notions of dependence in propositional logic: formula-formula dependence and formula forgetting. The first is a relation between formulas capturing whether a formula depends on another one, while the second is an operation that returns the strongest consequence independent of a formula. We also apply these two notions in two well-known issues: belief update and conservative extension. Firstly, we define a new update operator based on formula-formula dependence. Furthermore, we reduce conservative extension to formula forgetting.
Generate the corresponding Image from Text Description using Modified GAN-CLS Algorithm
Authors: Fuzhou Gong, Zigeng Xia
Date: 2018-06-29 00:00:00

Synthesizing images or texts automatically is a useful research area in the
artificial intelligence nowadays. Generative adversarial networks (GANs), which
are proposed by Goodfellow in 2014, make this task to be done more efficiently
by using deep neural networks. We consider generating corresponding images from
an input text description using a GAN. In this paper, we analyze the GAN-CLS
algorithm, which is a kind of advanced method of GAN proposed by Scott Reed in
2016. First, we find the problem with this algorithm through inference. Then we
correct the GAN-CLS algorithm according to the inference by modifying the
objective function of the model. Finally, we do the experiments on the
Oxford-102 dataset and the CUB dataset. As a result, our modified algorithm can
generate images which are more plausible than the GAN-CLS algorithm in some
cases. Also, some of the generated images match the input texts better.
A General Multi-agent Epistemic Planner Based on Higher-order Belief Change
Authors: Xiao Huang, Biqing Fang, Hai Wan, Yongmei Liu
Date: 2018-06-29 00:00:00

In recent years, multi-agent epistemic planning has received attention from
both dynamic logic and planning communities. Existing implementations of
multi-agent epistemic planning are based on compilation into classical planning
and suffer from various limitations, such as generating only linear plans,
restriction to public actions, and incapability to handle disjunctive beliefs.
In this paper, we propose a general representation language for multi-agent
epistemic planning where the initial KB and the goal, the preconditions and
effects of actions can be arbitrary multi-agent epistemic formulas, and the
solution is an action tree branching on sensing results. To support efficient
reasoning in the multi-agent KD45 logic, we make use of a normal form called
alternating cover disjunctive formulas (ACDFs). We propose basic revision and
update algorithms for ACDFs. We also handle static propositional common
knowledge, which we call constraints. Based on our reasoning, revision and
update algorithms, adapting the PrAO algorithm for contingent planning from the
literature, we implemented a multi-agent epistemic planner called MEPK. Our
experimental results show the viability of our approach.
Action Recognition for Depth Video using Multi-view Dynamic Images
Authors: Yang Xiao, Jun Chen, Yancheng Wang, Zhiguo Cao, Joey Tianyi Zhou, Xiang Bai
Date: 2018-06-29 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization

Dynamic imaging is a recently proposed action description paradigm for
simultaneously capturing motion and temporal evolution information,
particularly in the context of deep convolutional neural networks (CNNs).
Compared with optical flow for motion characterization, dynamic imaging
exhibits superior efficiency and compactness. Inspired by the success of
dynamic imaging in RGB video, this study extends it to the depth domain. To
better exploit three-dimensional (3D) characteristics, multi-view dynamic
images are proposed. In particular, the raw depth video is densely projected
with respect to different virtual imaging viewpoints by rotating the virtual
camera within the 3D space. Subsequently, dynamic images are extracted from the
obtained multi-view depth videos and multi-view dynamic images are thus
constructed from these images. Accordingly, more view-tolerant visual cues can
be involved. A novel CNN model is then proposed to perform feature learning on
multi-view dynamic images. Particularly, the dynamic images from different
views share the same convolutional layers but correspond to different fully
connected layers. This is aimed at enhancing the tuning effectiveness on
shallow convolutional layers by alleviating the gradient vanishing problem.
Moreover, as the spatial occurrence variation of the actions may impair the
CNN, an action proposal approach is also put forth. In experiments, the
proposed approach can achieve state-of-the-art performance on three challenging
datasets.
Gated Feedback Refinement Network for Coarse-to-Fine Dense Semantic Image Labeling
Authors: Md Amirul Islam, Mrigank Rochan, Shujon Naha, Neil D. B. Bruce, Yang Wang
Date: 2018-06-29 00:00:00
Tasks: Decoder, Segmentation, Semantic Segmentation

Effective integration of local and global contextual information is crucial
for semantic segmentation and dense image labeling. We develop two
encoder-decoder based deep learning architectures to address this problem. We
first propose a network architecture called Label Refinement Network (LRN) that
predicts segmentation labels in a coarse-to-fine fashion at several spatial
resolutions. In this network, we also define loss functions at several stages
to provide supervision at different stages of training. However, there are
limits to the quality of refinement possible if ambiguous information is passed
forward. In order to address this issue, we also propose Gated Feedback
Refinement Network (G-FRNet) that addresses this limitation. Initially, G-FRNet
makes a coarse-grained prediction which it progressively refines to recover
details by effectively integrating local and global contextual information
during the refinement stages. This is achieved by gate units proposed in this
work, that control information passed forward in order to resolve the
ambiguity. Experiments were conducted on four challenging dense labeling
datasets (CamVid, PASCAL VOC 2012, Horse-Cow Parsing, PASCAL-Person-Part, and
SUN-RGBD). G-FRNet achieves state-of-the-art semantic segmentation results on
the CamVid and Horse-Cow Parsing datasets and produces results competitive with
the best performing approaches that appear in the literature for the other
three datasets.
Collective decision for open set recognition
Authors: Chuanxing Geng, Songcan Chen
Date: 2018-06-29 00:00:00
Tasks: Open Set Learning

In open set recognition (OSR), almost all existing methods are designed specially for recognizing individual instances, even these instances are collectively coming in batch. Recognizers in decision either reject or categorize them to some known class using empirically-set threshold. Thus the decision threshold plays a key role. However, the selection for it usually depends on the knowledge of known classes, inevitably incurring risks due to lacking available information from unknown classes. On the other hand, a more realistic OSR system should NOT just rest on a reject decision but should go further, especially for discovering the hidden unknown classes among the reject instances, whereas existing OSR methods do not pay special attention. In this paper, we introduce a novel collective/batch decision strategy with an aim to extend existing OSR for new class discovery while considering correlations among the testing instances. Specifically, a collective decision-based OSR framework (CD-OSR) is proposed by slightly modifying the Hierarchical Dirichlet process (HDP). Thanks to HDP, our CD-OSR does not need to define the decision threshold and can implement the open set recognition and new class discovery simultaneously. Finally, extensive experiments on benchmark datasets indicate the validity of CD-OSR.
Leaders, Followers, and Community Detection
Authors: Dhruv Parthasarathy, Devavrat Shah, Tauhid Zaman
Date: 2010-11-02 00:00:00
Tasks: Community Detection

Communities in social networks or graphs are sets of well-connected, overlapping vertices. The effectiveness of a community detection algorithm is determined by accuracy in finding the ground-truth communities and ability to scale with the size of the data. In this work, we provide three contributions. First, we show that a popular measure of accuracy known as the F1 score, which is between 0 and 1, with 1 being perfect detection, has an information lower bound is 0.5. We provide a trivial algorithm that produces communities with an F1 score of 0.5 for any graph! Somewhat surprisingly, we find that popular algorithms such as modularity optimization, BigClam and CESNA have F1 scores less than 0.5 for the popular IMDB graph. To rectify this, as the second contribution we propose a generative model for community formation, the sequential community graph, which is motivated by the formation of social networks. Third, motivated by our generative model, we propose the leader-follower algorithm (LFA). We prove that it recovers all communities for sequential community graphs by establishing a structural result that sequential community graphs are chordal. For a large number of popular social networks, it recovers communities with a much higher F1 score than other popular algorithms. For the IMDB graph, it obtains an F1 score of 0.81. We also propose a modification to the LFA called the fast leader-follower algorithm (FLFA) which in addition to being highly accurate, is also fast, with a scaling that is almost linear in the network size.
Neural Machine Translation with Key-Value Memory-Augmented Attention
Authors: Fandong Meng, Zhaopeng Tu, Yong Cheng, Haiyang Wu, Junjie Zhai, Yuekui Yang, Di Wang
Date: 2018-06-29 00:00:00
Tasks: Decoder, Machine Translation, NMT, Sentence, Translation

Although attention-based Neural Machine Translation (NMT) has achieved
remarkable progress in recent years, it still suffers from issues of repeating
and dropping translations. To alleviate these issues, we propose a novel
key-value memory-augmented attention model for NMT, called KVMEMATT.
Specifically, we maintain a timely updated keymemory to keep track of attention
history and a fixed value-memory to store the representation of source sentence
throughout the whole translation process. Via nontrivial transformations and
iterative interactions between the two memories, the decoder focuses on more
appropriate source word(s) for predicting the next target word at each decoding
step, therefore can improve the adequacy of translations. Experimental results
on Chinese=>English and WMT17 German<=>English translation tasks demonstrate
the superiority of the proposed model.
XGBoost: Scalable GPU Accelerated Learning
Authors: Rory Mitchell, Andrey Adinets, Thejaswi Rao, Eibe Frank
Date: 2018-06-29 00:00:00
Tasks: Cloud Computing, Data Compression, GPU

We describe the multi-GPU gradient boosting algorithm implemented in the
XGBoost library (https://github.com/dmlc/xgboost). Our algorithm allows fast,
scalable training on multi-GPU systems with all of the features of the XGBoost
library. We employ data compression techniques to minimise the usage of scarce
GPU memory while still allowing highly efficient implementation. Using our
algorithm we show that it is possible to process 115 million training instances
in under three minutes on a publicly available cloud computing instance. The
algorithm is implemented using end-to-end GPU parallelism, with prediction,
gradient calculation, feature quantisation, decision tree construction and
evaluation phases all computed on device.