pwc-archive/papers-with-abstracts huggingface dataset:
Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning
Authors: Jingwen Wang, Wenhao Jiang, Lin Ma, Wei Liu, Yong Xu
Date: 2018-03-31 00:00:00
Proceeding: CVPR 2018 6
Tasks: Decoder, Dense Video Captioning, Video Captioning

Dense video captioning is a newly emerging task that aims at both localizing
and describing all events in a video. We identify and tackle two challenges on
this task, namely, (1) how to utilize both past and future contexts for
accurate event proposal predictions, and (2) how to construct informative input
to the decoder for generating natural event descriptions. First, previous works
predominantly generate temporal event proposals in the forward direction, which
neglects future video context. We propose a bidirectional proposal method that
effectively exploits both past and future contexts to make proposal
predictions. Second, different events ending at (nearly) the same time are
indistinguishable in the previous works, resulting in the same captions. We
solve this problem by representing each event with an attentive fusion of
hidden states from the proposal module and video contents (e.g., C3D features).
We further propose a novel context gating mechanism to balance the
contributions from the current event and its surrounding contexts dynamically.
We empirically show that our attentively fused event representation is superior
to the proposal hidden states or video contents alone. By coupling proposal and
captioning modules into one unified framework, our model outperforms the
state-of-the-arts on the ActivityNet Captions dataset with a relative gain of
over 100% (Meteor score increases from 4.82 to 9.65).
Detection and Resolution of Rumours in Social Media: A Survey
Authors: Arkaitz Zubiaga, Ahmet Aker, Kalina Bontcheva, Maria Liakata, Rob Procter
Date: 2017-04-03 00:00:00
Tasks: Classification, General Classification, Rumour Detection, Stance Classification, Survey, Veracity Classification

Despite the increasing use of social media platforms for information and news
gathering, its unmoderated nature often leads to the emergence and spread of
rumours, i.e. pieces of information that are unverified at the time of posting.
At the same time, the openness of social media platforms provides opportunities
to study how users share and discuss rumours, and to explore how natural
language processing and data mining techniques may be used to find ways of
determining their veracity. In this survey we introduce and discuss two types
of rumours that circulate on social media; long-standing rumours that circulate
for long periods of time, and newly-emerging rumours spawned during fast-paced
events such as breaking news, where reports are released piecemeal and often
with an unverified status in their early stages. We provide an overview of
research into social media rumours with the ultimate goal of developing a
rumour classification system that consists of four components: rumour
detection, rumour tracking, rumour stance classification and rumour veracity
classification. We delve into the approaches presented in the scientific
literature for the development of each of these four components. We summarise
the efforts and achievements so far towards the development of rumour
classification systems and conclude with suggestions for avenues for future
research in social media mining for detection and resolution of rumours.
Adaptive distributed methods under communication constraints
Authors: Botond Szabo, Harry van Zanten
Date: 2018-04-03 00:00:00
Tasks: regression

We study distributed estimation methods under communication constraints in a
distributed version of the nonparametric random design regression model. We
derive minimax lower bounds and exhibit methods that attain those bounds.
Moreover, we show that adaptive estimation is possible in this setting.
Deep Appearance Maps
Authors: Maxim Maximov, Laura Leal-Taixé, Mario Fritz, Tobias Ritschel
Date: 2018-04-03 00:00:00
Proceeding: ICCV 2019 10

We propose a deep representation of appearance, i. e., the relation of color, surface orientation, viewer position, material and illumination. Previous approaches have useddeep learning to extract classic appearance representationsrelating to reflectance model parameters (e. g., Phong) orillumination (e. g., HDR environment maps). We suggest todirectly represent appearance itself as a network we call aDeep Appearance Map (DAM). This is a 4D generalizationover 2D reflectance maps, which held the view direction fixed. First, we show how a DAM can be learned from images or video frames and later be used to synthesize appearance, given new surface orientations and viewer positions. Second, we demonstrate how another network can be used to map from an image or video frames to a DAM network to reproduce this appearance, without using a lengthy optimization such as stochastic gradient descent (learning-to-learn). Finally, we show the example of an appearance estimation-and-segmentation task, mapping from an image showingmultiple materials to multiple deep appearance maps.
Convolutional Neural Networks with Alternately Updated Clique
Authors: Yibo Yang, Zhisheng Zhong, Tiancheng Shen, Zhouchen Lin
Date: 2018-02-28 00:00:00
Proceeding: CVPR 2018 6

Improving information flow in deep networks helps to ease the training
difficulties and utilize parameters more efficiently. Here we propose a new
convolutional neural network architecture with alternately updated clique
(CliqueNet). In contrast to prior networks, there are both forward and backward
connections between any two layers in the same block. The layers are
constructed as a loop and are updated alternately. The CliqueNet has some
unique properties. For each layer, it is both the input and output of any other
layer in the same block, so that the information flow among layers is
maximized. During propagation, the newly updated layers are concatenated to
re-update previously updated layer, and parameters are reused for multiple
times. This recurrent feedback structure is able to bring higher level visual
information back to refine low-level filters and achieve spatial attention. We
analyze the features generated at different stages and observe that using
refined features leads to a better result. We adopt a multi-scale feature
strategy that effectively avoids the progressive growth of parameters.
Experiments on image recognition datasets including CIFAR-10, CIFAR-100, SVHN
and ImageNet show that our proposed models achieve the state-of-the-art
performance with fewer parameters.
Unsupervised Semantic-based Aggregation of Deep Convolutional Features
Authors: Jian Xu, Chunheng Wang, Chengzuo Qi, Cunzhao Shi, Baihua Xiao
Date: 2018-04-03 00:00:00
Tasks: General Classification, Image Retrieval, Retrieval

In this paper, we propose a simple but effective semantic-based aggregation
(SBA) method. The proposed SBA utilizes the discriminative filters of deep
convolutional layers as semantic detectors. Moreover, we propose the effective
unsupervised strategy to select some semantic detectors to generate the
"probabilistic proposals", which highlight certain discriminative pattern of
objects and suppress the noise of background. The final global SBA
representation could then be acquired by aggregating the regional
representations weighted by the selected "probabilistic proposals"
corresponding to various semantic content. Our unsupervised SBA is easy to
generalize and achieves excellent performance on various tasks. We conduct
comprehensive experiments and show that our unsupervised SBA outperforms the
state-of-the-art unsupervised and supervised aggregation methods on image
retrieval, place recognition and cloud classification.
Neural Random Forests
Authors: Gérard Biau, Erwan Scornet, Johannes Welbl
Date: 2016-04-25 00:00:00
Tasks: regression

Given an ensemble of randomized regression trees, it is possible to
restructure them as a collection of multilayered neural networks with
particular connection weights. Following this principle, we reformulate the
random forest method of Breiman (2001) into a neural network setting, and in
turn propose two new hybrid procedures that we call neural random forests. Both
predictors exploit prior knowledge of regression trees for their architecture,
have less parameters to tune than standard networks, and less restrictions on
the geometry of the decision boundaries than trees. Consistency results are
proved, and substantial numerical evidence is provided on both synthetic and
real data sets to assess the excellent performance of our methods in a large
variety of prediction problems.
Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling
Authors: Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
Date: 2018-04-03 00:00:00
Proceeding: ICLR 2018 1

Recurrent neural networks (RNN), convolutional neural networks (CNN) and
self-attention networks (SAN) are commonly used to produce context-aware
representations. RNN can capture long-range dependency but is hard to
parallelize and not time-efficient. CNN focuses on local dependency but does
not perform well on some tasks. SAN can model both such dependencies via highly
parallelizable computation, but memory requirement grows rapidly in line with
sequence length. In this paper, we propose a model, called "bi-directional
block self-attention network (Bi-BloSAN)", for RNN/CNN-free sequence encoding.
It requires as little memory as RNN but with all the merits of SAN. Bi-BloSAN
splits the entire sequence into blocks, and applies an intra-block SAN to each
block for modeling local context, then applies an inter-block SAN to the
outputs for all blocks to capture long-range dependency. Thus, each SAN only
needs to process a short sequence, and only a small amount of memory is
required. Additionally, we use feature-level attention to handle the variation
of contexts around the same word, and use forward/backward masks to encode
temporal order information. On nine benchmark datasets for different NLP tasks,
Bi-BloSAN achieves or improves upon state-of-the-art accuracy, and shows better
efficiency-memory trade-off than existing RNN/CNN/SAN.
Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval
Authors: Jian Xu, Chunheng Wang, Chengzuo Qi, Cunzhao Shi, Baihua Xiao
Date: 2017-07-14 00:00:00
Tasks: Dimensionality Reduction, Image Retrieval, Retrieval

Existing manifold learning methods are not appropriate for image retrieval
task, because most of them are unable to process query image and they have much
additional computational cost especially for large scale database. Therefore,
we propose the iterative manifold embedding (IME) layer, of which the weights
are learned off-line by unsupervised strategy, to explore the intrinsic
manifolds by incomplete data. On the large scale database that contains 27000
images, IME layer is more than 120 times faster than other manifold learning
methods to embed the original representations at query time. We embed the
original descriptors of database images which lie on manifold in a high
dimensional space into manifold-based representations iteratively to generate
the IME representations in off-line learning stage. According to the original
descriptors and the IME representations of database images, we estimate the
weights of IME layer by ridge regression. In on-line retrieval stage, we employ
the IME layer to map the original representation of query image with ignorable
time cost (2 milliseconds). We experiment on five public standard datasets for
image retrieval. The proposed IME layer significantly outperforms related
dimension reduction methods and manifold learning methods. Without
post-processing, Our IME layer achieves a boost in performance of
state-of-the-art image retrieval methods with post-processing on most datasets,
and needs less computational cost.
Fine-Grained Attention Mechanism for Neural Machine Translation
Authors: Heeyoul Choi, Kyunghyun Cho, Yoshua Bengio
Date: 2018-03-30 00:00:00
Tasks: Machine Translation, NMT, Translation

Neural machine translation (NMT) has been a new paradigm in machine
translation, and the attention mechanism has become the dominant approach with
the state-of-the-art records in many language pairs. While there are variants
of the attention mechanism, all of them use only temporal attention where one
scalar value is assigned to one context vector corresponding to a source word.
In this paper, we propose a fine-grained (or 2D) attention mechanism where each
dimension of a context vector will receive a separate attention score. In
experiments with the task of En-De and En-Fi translation, the fine-grained
attention method improves the translation quality in terms of BLEU score. In
addition, our alignment analysis reveals how the fine-grained attention
mechanism exploits the internal structure of context vectors.
Differential Attention for Visual Question Answering
Authors: Badri Patro, Vinay P. Namboodiri
Date: 2018-04-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

In this paper we aim to answer questions based on images when provided with a
dataset of question-answer pairs for a number of images during training. A
number of methods have focused on solving this problem by using image based
attention. This is done by focusing on a specific part of the image while
answering the question. Humans also do so when solving this problem. However,
the regions that the previous systems focus on are not correlated with the
regions that humans focus on. The accuracy is limited due to this drawback. In
this paper, we propose to solve this problem by using an exemplar based method.
We obtain one or more supporting and opposing exemplars to obtain a
differential attention region. This differential attention is closer to human
attention than other image based attention methods. It also helps in obtaining
improved accuracy when answering questions. The method is evaluated on
challenging benchmark datasets. We perform better than other image based
attention methods and are competitive with other state of the art methods that
focus on both image and questions.
Learning on Hypergraphs with Sparsity
Authors: Canh Hao Nguyen, Hiroshi Mamitsuka
Date: 2018-04-03 00:00:00
Tasks: Sparse Learning

Hypergraph is a general way of representing high-order relations on a set of
objects. It is a generalization of graph, in which only pairwise relations can
be represented. It finds applications in various domains where relationships of
more than two objects are observed. On a hypergraph, as a generalization of
graph, one wishes to learn a smooth function with respect to its topology. A
fundamental issue is to find suitable smoothness measures of functions on the
nodes of a graph/hypergraph. We show a general framework that generalizes
previously proposed smoothness measures and also gives rise to new ones. To
address the problem of irrelevant or noisy data, we wish to incorporate sparse
learning framework into learning on hypergraphs. We propose sparsely smooth
formulations that learn smooth functions and induce sparsity on hypergraphs at
both hyperedge and node levels. We show their properties and sparse support
recovery results. We conduct experiments to show that our sparsely smooth
models have benefits to irrelevant and noisy data, and usually give similar or
improved performances compared to dense models.
Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yorùbá Language Text
Authors: Iroro Orife
Date: 2018-04-03 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition, text-to-speech, Text to Speech, Translation

Yor\`ub\'a is a widely spoken West African language with a writing system
rich in tonal and orthographic diacritics. With very few exceptions, diacritics
are omitted from electronic texts, due to limited device and application
support. Diacritics provide morphological information, are crucial for lexical
disambiguation, pronunciation and are vital for any Yor\`ub\'a text-to-speech
(TTS), automatic speech recognition (ASR) and natural language processing (NLP)
tasks. Reframing Automatic Diacritic Restoration (ADR) as a machine translation
task, we experiment with two different attentive Sequence-to-Sequence neural
models to process undiacritized text. On our evaluation dataset, this approach
produces diacritization error rates of less than 5%. We have released
pre-trained models, datasets and source-code as an open-source project to
advance efforts on Yor\`ub\'a language technology.
Incorporating Word Embeddings into Open Directory Project based Large-scale Classification
Authors: Kang-Min Kim, Aliyeva Dinara, Byung-Ju Choi, SangKeun Lee
Date: 2018-04-03 00:00:00
Tasks: Classification, General Classification, Semantic Similarity, Semantic Textual Similarity, text-classification, Text Classification, Word Embeddings

Recently, implicit representation models, such as embedding or deep learning,
have been successfully adopted to text classification task due to their
outstanding performance. However, these approaches are limited to small- or
moderate-scale text classification. Explicit representation models are often
used in a large-scale text classification, like the Open Directory Project
(ODP)-based text classification. However, the performance of these models is
limited to the associated knowledge bases. In this paper, we incorporate word
embeddings into the ODP-based large-scale classification. To this end, we first
generate category vectors, which represent the semantics of ODP categories by
jointly modeling word embeddings and the ODP-based text classification. We then
propose a novel semantic similarity measure, which utilizes the category and
word vectors obtained from the joint model and word embeddings, respectively.
The evaluation results clearly show the efficacy of our methodology in
large-scale text classification. The proposed scheme exhibits significant
improvements of 10% and 28% in terms of macro-averaging F1-score and precision
at k, respectively, over state-of-the-art techniques.
Domain Randomization and Generative Models for Robotic Grasping
Authors: Joshua Tobin, Lukas Biewald, Rocky Duan, Marcin Andrychowicz, Ankur Handa, Vikash Kumar, Bob McGrew, Jonas Schneider, Peter Welinder, Wojciech Zaremba, Pieter Abbeel
Date: 2017-10-17 00:00:00
Tasks: Object, Robotic Grasping

Deep learning-based robotic grasping has made significant progress thanks to
algorithmic improvements and increased data availability. However,
state-of-the-art models are often trained on as few as hundreds or thousands of
unique object instances, and as a result generalization can be a challenge.
  In this work, we explore a novel data generation pipeline for training a deep
neural network to perform grasp planning that applies the idea of domain
randomization to object synthesis. We generate millions of unique, unrealistic
procedurally generated objects, and train a deep neural network to perform
grasp planning on these objects.
  Since the distribution of successful grasps for a given object can be highly
multimodal, we propose an autoregressive grasp planning model that maps sensor
inputs of a scene to a probability distribution over possible grasps. This
model allows us to sample grasps efficiently at test time (or avoid sampling
entirely).
  We evaluate our model architecture and data generation pipeline in simulation
and the real world. We find we can achieve a $>$90% success rate on previously
unseen realistic objects at test time in simulation despite having only been
trained on random objects. We also demonstrate an 80% success rate on
real-world grasp attempts despite having only been trained on random simulated
objects.
Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory
Authors: Yuxiu Hua, Zhifeng Zhao, Rongpeng Li, Xianfu Chen, Zhiming Liu, Honggang Zhang
Date: 2017-11-08 00:00:00
Tasks: Traffic Prediction

Traffic prediction plays an important role in evaluating the performance of
telecommunication networks and attracts intense research interests. A
significant number of algorithms and models have been put forward to analyse
traffic data and make prediction. In the recent big data era, deep learning has
been exploited to mine the profound information hidden in the data. In
particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network
(RNN) schemes, has attracted a lot of attentions due to its capability of
processing the long-range dependency embedded in the sequential traffic data.
However, LSTM has considerable computational cost, which can not be tolerated
in tasks with stringent latency requirement. In this paper, we propose a deep
learning model based on LSTM, called Random Connectivity LSTM (RCLSTM).
Compared to the conventional LSTM, RCLSTM makes a notable breakthrough in the
formation of neural network, which is that the neurons are connected in a
stochastic manner rather than full connected. So, the RCLSTM, with certain
intrinsic sparsity, have many neural connections absent (distinguished from the
full connectivity) and which leads to the reduction of the parameters to be
trained and the computational cost. We apply the RCLSTM to predict traffic and
validate that the RCLSTM with even 35% neural connectivity still shows a
satisfactory performance. When we gradually add training samples, the
performance of RCLSTM becomes increasingly closer to the baseline LSTM.
Moreover, for the input traffic sequences of enough length, the RCLSTM exhibits
even superior prediction accuracy than the baseline LSTM.
Equitability of Dependence Measure
Authors: Hangjin Jiang, Kan Liu, Yiming Ding
Date: 2015-01-09 00:00:00
Tasks: Variable Selection

Measuring dependence between two random variables is very important, and
critical in many applied areas such as variable selection, brain network
analysis. However, we do not know what kind of functional relationship is
between two covariates, which requires the dependence measure to be equitable.
That is, it gives similar scores to equally noisy relationship of different
types. In fact, the dependence score is a continuous random variable taking
values in $[0,1]$, thus it is theoretically impossible to give similar scores.
In this paper, we introduce a new definition of equitability of a dependence
measure, i.e, power-equitable (weak-equitable) and show by simulation that HHG
and Copula Dependence Coefficient (CDC) are weak-equitable.
A Survey on Multi-View Clustering
Authors: Guoqing Chao, Shiliang Sun, Jinbo Bi
Date: 2017-12-18 00:00:00
Tasks: Clustering, MULTI-VIEW LEARNING, Survey

With advances in information acquisition technologies, multi-view data become
ubiquitous. Multi-view learning has thus become more and more popular in
machine learning and data mining fields. Multi-view unsupervised or
semi-supervised learning, such as co-training, co-regularization has gained
considerable attention. Although recently, multi-view clustering (MVC) methods
have been developed rapidly, there has not been a survey to summarize and
analyze the current progress. Therefore, this paper reviews the common
strategies for combining multiple views of data and based on this summary we
propose a novel taxonomy of the MVC approaches. We further discuss the
relationships between MVC and multi-view representation, ensemble clustering,
multi-task clustering, multi-view supervised and semi-supervised learning.
Several representative real-world applications are elaborated. To promote
future development of MVC, we envision several open problems that may require
further investigation and thorough examination.