pwc-archive/papers-with-abstracts huggingface dataset:
Deep Active Learning with a Neural Architecture Search
Authors: Yonatan Geifman, Ran El-Yaniv
Date: 2018-11-19 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Active Learning, Neural Architecture Search

We consider active learning of deep neural networks. Most active learning works in this context have focused on studying effective querying mechanisms and assumed that an appropriate network architecture is a priori known for the problem at hand. We challenge this assumption and propose a novel active strategy whereby the learning algorithm searches for effective architectures on the fly, while actively learning. We apply our strategy using three known querying techniques (softmax response, MC-dropout, and coresets) and show that the proposed approach overwhelmingly outperforms active learning using fixed architectures.
Fine-grained Classification using Heterogeneous Web Data and Auxiliary Categories
Authors: Li Niu, Ashok Veeraraghavan, Ashu Sabharwal
Date: 2018-11-19 00:00:00
Tasks: Classification, General Classification, TAG, Zero-Shot Learning

Fine-grained classification remains a very challenging problem, because of
the absence of well-labeled training data caused by the high cost of annotating
a large number of fine-grained categories. In the extreme case, given a set of
test categories without any well-labeled training data, the majority of
existing works can be grouped into the following two research directions: 1)
crawl noisy labeled web data for the test categories as training data, which is
dubbed as webly supervised learning; 2) transfer the knowledge from auxiliary
categories with well-labeled training data to the test categories, which
corresponds to zero-shot learning setting. Nevertheless, the above two research
directions still have critical issues to be addressed. For the first direction,
web data have noisy labels and considerably different data distribution from
test data. For the second direction, zero-shot learning is struggling to
achieve compelling results compared with conventional supervised learning. The
issues of the above two directions motivate us to develop a novel approach
which can jointly exploit both noisy web training data from test categories and
well-labeled training data from auxiliary categories. In particular, on one
hand, we crawl web data for test categories as noisy training data. On the
other hand, we transfer the knowledge from auxiliary categories with
well-labeled training data to test categories by virtue of free semantic
information (e.g., word vector) of all categories. Moreover, given the fact
that web data are generally associated with additional textual information
(e.g., title and tag), we extend our method by using the surrounding textual
information of web data as privileged information. Extensive experiments show
the effectiveness of our proposed methods.
Neural Joint Source-Channel Coding
Authors: Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon
Date: 2018-11-19 00:00:00
Tasks: Decoder

For reliable transmission across a noisy communication channel, classical results from information theory show that it is asymptotically optimal to separate out the source and channel coding processes. However, this decomposition can fall short in the finite bit-length regime, as it requires non-trivial tuning of hand-crafted codes and assumes infinite computational power for decoding. In this work, we propose to jointly learn the encoding and decoding processes using a new discrete variational autoencoder model. By adding noise into the latent codes to simulate the channel during training, we learn to both compress and error-correct given a fixed bit-length and computational budget. We obtain codes that are not only competitive against several separation schemes, but also learn useful robust representations of the data for downstream tasks such as classification. Finally, inference amortization yields an extremely fast neural decoder, almost an order of magnitude faster compared to standard decoding methods based on iterative belief propagation.
Three Dimensional Convolutional Neural Network Pruning with Regularization-Based Method
Authors: Yuxin Zhang, Huan Wang, Yang Luo, Lu Yu, Haoji Hu, Hangguan Shan, Tony Q. S. Quek
Date: 2018-11-19 00:00:00
Proceeding: NIPS Workshop CDNNRIA 2018
Tasks: Model Compression, Network Pruning

Despite enjoying extensive applications in video analysis, three-dimensional convolutional neural networks (3D CNNs)are restricted by their massive computation and storage consumption. To solve this problem, we propose a threedimensional regularization-based neural network pruning method to assign different regularization parameters to different weight groups based on their importance to the network. Further we analyze the redundancy and computation cost for each layer to determine the different pruning ratios. Experiments show that pruning based on our method can lead to 2x theoretical speedup with only 0.41% accuracy loss for 3DResNet18 and 3.28% accuracy loss for C3D. The proposed method performs favorably against other popular methods for model compression and acceleration.
Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning
Authors: Yuexin Wu, Xiujun Li, Jingjing Liu, Jianfeng Gao, Yiming Yang
Date: 2018-11-19 00:00:00
Tasks: Active Learning, Q-Learning, Reinforcement Learning, Task-Completion Dialogue Policy Learning

Training task-completion dialogue agents with reinforcement learning usually
requires a large number of real user experiences. The Dyna-Q algorithm extends
Q-learning by integrating a world model, and thus can effectively boost
training efficiency using simulated experiences generated by the world model.
The effectiveness of Dyna-Q, however, depends on the quality of the world model
- or implicitly, the pre-specified ratio of real vs. simulated experiences used
for Q-learning. To this end, we extend the recently proposed Deep Dyna-Q (DDQ)
framework by integrating a switcher that automatically determines whether to
use a real or simulated experience for Q-learning. Furthermore, we explore the
use of active learning for improving sample efficiency, by encouraging the
world model to generate simulated experiences in the state-action space where
the agent has not (fully) explored. Our results show that by combining switcher
and active learning, the new framework named as Switch-based Active Deep Dyna-Q
(Switch-DDQ), leads to significant improvement over DDQ and Q-learning
baselines in both simulation and human evaluations.
iQIYI-VID: A Large Dataset for Multi-modal Person Identification
Authors: Yuanliu Liu, Bo Peng, Peipei Shi, He Yan, Yong Zhou, Bing Han, Yi Zheng, Chao Lin, Jianbin Jiang, Yin Fan, Tingwei Gao, Ganwen Wang, Jian Liu, Xiangju Lu, Danming Xie
Date: 2018-11-19 00:00:00
Tasks: Face Recognition, Multi-Modal Person Identification, Person Identification, Person Re-Identification, Speaker Recognition

Person identification in the wild is very challenging due to great variation
in poses, face quality, clothes, makeup and so on. Traditional research, such
as face recognition, person re-identification, and speaker recognition, often
focuses on a single modal of information, which is inadequate to handle all the
situations in practice. Multi-modal person identification is a more promising
way that we can jointly utilize face, head, body, audio features, and so on. In
this paper, we introduce iQIYI-VID, the largest video dataset for multi-modal
person identification. It is composed of 600K video clips of 5,000 celebrities.
These video clips are extracted from 400K hours of online videos of various
types, ranging from movies, variety shows, TV series, to news broadcasting. All
video clips pass through a careful human annotation process, and the error rate
of labels is lower than 0.2\%. We evaluated the state-of-art models of face
recognition, person re-identification, and speaker recognition on the iQIYI-VID
dataset. Experimental results show that these models are still far from being
perfect for the task of person identification in the wild. We proposed a
Multi-modal Attention module to fuse multi-modal features that can improve
person identification considerably. We have released the dataset online to
promote multi-modal person identification research.
Representation based and Attention augmented Meta learning
Authors: Yunxiao Qin, Chenxu Zhao, Zezheng Wang, Junliang Xing, Jun Wan, Zhen Lei
Date: 2018-11-19 00:00:00
Tasks: Few-Shot Learning, Meta-Learning

Deep learning based computer vision fails to work when labeled images are
scarce. Recently, Meta learning algorithm has been confirmed as a promising way
to improve the ability of learning from few images for computer vision.
However, previous Meta learning approaches expose problems:
  1) they ignored the importance of attention mechanism for the Meta learner;
  2) they didn't give the Meta learner the ability of well using the past
knowledge which can help to express images into high representations, resulting
in that the Meta learner has to solve few shot learning task directly from the
original high dimensional RGB images.
  In this paper, we argue that the attention mechanism and the past knowledge
are crucial for the Meta learner, and the Meta learner should be trained on
high representations of the RGB images instead of directly on the original
ones. Based on these arguments, we propose two methods: Attention augmented
Meta Learning (AML) and Representation based and Attention augmented Meta
Learning(RAML). The method AML aims to improve the Meta learner's attention
ability by explicitly embedding an attention model into its network. The method
RAML aims to give the Meta learner the ability of leveraging the past learned
knowledge to reduce the dimension of the original input data by expressing it
into high representations, and help the Meta learner to perform well. Extensive
experiments demonstrate the effectiveness of the proposed models, with
state-of-the-art few shot learning performances on several few shot learning
benchmarks. The source code of our proposed methods will be released soon to
facilitate further studies on those aforementioned problem.
CA3Net: Contextual-Attentional Attribute-Appearance Network for Person Re-Identification
Authors: Jiawei Liu, Zheng-Jun Zha, Hongtao Xie, Zhiwei Xiong, Yongdong Zhang
Date: 2018-11-19 00:00:00
Tasks: Attribute, Multi-Task Learning, Person Re-Identification

Person re-identification aims to identify the same pedestrian across
non-overlapping camera views. Deep learning techniques have been applied for
person re-identification recently, towards learning representation of
pedestrian appearance. This paper presents a novel Contextual-Attentional
Attribute-Appearance Network (CA3Net) for person re-identification. The CA3Net
simultaneously exploits the complementarity between semantic attributes and
visual appearance, the semantic context among attributes, visual attention on
attributes as well as spatial dependencies among body parts, leading to
discriminative and robust pedestrian representation. Specifically, an attribute
network within CA3Net is designed with an Attention-LSTM module. It
concentrates the network on latent image regions related to each attribute as
well as exploits the semantic context among attributes by a LSTM module. An
appearance network is developed to learn appearance features from the full
body, horizontal and vertical body parts of pedestrians with spatial
dependencies among body parts. The CA3Net jointly learns the attribute and
appearance features in a multi-task learning manner, generating comprehensive
representation of pedestrians. Extensive experiments on two challenging
benchmarks, i.e., Market-1501 and DukeMTMC-reID datasets, have demonstrated the
effectiveness of the proposed approach.
A Pretrained DenseNet Encoder for Brain Tumor Segmentation
Authors: Jean Stawiaski
Date: 2018-11-19 00:00:00
Tasks: Brain Tumor Segmentation, Decoder, Transfer Learning, Tumor Segmentation

This article presents a convolutional neural network for the automatic
segmentation of brain tumors in multimodal 3D MR images based on a U-net
architecture.We evaluate the use of a densely connected convolutional network
encoder (DenseNet) which was pretrained on the ImageNet data set. We detail two
network architectures that can take into account multiple 3D images as inputs.
This work aims to identify if a generic pretrained network can be used for very
specific medical applications where the target data differ both in the number
of spatial dimensions as well as in the number of inputs channels. Moreover in
order to regularize this transfer learning task we only train the decoder part
of the U-net architecture. We evaluate the effectiveness of the proposed
approach on the BRATS 2018 segmentation challenge where we obtained dice scores
of 0.79, 0.90, 0.85 and 95/% Hausdorff distance of 2.9mm, 3.95mm, and 6.48mm
for enhanced tumor core, whole tumor and tumor core respectively on the
validation set. This scores degrades to 0.77, 0.88, 0.78 and 95 /% Hausdorff
distance of 3.6mm, 5.72mm, and 5.83mm on the testing set.
Contributors profile modelization in crowdsourcing platforms
Authors: Constance Thierry, Jean-Christophe Dubois, Yolande Le Gall, Arnaud Martin
Date: 2018-11-19 00:00:00

The crowdsourcing consists in the externalisation of tasks to a crowd of
people remunerated to execute this ones. The crowd, usually diversified, can
include users without qualification and/or motivation for the tasks. In this
paper we will introduce a new method of user expertise modelization in the
crowdsourcing platforms based on the theory of belief functions in order to
identify serious and qualificated users.
Variational Bayesian Dropout with a Hierarchical Prior
Authors: Yuhang Liu, Wenyong Dong, Lei Zhang, Dong Gong, Qinfeng Shi
Date: 2018-11-19 00:00:00
Proceeding: CVPR 2019 6

Variational dropout (VD) is a generalization of Gaussian dropout, which aims
at inferring the posterior of network weights based on a log-uniform prior on
them to learn these weights as well as dropout rate simultaneously. The
log-uniform prior not only interprets the regularization capacity of Gaussian
dropout in network training, but also underpins the inference of such
posterior. However, the log-uniform prior is an improper prior (i.e., its
integral is infinite) which causes the inference of posterior to be ill-posed,
thus restricting the regularization performance of VD. To address this problem,
we present a new generalization of Gaussian dropout, termed variational
Bayesian dropout (VBD), which turns to exploit a hierarchical prior on the
network weights and infer a new joint posterior. Specifically, we implement the
hierarchical prior as a zero-mean Gaussian distribution with variance sampled
from a uniform hyper-prior. Then, we incorporate such a prior into inferring
the joint posterior over network weights and the variance in the hierarchical
prior, with which both the network training and the dropout rate estimation can
be cast into a joint optimization problem. More importantly, the hierarchical
prior is a proper prior which enables the inference of posterior to be
well-posed. In addition, we further show that the proposed VBD can be
seamlessly applied to network compression. Experiments on both classification
and network compression tasks demonstrate the superior performance of the
proposed VBD in terms of regularizing network training.
Feature selection as Monte-Carlo Search in Growing Single Rooted Directed Acyclic Graph by Best Leaf Identification
Authors: Aurelien Pelissier, Atsuyoshi Nakamura, Koji Tabata
Date: 2018-11-19 00:00:00
Tasks: feature selection

Monte Carlo tree search (MCTS) has received considerable interest due to its
spectacular success in the difficult problem of computer Go and also proved
beneficial in a range of other domains. A major issue that has received little
attention in the MCTS literature is the fact that, in most games, different
actions can lead to the same state, that may lead to a high degree of
redundancy in tree representation and unnecessary additional computational
cost. We extend MCTS to single rooted directed acyclic graph (SR-DAG), and
consider the Best Arm Identification (BAI) and the Best Leaf Identification
(BLI) problem of an expanding SR-DAG of arbitrary depth. We propose algorithms
that are (epsilon, delta)-correct in the fixed confidence setting, and prove an
asymptotic upper bounds of sample complexity for our BAI algorithm. As a major
application for our BLI algorithm, a novel approach for Feature Selection is
proposed by representing the feature set space as a SR-DAG and repeatedly
evaluating feature subsets until a candidate for the best leaf is returned, a
proof of concept is shown on benchmark data sets.
Practical Deep Reinforcement Learning Approach for Stock Trading
Authors: Xiao-Yang Liu, Zhuoran Xiong, Shan Zhong, Hongyang Yang, Anwar Walid
Date: 2018-11-19 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent's performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns.
Higher-order Network for Action Recognition
Authors: Kai Hu, Bhiksha Raj
Date: 2018-11-19 00:00:00
Tasks: Action Recognition, General Classification, Video Classification, Video Recognition

Capturing spatiotemporal dynamics is an essential topic in video recognition. In this paper, we present learnable higher-order operations as a generic family of building blocks for capturing spatiotemporal dynamics from RGB input video space. Similar to higher-order functions, the weights of higher-order operations are themselves derived from the data with learnable parameters. Classical architectures such as residual learning and network-in-network are first-order operations where weights are directly learned from the data. Higher-order operations make it easier to capture context-sensitive patterns, such as motion. Self-attention models are also higher-order operations, but the attention weights are mostly computed from an affine operation or dot product. Learnable higher-order operations can be more generic and flexible. Experimentally, we show that on the task of video recognition, our higher-order models can achieve results on par with or better than the existing state-of-the-art methods on Something-Something (V1 and V2), Kinetics and Charades datasets.
Unsupervised Learning in Reservoir Computing for EEG-based Emotion Recognition
Authors: Rahma Fourati, Boudour Ammar, Javier Sanchez-Medina, Adel M. ALIMI
Date: 2018-11-19 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), Emotion Recognition, General Classification, Time Series, Time Series Analysis, Time Series Prediction

In real-world applications such as emotion recognition from recorded brain
activity, data are captured from electrodes over time. These signals constitute
a multidimensional time series. In this paper, Echo State Network (ESN), a
recurrent neural network with a great success in time series prediction and
classification, is optimized with different neural plasticity rules for
classification of emotions based on electroencephalogram (EEG) time series.
Actually, the neural plasticity rules are a kind of unsupervised learning
adapted for the reservoir, i.e. the hidden layer of ESN. More specifically, an
investigation of Oja's rule, BCM rule and gaussian intrinsic plasticity rule
was carried out in the context of EEG-based emotion recognition. The study,
also, includes a comparison of the offline and online training of the ESN. When
testing on the well-known affective benchmark "DEAP dataset" which contains EEG
signals from 32 subjects, we find that pretraining ESN with gaussian intrinsic
plasticity enhanced the classification accuracy and outperformed the results
achieved with an ESN pretrained with synaptic plasticity. Four classification
problems were conducted in which the system complexity is increased and the
discrimination is more challenging, i.e. inter-subject emotion discrimination.
Our proposed method achieves higher performance over the state of the art
methods.
NSEEN: Neural Semantic Embedding for Entity Normalization
Authors: Shobeir Fakhraei, Joel Mathew, Jose Luis Ambite
Date: 2018-11-19 00:00:00
Tasks: Entity Resolution, Knowledge Graphs

Much of human knowledge is encoded in text, available in scientific publications, books, and the web. Given the rapid growth of these resources, we need automated methods to extract such knowledge into machine-processable structures, such as knowledge graphs. An important task in this process is entity normalization, which consists of mapping noisy entity mentions in text to canonical entities in well-known reference sets. However, entity normalization is a challenging problem; there often are many textual forms for a canonical entity that may not be captured in the reference set, and entities mentioned in text may include many syntactic variations, or errors. The problem is particularly acute in scientific domains, such as biology. To address this problem, we have developed a general, scalable solution based on a deep Siamese neural network model to embed the semantic information about the entities, as well as their syntactic variations. We use these embeddings for fast mapping of new entities to large reference sets, and empirically show the effectiveness of our framework in challenging bio-entity normalization datasets.
Modularity in biological evolution and evolutionary computation
Authors: Anton Eremeev, Alexander Spirov
Date: 2018-11-19 00:00:00
Tasks: Transfer Learning

One of the main properties of biological systems is modularity, which
manifests itself at all levels of their organization, starting with the level
of molecular genetics, ending with the level of whole organisms and their
communities. In a simplified form, these basic principles were transferred from
the genetics of populations to the field of evolutionary computations, in order
to solve applied optimization problems. Over almost half a century of
development in this field of computer science, considerable practical
experience has been gained and interesting theoretical results have been
obtained. In this survey, the phenomena and patterns associated with modularity
in genetics and evolutionary computations are compared. An analysis of
similarities and differences in the results obtained in these areas is carried
out from the modularity view point. The possibilities for knowledge transfer
between the areas are discussed.