pwc-archive/papers-with-abstracts huggingface dataset:
Synergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular Prediction
Authors: Talia B. Kimber, Sebastian Engelke, Igor V. Tetko, Eric Bruno, Guillaume Godin
Date: 2018-12-11 00:00:00
Tasks: Ensemble Learning, Small Data Image Classification

In our study, we demonstrate the synergy effect between convolutional neural
networks and the multiplicity of SMILES. The model we propose, the so-called
Convolutional Neural Fingerprint (CNF) model, reaches the accuracy of
traditional descriptors such as Dragon (Mauri et al. [22]), RDKit (Landrum
[18]), CDK2 (Willighagen et al. [43]) and PyDescriptor (Masand and Rastija
[20]). Moreover the CNF model generally performs better than highly fine-tuned
traditional descriptors, especially on small data sets, which is of great
interest for the chemical field where data sets are generally small due to
experimental costs, the availability of molecules or accessibility to private
databases. We evaluate the CNF model along with SMILES augmentation during both
training and testing. To the best of our knowledge, this is the first time that
such a methodology is presented. We show that using the multiplicity of SMILES
during training acts as a regulariser and therefore avoids overfitting and can
be seen as ensemble learning when considered for testing.
Face-Focused Cross-Stream Network for Deception Detection in Videos
Authors: Mingyu Ding, An Zhao, Zhiwu Lu, Tao Xiang, Ji-Rong Wen
Date: 2018-12-11 00:00:00
Proceeding: CVPR 2019 6
Tasks: Deception Detection, Deception Detection In Videos, Emotion Recognition, Face Detection, Meta-Learning

Automated deception detection (ADD) from real-life videos is a challenging
task. It specifically needs to address two problems: (1) Both face and body
contain useful cues regarding whether a subject is deceptive. How to
effectively fuse the two is thus key to the effectiveness of an ADD model. (2)
Real-life deceptive samples are hard to collect; learning with limited training
data thus challenges most deep learning based ADD models. In this work, both
problems are addressed. Specifically, for face-body multimodal learning, a
novel face-focused cross-stream network (FFCSN) is proposed. It differs
significantly from the popular two-stream networks in that: (a) face detection
is added into the spatial stream to capture the facial expressions explicitly,
and (b) correlation learning is performed across the spatial and temporal
streams for joint deep feature learning across both face and body. To address
the training data scarcity problem, our FFCSN model is trained with both meta
learning and adversarial learning. Extensive experiments show that our FFCSN
model achieves state-of-the-art results. Further, the proposed FFCSN model as
well as its robust training strategy are shown to be generally applicable to
other human-centric video analysis tasks such as emotion recognition from
user-generated videos.
Efficient learning of smooth probability functions from Bernoulli tests with guarantees
Authors: Paul Rolland, Ali Kavis, Alex Immer, Adish Singla, Volkan Cevher
Date: 2018-12-11 00:00:00

We study the fundamental problem of learning an unknown, smooth probability function via pointwise Bernoulli tests. We provide a scalable algorithm for efficiently solving this problem with rigorous guarantees. In particular, we prove the convergence rate of our posterior update rule to the true probability function in L2-norm. Moreover, we allow the Bernoulli tests to depend on contextual features and provide a modified inference engine with provable guarantees for this novel setting. Numerical results show that the empirical convergence rates match the theory, and illustrate the superiority of our approach in handling contextual features over the state-of-the-art.
Zero-Shot Learning with Sparse Attribute Propagation
Authors: Nanyi Fei, Jiechao Guan, Zhiwu Lu, Tao Xiang, Ji-Rong Wen
Date: 2018-12-11 00:00:00
Tasks: Attribute, Image Retrieval, Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize a set of unseen classes without
any training images. The standard approach to ZSL requires a set of training
images annotated with seen class labels and a semantic descriptor for
seen/unseen classes (attribute vector is the most widely used). Class
label/attribute annotation is expensive; it thus severely limits the
scalability of ZSL. In this paper, we define a new ZSL setting where only a few
annotated images are collected from each seen class. This is clearly more
challenging yet more realistic than the conventional ZSL setting. To overcome
the resultant image-level attribute sparsity, we propose a novel inductive ZSL
model termed sparse attribute propagation (SAP) by propagating attribute
annotations to more unannotated images using sparse coding. This is followed by
learning bidirectional projections between features and attributes for ZSL. An
efficient solver is provided, together with rigorous theoretic algorithm
analysis. With our SAP, we show that a ZSL training dataset can now be
augmented by the abundant web images returned by image search engine, to
further improve the model performance. Moreover, the general applicability of
SAP is demonstrated on solving the social image annotation (SIA) problem.
Extensive experiments show that our model achieves superior performance on both
ZSL and SIA.
Towards Automatic Identification of Elephants in the Wild
Authors: Matthias Körschens, Björn Barz, Joachim Denzler
Date: 2018-12-11 00:00:00

Identifying animals from a large group of possible individuals is very
important for biodiversity monitoring and especially for collecting data on a
small number of particularly interesting individuals, as these have to be
identified first before this can be done. Identifying them can be a very
time-consuming task. This is especially true, if the animals look very similar
and have only a small number of distinctive features, like elephants do. In
most cases the animals stay at one place only for a short period of time during
which the animal needs to be identified for knowing whether it is important to
collect new data on it. For this reason, a system supporting the researchers in
identifying elephants to speed up this process would be of great benefit. In
this paper, we present such a system for identifying elephants in the face of a
large number of individuals with only few training images per individual. For
that purpose, we combine object part localization, off-the-shelf CNN features,
and support vector machine classification to provide field researches with
proposals of possible individuals given new images of an elephant. The
performance of our system is demonstrated on a dataset comprising a total of
2078 images of 276 individual elephants, where we achieve 56% top-1 test
accuracy and 80% top-10 accuracy. To deal with occlusion, varying viewpoints,
and different poses present in the dataset, we furthermore enable the analysts
to provide the system with multiple images of the same elephant to be
identified and aggregate confidence values generated by the classifier. With
that, our system achieves a top-1 accuracy of 74% and a top-10 accuracy of 88%
on the held-out test dataset.
MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation
Authors: Chang Li, Ilya Markov, Maarten de Rijke, Masrour Zoghi
Date: 2018-12-11 00:00:00
Tasks: Information Retrieval, Online Ranker Evaluation, Retrieval, Thompson Sampling

Online ranker evaluation is one of the key challenges in information retrieval. While the preferences of rankers can be inferred by interleaving methods, the problem of how to effectively choose the ranker pair that generates the interleaved list without degrading the user experience too much is still challenging. On the one hand, if two rankers have not been compared enough, the inferred preference can be noisy and inaccurate. On the other, if two rankers are compared too many times, the interleaving process inevitably hurts the user experience too much. This dilemma is known as the exploration versus exploitation tradeoff. It is captured by the $K$-armed dueling bandit problem, which is a variant of the $K$-armed bandit problem, where the feedback comes in the form of pairwise preferences. Today's deployed search systems can evaluate a large number of rankers concurrently, and scaling effectively in the presence of numerous rankers is a critical aspect of $K$-armed dueling bandit problems. In this paper, we focus on solving the large-scale online ranker evaluation problem under the so-called Condorcet assumption, where there exists an optimal ranker that is preferred to all other rankers. We propose Merge Double Thompson Sampling (MergeDTS), which first utilizes a divide-and-conquer strategy that localizes the comparisons carried out by the algorithm to small batches of rankers, and then employs Thompson Sampling (TS) to reduce the comparisons between suboptimal rankers inside these small batches. The effectiveness (regret) and efficiency (time complexity) of MergeDTS are extensively evaluated using examples from the domain of online evaluation for web search. Our main finding is that for large-scale Condorcet ranker evaluation problems, MergeDTS outperforms the state-of-the-art dueling bandit algorithms.
Learning Item-Interaction Embeddings for User Recommendations
Authors: Xiaoting Zhao, Raphael Louca, Diane Hu, Liangjie Hong
Date: 2018-12-11 00:00:00
Tasks: Computational Efficiency, Recommendation Systems

Industry-scale recommendation systems have become a cornerstone of the
e-commerce shopping experience. For Etsy, an online marketplace with over 50
million handmade and vintage items, users come to rely on personalized
recommendations to surface relevant items from its massive inventory. One
hallmark of Etsy's shopping experience is the multitude of ways in which a user
can interact with an item they are interested in: they can view it, favorite
it, add it to a collection, add it to cart, purchase it, etc. We hypothesize
that the different ways in which a user interacts with an item indicates
different kinds of intent. Consequently, a user's recommendations should be
based not only on the item from their past activity, but also the way in which
they interacted with that item. In this paper, we propose a novel method for
learning interaction-based item embeddings that encode the co-occurrence
patterns of not only the item itself, but also the interaction type. The
learned embeddings give us a convenient way of approximating the likelihood
that one item-interaction pair would co-occur with another by way of a simple
inner product. Because of its computational efficiency, our model lends itself
naturally as a candidate set selection method, and we evaluate it as such in an
industry-scale recommendation system that serves live traffic on Etsy.com. Our
experiments reveal that taking interaction type into account shows promising
results in improving the accuracy of modeling user shopping behavior.
Conditional Variational Autoencoder for Neural Machine Translation
Authors: Artidoro Pagnoni, Kevin Liu, Shangyan Li
Date: 2018-12-11 00:00:00
Tasks: Conditional Text Generation, Decoder, Machine Translation, NMT, Text Generation, Translation

We explore the performance of latent variable models for conditional text
generation in the context of neural machine translation (NMT). Similar to Zhang
et al., we augment the encoder-decoder NMT paradigm by introducing a continuous
latent variable to model features of the translation process. We extend this
model with a co-attention mechanism motivated by Parikh et al. in the inference
network. Compared to the vision domain, latent variable models for text face
additional challenges due to the discrete nature of language, namely posterior
collapse. We experiment with different approaches to mitigate this issue. We
show that our conditional variational model improves upon both discriminative
attention-based translation and the variational baseline presented in Zhang et
al. Finally, we present some exploration of the learned latent space to
illustrate what the latent variable is capable of capturing. This is the first
reported conditional variational model for text that meaningfully utilizes the
latent variable without weakening the translation model.
Encoding prior knowledge in the structure of the likelihood
Authors: Jakob Knollmüller, Torsten A. Enßlin
Date: 2018-12-11 00:00:00

The inference of deep hierarchical models is problematic due to strong
dependencies between the hierarchies. We investigate a specific transformation
of the model parameters based on the multivariate distributional transform.
This transformation is a special form of the reparametrization trick, flattens
the hierarchy and leads to a standard Gaussian prior on all resulting
parameters. The transformation also transfers all the prior information into
the structure of the likelihood, hereby decoupling the transformed parameters a
priori from each other. A variational Gaussian approximation in this
standardized space will be excellent in situations of relatively uninformative
data. Additionally, the curvature of the log-posterior is well-conditioned in
directions that are weakly constrained by the data, allowing for fast inference
in such a scenario. In an example we perform the transformation explicitly for
Gaussian process regression with a priori unknown correlation structure. Deep
models are inferred rapidly in highly and slowly in poorly informed situations.
The flat model show exactly the opposite performance pattern. A synthesis of
both, the deep and the flat perspective, provides their combined advantages and
overcomes the individual limitations, leading to a faster inference.
From Adaptive Kernel Density Estimation to Sparse Mixture Models
Authors: Colas Schretter, Jianyong Sun, Peter Schelkens
Date: 2018-12-11 00:00:00
Tasks: Density Estimation

We introduce a balloon estimator in a generalized expectation-maximization
method for estimating all parameters of a Gaussian mixture model given one data
sample per mixture component. Instead of limiting explicitly the model size,
this regularization strategy yields low-complexity sparse models where the
number of effective mixture components reduces with an increase of a smoothing
probability parameter $\mathbf{P>0}$. This semi-parametric method bridges from
non-parametric adaptive kernel density estimation (KDE) to parametric ordinary
least-squares when $\mathbf{P=1}$. Experiments show that simpler sparse mixture
models retain the level of details present in the adaptive KDE solution.
Deep Reader: Information extraction from Document images via relation extraction and Natural Language
Authors: Vishwanath D, Rohit Rahul, Gunjan Sehgal, Swati, Arindam Chowdhury, Monika Sharma, Lovekesh Vig, Gautam Shroff, Ashwin Srinivasan
Date: 2018-12-11 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Relation, Relation Extraction

Recent advancements in the area of Computer Vision with state-of-art Neural
Networks has given a boost to Optical Character Recognition (OCR) accuracies.
However, extracting characters/text alone is often insufficient for relevant
information extraction as documents also have a visual structure that is not
captured by OCR. Extracting information from tables, charts, footnotes, boxes,
headings and retrieving the corresponding structured representation for the
document remains a challenge and finds application in a large number of
real-world use cases. In this paper, we propose a novel enterprise based
end-to-end framework called DeepReader which facilitates information extraction
from document images via identification of visual entities and populating a
meta relational model across different entities in the document image. The
model schema allows for an easy to understand abstraction of the entities
detected by the deep vision models and the relationships between them.
DeepReader has a suite of state-of-the-art vision algorithms which are applied
to recognize handwritten and printed text, eliminate noisy effects, identify
the type of documents and detect visual entities like tables, lines and boxes.
Deep Reader maps the extracted entities into a rich relational schema so as to
capture all the relevant relationships between entities (words, textboxes,
lines etc) detected in the document. Relevant information and fields can then
be extracted from the document by writing SQL queries on top of the
relationship tables. A natural language based interface is added on top of the
relationship schema so that a non-technical user, specifying the queries in
natural language, can fetch the information with minimal effort. In this paper,
we also demonstrate many different capabilities of Deep Reader and report
results on a real-world use case.
Sparse component separation from Poisson measurements
Authors: I. El Hamzaoui, J. Bobin
Date: 2018-12-11 00:00:00
Tasks: blind source separation

Blind source separation (BSS) aims at recovering signals from mixtures. This
problem has been extensively studied in cases where the mixtures are
contaminated with additive Gaussian noise. However, it is not well suited to
describe data that are corrupted with Poisson measurements such as in low
photon count optics or in high-energy astronomical imaging (e.g. observations
from the Chandra or Fermi telescopes). To that purpose, we propose a novel BSS
algorithm coined pGMCA that specifically tackles the blind separation of sparse
sources from Poisson measurements.
Variational Bayesian Weighted Complex Network Reconstruction
Authors: Shuang Xu, Chun-Xia Zhang, Pei Wang, Jiangshe Zhang
Date: 2018-12-11 00:00:00
Tasks: regression

Complex network reconstruction is a hot topic in many fields. Currently, the most popular data-driven reconstruction framework is based on lasso. However, it is found that, in the presence of noise, lasso loses efficiency for weighted networks. This paper builds a new framework to cope with this problem. The key idea is to employ a series of linear regression problems to model the relationship between network nodes, and then to use an efficient variational Bayesian algorithm to infer the unknown coefficients. The numerical experiments conducted on both synthetic and real data demonstrate that the new method outperforms lasso with regard to both reconstruction accuracy and running speed.
Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression
Authors: Yuchao Li, Shaohui Lin, Baochang Zhang, Jianzhuang Liu, David Doermann, Yongjian Wu, Feiyue Huang, Rongrong Ji
Date: 2018-12-11 00:00:00
Proceeding: CVPR 2019 6
Tasks: Clustering, Model Compression

Compressing convolutional neural networks (CNNs) has received ever-increasing
research focus. However, most existing CNN compression methods do not interpret
their inherent structures to distinguish the implicit redundancy. In this
paper, we investigate the problem of CNN compression from a novel interpretable
perspective. The relationship between the input feature maps and 2D kernels is
revealed in a theoretical framework, based on which a kernel sparsity and
entropy (KSE) indicator is proposed to quantitate the feature map importance in
a feature-agnostic manner to guide model compression. Kernel clustering is
further conducted based on the KSE indicator to accomplish high-precision CNN
compression. KSE is capable of simultaneously compressing each layer in an
efficient way, which is significantly faster compared to previous data-driven
feature map pruning methods. We comprehensively evaluate the compression and
speedup of the proposed method on CIFAR-10, SVHN and ImageNet 2012. Our method
demonstrates superior performance gains over previous ones. In particular, it
achieves 4.7 \times FLOPs reduction and 2.9 \times compression on ResNet-50
with only a Top-5 accuracy drop of 0.35% on ImageNet 2012, which significantly
outperforms state-of-the-art methods.
Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes
Authors: Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric
Date: 2018-12-11 00:00:00
Tasks: Efficient Exploration

We introduce and analyse two algorithms for exploration-exploitation in
discrete and continuous Markov Decision Processes (MDPs) based on exploration
bonuses. SCAL$^+$ is a variant of SCAL (Fruit et al., 2018) that performs
efficient exploration-exploitation in any unknown weakly-communicating MDP for
which an upper bound C on the span of the optimal bias function is known. For
an MDP with $S$ states, $A$ actions and $\Gamma \leq S$ possible next states,
we prove that SCAL$^+$ achieves the same theoretical guarantees as SCAL (i.e.,
a high probability regret bound of $\widetilde{O}(C\sqrt{\Gamma SAT})$), with a
much smaller computational complexity. Similarly, C-SCAL$^+$ exploits an
exploration bonus to achieve sublinear regret in any undiscounted MDP with
continuous state space. We show that C-SCAL$^+$ achieves the same regret bound
as UCCRL (Ortner and Ryabko, 2012) while being the first implementable
algorithm with regret guarantees in this setting. While optimistic algorithms
such as UCRL, SCAL or UCCRL maintain a high-confidence set of plausible MDPs
around the true unknown MDP, SCAL$^+$ and C-SCAL$^+$ leverage on an exploration
bonus to directly plan on the empirically estimated MDP, thus being more
computationally efficient.
RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information
Authors: Shikhar Vashishth, Rishabh Joshi, Sai Suman Prayaga, Chiranjib Bhattacharyya, Partha Talukdar
Date: 2018-12-11 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Relation, Relation Extraction, Relationship Extraction (Distant Supervised)

Distantly-supervised Relation Extraction (RE) methods train an extractor by
automatically aligning relation instances in a Knowledge Base (KB) with
unstructured text. In addition to relation instances, KBs often contain other
relevant side information, such as aliases of relations (e.g., founded and
co-founded are aliases for the relation founderOfCompany). RE models usually
ignore such readily available side information. In this paper, we propose
RESIDE, a distantly-supervised neural relation extraction method which utilizes
additional side information from KBs for improved relation extraction. It uses
entity type and relation alias information for imposing soft constraints while
predicting relations. RESIDE employs Graph Convolution Networks (GCN) to encode
syntactic information from text and improves performance even when limited side
information is available. Through extensive experiments on benchmark datasets,
we demonstrate RESIDE's effectiveness. We have made RESIDE's source code
available to encourage reproducible research.
Efficient Model-Free Reinforcement Learning Using Gaussian Process
Authors: Ying Fan, Letian Chen, Yizhou Wang
Date: 2018-12-11 00:00:00
Tasks: model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Efficient Reinforcement Learning usually takes advantage of demonstration or
good exploration strategy. By applying posterior sampling in model-free RL
under the hypothesis of GP, we propose Gaussian Process Posterior Sampling
Reinforcement Learning(GPPSTD) algorithm in continuous state space, giving
theoretical justifications and empirical results. We also provide theoretical
and empirical results that various demonstration could lower expected
uncertainty and benefit posterior sampling exploration. In this way, we
combined the demonstration and exploration process together to achieve a more
efficient reinforcement learning.