pwc-archive/papers-with-abstracts huggingface dataset:
Supervector Compression Strategies to Speed up I-Vector System Development
Authors: Ville Vestman, Tomi Kinnunen
Date: 2018-05-03 00:00:00
Tasks: Speaker Verification

The front-end factor analysis (FEFA), an extension of principal component
analysis (PPCA) tailored to be used with Gaussian mixture models (GMMs), is
currently the prevalent approach to extract compact utterance-level features
(i-vectors) for automatic speaker verification (ASV) systems. Little research
has been conducted comparing FEFA to the conventional PPCA applied to maximum a
posteriori (MAP) adapted GMM supervectors. We study several alternative
methods, including PPCA, factor analysis (FA), and two supervised approaches,
supervised PPCA (SPPCA) and the recently proposed probabilistic partial least
squares (PPLS), to compress MAP-adapted GMM supervectors. The resulting
i-vectors are used in ASV tasks with a probabilistic linear discriminant
analysis (PLDA) back-end. We experiment on two different datasets, on the
telephone condition of NIST SRE 2010 and on the recent VoxCeleb corpus
collected from YouTube videos containing celebrity interviews recorded in
various acoustical and technical conditions. The results suggest that, in terms
of ASV accuracy, the supervector compression approaches are on a par with FEFA.
The supervised approaches did not result in improved performance. In comparison
to FEFA, we obtained more than hundred-fold (100x) speedups in the total
variability model (TVM) training using the PPCA and FA supervector compression
approaches.
N-fold Superposition: Improving Neural Networks by Reducing the Noise in Feature Maps
Authors: Yang Liu, Qiang Qu, Chao GAO
Date: 2018-04-23 00:00:00
Tasks: LEMMA

Considering the use of Fully Connected (FC) layer limits the performance of
Convolutional Neural Networks (CNNs), this paper develops a method to improve
the coupling between the convolution layer and the FC layer by reducing the
noise in Feature Maps (FMs). Our approach is divided into three steps. Firstly,
we separate all the FMs into n blocks equally. Then, the weighted summation of
FMs at the same position in all blocks constitutes a new block of FMs. Finally,
we replicate this new block into n copies and concatenate them as the input to
the FC layer. This sharing of FMs could reduce the noise in them apparently and
avert the impact by a particular FM on the specific part weight of hidden
layers, hence preventing the network from overfitting to some extent. Using the
Fermat Lemma, we prove that this method could make the global minima value
range of the loss function wider, by which makes it easier for neural networks
to converge and accelerates the convergence process. This method does not
significantly increase the amounts of network parameters (only a few more
coefficients added), and the experiments demonstrate that this method could
increase the convergence speed and improve the classification performance of
neural networks.
VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution
Authors: Rui Wang, Jeff Clune, Kenneth O. Stanley
Date: 2018-05-03 00:00:00
Tasks: Data Visualization, Evolutionary Algorithms, Reinforcement Learning, Reinforcement Learning (RL)

Recent advances in deep neuroevolution have demonstrated that evolutionary
algorithms, such as evolution strategies (ES) and genetic algorithms (GA), can
scale to train deep neural networks to solve difficult reinforcement learning
(RL) problems. However, it remains a challenge to analyze and interpret the
underlying process of neuroevolution in such high dimensions. To begin to
address this challenge, this paper presents an interactive data visualization
tool called VINE (Visual Inspector for NeuroEvolution) aimed at helping
neuroevolution researchers and end-users better understand and explore this
family of algorithms. VINE works seamlessly with a breadth of neuroevolution
algorithms, including ES and GA, and addresses the difficulty of observing the
underlying dynamics of the learning process through an interactive
visualization of the evolving agent's behavior characterizations over
generations. As neuroevolution scales to neural networks with millions or more
connections, visualization tools like VINE that offer fresh insight into the
underlying dynamics of evolution become increasingly valuable and important for
inspiring new innovations and applications.
Unsupervised Features for Facial Expression Intensity Estimation over Time
Authors: Maren Awiszus, Stella Graßhof, Felix Kuhnke, Jörn Ostermann
Date: 2018-05-02 00:00:00
Tasks: Diversity

The diversity of facial shapes and motions among persons is one of the
greatest challenges for automatic analysis of facial expressions. In this
paper, we propose a feature describing expression intensity over time, while
being invariant to person and the type of performed expression. Our feature is
a weighted combination of the dynamics of multiple points adapted to the
overall expression trajectory. We evaluate our method on several tasks all
related to temporal analysis of facial expression. The proposed feature is
compared to a state-of-the-art method for expression intensity estimation,
which it outperforms. We use our proposed feature to temporally align multiple
sequences of recorded 3D facial expressions. Furthermore, we show how our
feature can be used to reveal person-specific differences in performances of
facial expressions. Additionally, we apply our feature to identify the local
changes in face video sequences based on action unit labels. For all the
experiments our feature proves to be robust against noise and outliers, making
it applicable to a variety of applications for analysis of facial movements.
Nonparametric Pricing Analytics with Customer Covariates
Authors: Ningyuan Chen, Guillermo Gallego
Date: 2018-05-03 00:00:00

Personalized pricing analytics is becoming an essential tool in retailing. Upon observing the personalized information of each arriving customer, the firm needs to set a price accordingly based on the covariates such as income, education background, past purchasing history to extract more revenue. For new entrants of the business, the lack of historical data may severely limit the power and profitability of personalized pricing. We propose a nonparametric pricing policy to simultaneously learn the preference of customers based on the covariates and maximize the expected revenue over a finite horizon. The policy does not depend on any prior assumptions on how the personalized information affects consumers' preferences (such as linear models). It is adaptively splits the covariate space into smaller bins (hyper-rectangles) and clusters customers based on their covariates and preferences, offering similar prices for customers who belong to the same cluster trading off granularity and accuracy. We show that the algorithm achieves a regret of order $O(\log(T)^2 T^{(2+d)/(4+d)})$, where $T$ is the length of the horizon and $d$ is the dimension of the covariate. It improves the current regret in the literature \citep{slivkins2014contextual}, under mild technical conditions in the pricing context (smoothness and local concavity). We also prove that no policy can achieve a regret less than $O(T^{(2+d)/(4+d)})$ for a particular instance and thus demonstrate the near optimality of the proposed policy.
A Tutorial on Deep Learning for Music Information Retrieval
Authors: Keunwoo Choi, György Fazekas, Kyunghyun Cho, Mark Sandler
Date: 2017-09-13 00:00:00
Tasks: Deep Learning, Information Retrieval, Music Information Retrieval, Navigate, Retrieval

Following their success in Computer Vision and other areas, deep learning
techniques have recently become widely adopted in Music Information Retrieval
(MIR) research. However, the majority of works aim to adopt and assess methods
that have been shown to be effective in other domains, while there is still a
great need for more original research focusing on music primarily and utilising
musical knowledge and insight. The goal of this paper is to boost the interest
of beginners by providing a comprehensive tutorial and reducing the barriers to
entry into deep learning for MIR. We lay out the basic principles and review
prominent works in this hard to navigate the field. We then outline the network
structures that have been successful in MIR problems and facilitate the
selection of building blocks for the problems at hand. Finally, guidelines for
new tasks and some advanced topics in deep learning are discussed to stimulate
new research in this fascinating field.
Learning of Colors from Color Names: Distribution and Point Estimation
Authors: Lyndon White, Roberto Togneri, Wei Liu, Mohammed Bennamoun
Date: 2017-09-27 00:00:00
Tasks: Natural Language Understanding, Word Embeddings

Color names are often made up of multiple words. As a task in natural language understanding we investigate in depth the capacity of neural networks based on sums of word embeddings (SOWE), recurrence (LSTM and GRU based RNNs) and convolution (CNN), to estimate colors from sequences of terms. We consider both point and distribution estimates of color. We argue that the latter has a particular value as there is no clear agreement between people as to what a particular color describes -- different people have a different idea of what it means to be ``very dark orange'', for example. Surprisingly, despite it's simplicity, the sum of word embeddings generally performs the best on almost all evaluations.
Local Critic Training of Deep Neural Networks
Authors: Hojung Lee, Jong-Seok Lee
Date: 2018-05-03 00:00:00
Proceeding: ICLR 2019 5
Tasks: General Classification

This paper proposes a novel approach to train deep neural networks by
unlocking the layer-wise dependency of backpropagation training. The approach
employs additional modules called local critic networks besides the main
network model to be trained, which are used to obtain error gradients without
complete feedforward and backward propagation processes. We propose a cascaded
learning strategy for these local networks. In addition, the approach is also
useful from multi-model perspectives, including structural optimization of
neural networks, computationally efficient progressive inference, and ensemble
classification for performance improvement. Experimental results show the
effectiveness of the proposed approach and suggest guidelines for determining
appropriate algorithm parameters.
Binarizer at SemEval-2018 Task 3: Parsing dependency and deep learning for irony detection
Authors: Nishant Nikhil, Muktabh Mayank Srivastava
Date: 2018-05-03 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: General Classification

In this paper, we describe the system submitted for the SemEval 2018 Task 3
(Irony detection in English tweets) Subtask A by the team Binarizer. Irony
detection is a key task for many natural language processing works. Our method
treats ironical tweets to consist of smaller parts containing different
emotions. We break down tweets into separate phrases using a dependency parser.
We then embed those phrases using an LSTM-based neural network model which is
pre-trained to predict emoticons for tweets. Finally, we train a
fully-connected network to achieve classification.
PURE: Scalable Phase Unwrapping with Spatial Redundant Arcs
Authors: Ravi Lanka
Date: 2018-04-19 00:00:00

Phase unwrapping is a key problem in many coherent imaging systems, such as
synthetic aperture radar (SAR) interferometry. A general formulation for
redundant integration of finite differences for phase unwrapping (Costantini et
al., 2010) was shown to produce a more reliable solution by exploiting
redundant differential estimates. However, this technique requires a commercial
linear programming solver for large-scale problems. For a linear cost function,
we propose a method based on Dual Decomposition that breaks the given problem
defined over a non-planar graph into tractable sub-problems over planar
subgraphs. We also propose a decomposition technique that exploits the
underlying graph structure for solving the sub-problems efficiently and
guarantees asymptotic convergence to the globally optimal solution. The
experimental results demonstrate that the proposed approach is comparable to
the existing state-of-the-art methods in terms of the estimate with a better
runtime and memory footprint.
DisSent: Sentence Representation Learning from Explicit Discourse Relations
Authors: Allen Nie, Erin D. Bennett, Noah D. Goodman
Date: 2017-10-12 00:00:00
Tasks: Dependency Parsing, Natural Language Understanding, Relation, Relation Prediction, Representation Learning, Sentence, Sentence Embeddings

Learning effective representations of sentences is one of the core missions of natural language understanding. Existing models either train on a vast amount of text, or require costly, manually curated sentence relation datasets. We show that with dependency parsing and rule-based rubrics, we can curate a high quality sentence relation task by leveraging explicit discourse relations. We show that our curated dataset provides an excellent signal for learning vector representations of sentence meaning, representing relations that can only be determined when the meanings of two sentences are combined. We demonstrate that the automatically curated corpus allows a bidirectional LSTM sentence encoder to yield high quality sentence embeddings and can serve as a supervised fine-tuning dataset for larger models such as BERT. Our fixed sentence embeddings achieve high performance on a variety of transfer tasks, including SentEval, and we achieve state-of-the-art results on Penn Discourse Treebank's implicit relation prediction task.
A generalized method toward drug-target interaction prediction via low-rank matrix projection
Authors: Ratha Pech, Dong Hao, Yan-Li Lee, Maryna Po, Tao Zhou
Date: 2017-06-06 00:00:00
Tasks: BIG-bench Machine Learning, Drug Discovery, Link Prediction, Prediction

Drug-target interaction (DTI) prediction plays a very important role in drug
development and drug discovery. Biochemical experiments or \textit{in vitro}
methods are very expensive, laborious and time-consuming. Therefore, \textit{in
silico} approaches including docking simulation and machine learning have been
proposed to solve this problem. In particular, machine learning approaches have
attracted increasing attentions recently. However, in addition to the known
drug-target interactions, most of the machine learning methods require extra
characteristic information such as chemical structures, genome sequences,
binding types and so on. Whenever such information is not available, they may
perform poor. Very recently, the similarity-based link prediction methods were
extended to bipartite networks, which can be applied to solve the DTI
prediction problem by using topological information only. In this work, we
propose a method based on low-rank matrix projection to solve the DTI
prediction problem. On one hand, when there is no extra characteristic
information of drugs or targets, the proposed method utilizes only the known
interactions. On the other hand, the proposed method can also utilize the extra
characteristic information when it is available and the performances will be
remarkably improved. Moreover, the proposed method can predict the interactions
associated with new drugs or targets of which we know nothing about their
associated interactions, but only some characteristic information. We compare
the proposed method with ten baseline methods, e.g., six similarity-based
methods that utilize only the known interactions and four methods that utilize
the extra characteristic information. The datasets and codes implementing the
simulations are available at https://github.com/rathapech/DTI_LMP.
Globally Variance-Constrained Sparse Representation and Its Application in Image Set Coding
Authors: Xiang Zhang, Jiarui Sun, Siwei Ma, Zhouchen Lin, Jian Zhang, Shiqi Wang, Wen Gao
Date: 2016-08-17 00:00:00
Tasks: Data Compression, Dictionary Learning

Sparse representation leads to an efficient way to approximately recover a
signal by the linear composition of a few bases from a learnt dictionary, based
on which various successful applications have been achieved. However, in the
scenario of data compression, its efficiency and popularity are hindered. It is
because of the fact that encoding sparsely distributed coefficients may consume
more bits for representing the index of nonzero coefficients. Therefore,
introducing an accurate rate-constraint in sparse coding and dictionary
learning becomes meaningful, which has not been fully exploited in the context
of sparse representation. According to the Shannon entropy inequality, the
variance of a Gaussian distributed data bounds its entropy, indicating the
actual bitrate can be well estimated by its variance. Hence, a Globally
Variance-Constrained Sparse Representation (GVCSR) model is proposed in this
work, where a variance-constrained rate term is introduced to the optimization
process. Specifically, we employ the Alternating Direction Method of
Multipliers (ADMM) to solve the non-convex optimization problem for sparse
coding and dictionary learning, both of them have shown the state-of-the-art
rate-distortion performance for image representation. Furthermore, we
investigate the potential of applying the GVCSR algorithm in the practical
image set compression, where the optimized dictionary is trained to efficiently
represent the images captured in similar scenarios by implicitly utilizing
inter-image correlations. Experimental results have demonstrated superior
rate-distortion performance against the state-of-the-art methods.
Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images
Authors: Vahid Mirjalili, Sebastian Raschka, Anoop Namboodiri, Arun Ross
Date: 2017-12-01 00:00:00
Tasks: Face Recognition, Gender Classification

In this paper, we design and evaluate a convolutional autoencoder that
perturbs an input face image to impart privacy to a subject. Specifically, the
proposed autoencoder transforms an input face image such that the transformed
image can be successfully used for face recognition but not for gender
classification. In order to train this autoencoder, we propose a novel training
scheme, referred to as semi-adversarial training in this work. The training is
facilitated by attaching a semi-adversarial module consisting of a pseudo
gender classifier and a pseudo face matcher to the autoencoder. The objective
function utilized for training this network has three terms: one to ensure that
the perturbed image is a realistic face image; another to ensure that the
gender attributes of the face are confounded; and a third to ensure that
biometric recognition performance due to the perturbed image is not impacted.
Extensive experiments confirm the efficacy of the proposed architecture in
extending gender privacy to face images.
The feasibility of automated identification of six algae types using neural networks and fluorescence-based spectral-morphological features
Authors: Jason L. Deglint, Chao Jin, Angela Chao, Alexander Wong
Date: 2018-05-03 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Harmful algae blooms (HABs), which produce lethal toxins, are a growing
global concern since they negatively affect the quality of drinking water and
have major negative impact on wildlife, the fishing industry, as well as
tourism and recreational water use. In this study, we investigate the
feasibility of leveraging machine learning and fluorescence-based
spectral-morphological features to enable the identification of six different
algae types in an automated fashion. More specifically, a custom multi-band
fluorescence imaging microscope is used to capture fluorescence imaging data of
a water sample at six different excitation wavelengths ranging from 405 nm -
530 nm. A number of morphological and spectral fluorescence features are then
extracted from the isolated micro-organism imaging data, and used to train
neural network classification models designed for the purpose of identification
of the six algae types given an isolated micro-organism. Experimental results
using three different neural network classification models showed that the use
of either fluorescence-based spectral features or fluorescence-based
spectral-morphological features to train neural network classification models
led to statistically significant improvements in identification accuracy when
compared to the use of morphological features (with average identification
accuracies of 95.7%+/-3.5% and 96.1%+/-1.5%, respectively). These preliminary
results are quite promising, given that the identification accuracy of human
taxonomists are typically between the range of 67% and 83%, and thus
illustrates the feasibility of leveraging machine learning and
fluorescence-based spectral-morphological features as a viable method for
automated identification of different algae types.
USAR: an Interactive User-specific Aesthetic Ranking Framework for Images
Authors: Pei Lv, Meng Wang, Yongbo Xu, Ze Peng, Junyi Sun, Shimei Su, Bing Zhou, Mingliang Xu
Date: 2018-05-03 00:00:00

When assessing whether an image is of high or low quality, it is
indispensable to take personal preference into account. Existing aesthetic
models lay emphasis on hand-crafted features or deep features commonly shared
by high quality images, but with limited or no consideration for personal
preference and user interaction. To that end, we propose a novel and
user-friendly aesthetic ranking framework via powerful deep neural network and
a small amount of user interaction, which can automatically estimate and rank
the aesthetic characteristics of images in accordance with users' preference.
Our framework takes as input a series of photos that users prefer, and produces
as output a reliable, user-specific aesthetic ranking model matching with
users' preference. Considering the subjectivity of personal preference and the
uncertainty of user's single selection, a unique and exclusive dataset will be
constructed interactively to describe the preference of one individual by
retrieving the most similar images with regard to those specified by users.
Based on this unique user-specific dataset and sufficient well-designed
aesthetic attributes, a customized aesthetic distribution model can be learned,
which concatenates both personalized preference and aesthetic rules. We conduct
extensive experiments and user studies on two large-scale public datasets, and
demonstrate that our framework outperforms those work based on conventional
aesthetic assessment or ranking model.