pwc-archive/papers-with-abstracts huggingface dataset:
Toward Explainable Fashion Recommendation
Authors: Pongsate Tangseng, Takayuki Okatani
Date: 2019-01-15 00:00:00

Many studies have been conducted so far to build systems for recommending fashion items and outfits. Although they achieve good performances in their respective tasks, most of them cannot explain their judgments to the users, which compromises their usefulness. Toward explainable fashion recommendation, this study proposes a system that is able not only to provide a goodness score for an outfit but also to explain the score by providing reason behind it. For this purpose, we propose a method for quantifying how influential each feature of each item is to the score. Using this influence value, we can identify which item and what feature make the outfit good or bad. We represent the image of each item with a combination of human-interpretable features, and thereby the identification of the most influential item-feature pair gives useful explanation of the output score. To evaluate the performance of this approach, we design an experiment that can be performed without human annotation; we replace a single item-feature pair in an outfit so that the score will decrease, and then we test if the proposed method can detect the replaced item correctly using the above influence values. The experimental results show that the proposed method can accurately detect bad items in outfits lowering their scores.
Practical Lossless Compression with Latent Variables using Bits Back Coding
Authors: James Townsend, Tom Bird, David Barber
Date: 2019-01-15 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image Compression

Deep latent variable models have seen recent success in many data domains.
Lossless compression is an application of these models which, despite having
the potential to be highly useful, has yet to be implemented in a practical
manner. We present `Bits Back with ANS' (BB-ANS), a scheme to perform lossless
compression with latent variable models at a near optimal rate. We demonstrate
this scheme by using it to compress the MNIST dataset with a variational
auto-encoder model (VAE), achieving compression rates superior to standard
methods with only a simple VAE. Given that the scheme is highly amenable to
parallelization, we conclude that with a sufficiently high quality generative
model this scheme could be used to achieve substantial improvements in
compression rate with acceptable running time. We make our implementation
available open source at https://github.com/bits-back/bits-back .
Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data
Authors: Felix M. Riese, Sina Keller
Date: 2019-01-15 00:00:00
Tasks: General Classification, Texture Classification

Soil texture is important for many environmental processes. In this paper, we
study the classification of soil texture based on hyperspectral data. We
develop and implement three 1-dimensional (1D) convolutional neural networks
(CNN): the LucasCNN, the LucasResNet which contains an identity block as
residual network, and the LucasCoordConv with an additional coordinates layer.
Furthermore, we modify two existing 1D CNN approaches for the presented
classification task. The code of all five CNN approaches is available on GitHub
(Riese, 2019). We evaluate the performance of the CNN approaches and compare
them to a random forest classifier. Thereby, we rely on the freely available
LUCAS topsoil dataset. The CNN approach with the least depth turns out to be
the best performing classifier. The LucasCoordConv achieves the best
performance regarding the average accuracy. In future work, we can further
enhance the introduced LucasCNN, LucasResNet and LucasCoordConv and include
additional variables of the rich LUCAS dataset.
Exploiting Synchronized Lyrics And Vocal Features For Music Emotion Detection
Authors: Loreto Parisi, Simone Francia, Silvio Olivastri, Maria Stella Tavella
Date: 2019-01-15 00:00:00
Tasks: Information Retrieval, Music Information Retrieval, Music Recommendation, Retrieval

One of the key points in music recommendation is authoring engaging playlists
according to sentiment and emotions. While previous works were mostly based on
audio for music discovery and playlists generation, we take advantage of our
synchronized lyrics dataset to combine text representations and music features
in a novel way; we therefore introduce the Synchronized Lyrics Emotion Dataset.
Unlike other approaches that randomly exploited the audio samples and the whole
text, our data is split according to the temporal information provided by the
synchronization between lyrics and audio. This work shows a comparison between
text-based and audio-based deep learning classification models using different
techniques from Natural Language Processing and Music Information Retrieval
domains. From the experiments on audio we conclude that using vocals only,
instead of the whole audio data improves the overall performances of the audio
classifier. In the lyrics experiments we exploit the state-of-the-art word
representations applied to the main Deep Learning architectures available in
literature. In our benchmarks the results show how the Bilinear LSTM classifier
with Attention based on fastText word embedding performs better than the CNN
applied on audio.
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC
Authors: Andrés F. López-Lopera, François Bachoc, Nicolas Durrande, Jérémy Rohmer, Déborah Idier, Olivier Roustant
Date: 2019-01-15 00:00:00
Tasks: Gaussian Processes

Adding inequality constraints (e.g. boundedness, monotonicity, convexity) into Gaussian processes (GPs) can lead to more realistic stochastic emulators. Due to the truncated Gaussianity of the posterior, its distribution has to be approximated. In this work, we consider Monte Carlo (MC) and Markov Chain Monte Carlo (MCMC) methods. However, strictly interpolating the observations may entail expensive computations due to highly restrictive sample spaces. Furthermore, having (constrained) GP emulators when data are actually noisy is also of interest for real-world implementations. Hence, we introduce a noise term for the relaxation of the interpolation conditions, and we develop the corresponding approximation of GP emulators under linear inequality constraints. We show with various toy examples that the performance of MC and MCMC samplers improves when considering noisy observations. Finally, on 2D and 5D coastal flooding applications, we show that more flexible and realistic GP implementations can be obtained by considering noise effects and by enforcing the (linear) inequality constraints.
Learning Direct and Inverse Transmission Matrices
Authors: Daniele Ancora, Luca Leuzzi
Date: 2019-01-15 00:00:00
Tasks: regression

Linear problems appear in a variety of disciplines and their application for
the transmission matrix recovery is one of the most stimulating challenges in
biomedical imaging. Its knowledge turns any random media into an optical tool
that can focus or transmit an image through disorder. Here, converting an
input-output problem into a statistical mechanical formulation, we investigate
how inference protocols can learn the transmission couplings by
pseudolikelihood maximization. Bridging linear regression and thermodynamics
let us propose an innovative framework to pursue the solution of the
scattering-riddle.
Mixed Variational Inference
Authors: Nikolaos Gianniotis
Date: 2019-01-15 00:00:00
Tasks: Bayesian Inference, Variational Inference

The Laplace approximation has been one of the workhorses of Bayesian inference. It often delivers good approximations in practice despite the fact that it does not strictly take into account where the volume of posterior density lies. Variational approaches avoid this issue by explicitly minimising the Kullback-Leibler divergence DKL between a postulated posterior and the true (unnormalised) logarithmic posterior. However, they rely on a closed form DKL in order to update the variational parameters. To address this, stochastic versions of variational inference have been devised that approximate the intractable DKL with a Monte Carlo average. This approximation allows calculating gradients with respect to the variational parameters. However, variational methods often postulate a factorised Gaussian approximating posterior. In doing so, they sacrifice a-posteriori correlations. In this work, we propose a method that combines the Laplace approximation with the variational approach. The advantages are that we maintain: applicability on non-conjugate models, posterior correlations and a reduced number of free variational parameters. Numerical experiments demonstrate improvement over the Laplace approximation and variational inference with factorised Gaussian posteriors.
A Tweet Dataset Annotated for Named Entity Recognition and Stance Detection
Authors: Dilek Küçük, Fazli Can
Date: 2019-01-15 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Stance Detection

Annotated datasets in different domains are critical for many supervised
learning-based solutions to related problems and for the evaluation of the
proposed solutions. Topics in natural language processing (NLP) similarly
require annotated datasets to be used for such purposes. In this paper, we
target at two NLP problems, named entity recognition and stance detection, and
present the details of a tweet dataset in Turkish annotated for named entity
and stance information. Within the course of the current study, both the named
entity and stance annotations of the included tweets are made publicly
available, although previously the dataset has been publicly shared with stance
annotations only. We believe that this dataset will be useful for uncovering
the possible relationships between named entity recognition and stance
detection in tweets.
Transfer Learning for Prosthetics Using Imitation Learning
Authors: Montaser Mohammedalamen, Waleed D. Khamies, Benjamin Rosman
Date: 2019-01-15 00:00:00
Tasks: Benchmarking, Imitation Learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

In this paper, We Apply Reinforcement learning (RL) techniques to train a
realistic biomechanical model to work with different people and on different
walking environments. We benchmarking 3 RL algorithms: Deep Deterministic
Policy Gradient (DDPG), Trust Region Policy Optimization (TRPO) and Proximal
Policy Optimization (PPO) in OpenSim environment, Also we apply imitation
learning to a prosthetics domain to reduce the training time needed to design
customized prosthetics. We use DDPG algorithm to train an original expert
agent. We then propose a modification to the Dataset Aggregation (DAgger)
algorithm to reuse the expert knowledge and train a new target agent to
replicate that behaviour in fewer than 5 iterations, compared to the 100
iterations taken by the expert agent which means reducing training time by 95%.
Our modifications to the DAgger algorithm improve the balance between
exploiting the expert policy and exploring the environment. We show empirically
that these improve convergence time of the target agent, particularly when
there is some degree of variation between expert and naive agent.
Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges
Authors: Songül Tolan
Date: 2019-01-15 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Fairness

Machine learning algorithms are now frequently used in sensitive contexts
that substantially affect the course of human lives, such as credit lending or
criminal justice. This is driven by the idea that `objective' machines base
their decisions solely on facts and remain unaffected by human cognitive
biases, discriminatory tendencies or emotions. Yet, there is overwhelming
evidence showing that algorithms can inherit or even perpetuate human biases in
their decision making when they are based on data that contains biased human
decisions. This has led to a call for fairness-aware machine learning. However,
fairness is a complex concept which is also reflected in the attempts to
formalize fairness for algorithmic decision making. Statistical formalizations
of fairness lead to a long list of criteria that are each flawed (or harmful
even) in different contexts. Moreover, inherent tradeoffs in these criteria
make it impossible to unify them in one general framework. Thus, fairness
constraints in algorithms have to be specific to the domains to which the
algorithms are applied. In the future, research in algorithmic decision making
systems should be aware of data and developer biases and add a focus on
transparency to facilitate regular fairness audits.
Imitation-Regularized Offline Learning
Authors: Yifei Ma, Yu-Xiang Wang, Balakrishnan, Narayanaswamy
Date: 2019-01-15 00:00:00
Tasks: counterfactual, Multi-Armed Bandits

We study the problem of offline learning in automated decision systems under
the contextual bandits model. We are given logged historical data consisting of
contexts, (randomized) actions, and (nonnegative) rewards. A common goal is to
evaluate what would happen if different actions were taken in the same
contexts, so as to optimize the action policies accordingly. The typical
approach to this problem, inverse probability weighted estimation (IPWE)
[Bottou et al., 2013], requires logged action probabilities, which may be
missing in practice due to engineering complications. Even when available,
small action probabilities cause large uncertainty in IPWE, rendering the
corresponding results insignificant. To solve both problems, we show how one
can use policy improvement (PIL) objectives, regularized by policy imitation
(IML). We motivate and analyze PIL as an extension to Clipped-IPWE, by showing
that both are lower-bound surrogates to the vanilla IPWE. We also formally
connect IML to IPWE variance estimation [Swaminathan and Joachims 2015] and
natural policy gradients. Without probability logging, our PIL-IML
interpretations justify and improve, by reward-weighting, the state-of-art
cross-entropy (CE) loss that predicts the action items among all action
candidates available in the same contexts. With probability logging, our main
theoretical contribution connects IML-underfitting to the existence of either
confounding variables or model misspecification. We show the value and accuracy
of our insights by simulations based on Simpson's paradox, standard UCI
multiclass-to-bandit conversions and on the Criteo counterfactual analysis
challenge dataset.
Global-to-local Memory Pointer Networks for Task-Oriented Dialogue
Authors: Chien-Sheng Wu, Richard Socher, Caiming Xiong
Date: 2019-01-15 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decoder, Task-Oriented Dialogue Systems

End-to-end task-oriented dialogue is challenging since knowledge bases are
usually large, dynamic and hard to incorporate into a learning framework. We
propose the global-to-local memory pointer (GLMP) networks to address this
issue. In our model, a global memory encoder and a local memory decoder are
proposed to share external knowledge. The encoder encodes dialogue history,
modifies global contextual representation, and generates a global memory
pointer. The decoder first generates a sketch response with unfilled slots.
Next, it passes the global memory pointer to filter the external knowledge for
relevant information, then instantiates the slots via the local memory
pointers. We empirically show that our model can improve copy accuracy and
mitigate the common out-of-vocabulary problem. As a result, GLMP is able to
improve over the previous state-of-the-art models in both simulated bAbI
Dialogue dataset and human-human Stanford Multi-domain Dialogue dataset on
automatic and human evaluation.
DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System
Authors: Zhi-Hong Deng, Ling Huang, Chang-Dong Wang, Jian-Huang Lai, Philip S. Yu
Date: 2019-01-15 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems, Representation Learning

In general, recommendation can be viewed as a matching problem, i.e., match
proper items for proper users. However, due to the huge semantic gap between
users and items, it's almost impossible to directly match users and items in
their initial representation spaces. To solve this problem, many methods have
been studied, which can be generally categorized into two types, i.e.,
representation learning-based CF methods and matching function learning-based
CF methods. Representation learning-based CF methods try to map users and items
into a common representation space. In this case, the higher similarity between
a user and an item in that space implies they match better. Matching function
learning-based CF methods try to directly learn the complex matching function
that maps user-item pairs to matching scores. Although both methods are well
developed, they suffer from two fundamental flaws, i.e., the limited
expressiveness of dot product and the weakness in capturing low-rank relations
respectively. To this end, we propose a general framework named DeepCF, short
for Deep Collaborative Filtering, to combine the strengths of the two types of
methods and overcome such flaws. Extensive experiments on four publicly
available datasets demonstrate the effectiveness of the proposed DeepCF
framework.
Phoneme-Based Persian Speech Recognition
Authors: Saber Malekzadeh
Date: 2019-01-15 00:00:00
Tasks: speech-recognition, Speech Recognition

Undoubtedly, one of the most important issues in computer science is
intelligent speech recognition. In these systems, computers try to detect and
respond to the speeches they are listening to, like humans. In this research,
presenting of a suitable method for the diagnosis of Persian phonemes by AI
using the signal processing and classification algorithms have tried. For this
purpose, the STFT algorithm has been used to process the audio signals, as well
as to detect and classify the signals processed by the deep artificial neural
network. At first, educational samples were provided as two phonological
phrases in Persian language and then signal processing operations were
performed on them. Then the results for the data training have been given to
the artificial deep neural network. At the final stage, the experiment was
conducted on new sounds.
Classical Music Generation in Distinct Dastgahs with AlimNet ACGAN
Authors: Saber Malekzadeh, Maryam Samami, Shahla RezazadehAzar, Maryam Rayegan
Date: 2019-01-15 00:00:00
Tasks: Music Generation

In this paper AlimNet (With respect to great musician, Alim Qasimov) an
auxiliary generative adversarial deep neural network (ACGAN) for generating
music categorically, is used. This proposed network is a conditional ACGAN to
condition the generation process on music tracks which has a hybrid
architecture, composing of different kind of layers of neural networks. The
employed music dataset is MICM which contains 1137 music samples (506 violins
and 631 straw) with seven types of classical music Dastgah labels. To extract
both temporal and spectral features, Short-Time Fourier Transform (STFT) is
applied to convert input audio signals from time domain to time-frequency
domain. GANs are composed of a generator for generating new samples and a
discriminator to help generator making better samples. Samples in
time-frequency domain are used to train discriminator in fourteen classes
(seven Dastgahs and two instruments). The outputs of the conditional ACGAN are
also artificial music samples in those mentioned scales in time-frequency
domain. Then the output of the generator is transformed by Inverse STFT
(ISTFT). Finally, randomly ten generated music samples (five violin and five
straw samples) are given to ten musicians to rate how exact the samples are and
the overall result was 76.5%.
Energy-Efficient Thermal Comfort Control in Smart Buildings via Deep Reinforcement Learning
Authors: Guanyu Gao, Jie Li, Yonggang Wen
Date: 2019-01-15 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Heating, Ventilation, and Air Conditioning (HVAC) is extremely
energy-consuming, accounting for 40% of total building energy consumption.
Therefore, it is crucial to design some energy-efficient building thermal
control policies which can reduce the energy consumption of HVAC while
maintaining the comfort of the occupants. However, implementing such a policy
is challenging, because it involves various influencing factors in a building
environment, which are usually hard to model and may be different from case to
case. To address this challenge, we propose a deep reinforcement learning based
framework for energy optimization and thermal comfort control in smart
buildings. We formulate the building thermal control as a cost-minimization
problem which jointly considers the energy consumption of HVAC and the thermal
comfort of the occupants. To solve the problem, we first adopt a deep neural
network based approach for predicting the occupants' thermal comfort, and then
adopt Deep Deterministic Policy Gradients (DDPG) for learning the thermal
control policy. To evaluate the performance, we implement a building thermal
control simulation system and evaluate the performance under various settings.
The experiment results show that our method can improve the thermal comfort
prediction accuracy, and reduce the energy consumption of HVAC while improving
the occupants' thermal comfort.
URNet : User-Resizable Residual Networks with Conditional Gating Module
Authors: Sang-ho Lee, Simyung Chang, Nojun Kwak
Date: 2019-01-15 00:00:00

Convolutional Neural Networks are widely used to process spatial scenes, but
their computational cost is fixed and depends on the structure of the network
used. There are methods to reduce the cost by compressing networks or varying
its computational path dynamically according to the input image. However, since
a user can not control the size of the learned model, it is difficult to
respond dynamically if the amount of service requests suddenly increases. We
propose User-Resizable Residual Networks (URNet), which allows users to adjust
the scale of the network as needed during evaluation. URNet includes
Conditional Gating Module (CGM) that determines the use of each residual block
according to the input image and the desired scale. CGM is trained in a
supervised manner using the newly proposed scale loss and its corresponding
training methods. URNet can control the amount of computation according to
user's demand without degrading the accuracy significantly. It can also be used
as a general compression method by fixing the scale size during training. In
the experiments on ImageNet, URNet based on ResNet-101 maintains the accuracy
of the baseline even when resizing it to approximately 80% of the original
network, and demonstrates only about 1% accuracy degradation when using about
65% of the computation.