pwc-archive/papers-with-abstracts huggingface dataset:
The Matrix Calculus You Need For Deep Learning
Authors: Terence Parr, Jeremy Howard
Date: 2018-02-05 00:00:00
Tasks: Articles, Deep Learning, Math

This paper is an attempt to explain all the matrix calculus you need in order
to understand the training of deep neural networks. We assume no math knowledge
beyond what you learned in calculus 1, and provide links to help you refresh
the necessary math where needed. Note that you do not need to understand this
material before you start learning to train and use deep learning in practice;
rather, this material is for those who are already familiar with the basics of
neural networks, and wish to deepen their understanding of the underlying math.
Don't worry if you get stuck at some point along the way---just go back and
reread the previous section, and try writing down and working through some
examples. And if you're still stuck, we're happy to answer your questions in
the Theory category at forums.fast.ai. Note: There is a reference section at
the end of the paper summarizing all the key matrix calculus rules and
terminology discussed here. See related articles at http://explained.ai
Exposure: A White-Box Photo Post-Processing Framework
Authors: Yuanming Hu, Hao He, Chenxi Xu, Baoyuan Wang, Stephen Lin
Date: 2017-09-27 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

Retouching can significantly elevate the visual appeal of photos, but many
casual photographers lack the expertise to do this well. To address this
problem, previous works have proposed automatic retouching systems based on
supervised learning from paired training images acquired before and after
manual editing. As it is difficult for users to acquire paired images that
reflect their retouching preferences, we present in this paper a deep learning
approach that is instead trained on unpaired data, namely a set of photographs
that exhibits a retouching style the user likes, which is much easier to
collect. Our system is formulated using deep convolutional neural networks that
learn to apply different retouching operations on an input image. Network
training with respect to various types of edits is enabled by modeling these
retouching operations in a unified manner as resolution-independent
differentiable filters. To apply the filters in a proper sequence and with
suitable parameters, we employ a deep reinforcement learning approach that
learns to make decisions on what action to take next, given the current state
of the image. In contrast to many deep learning systems, ours provides users
with an understandable solution in the form of conventional retouching edits,
rather than just a "black-box" result. Through quantitative comparisons and
user studies, we show that this technique generates retouching results
consistent with the provided photo set.
Système de traduction automatique statistique Anglais-Arabe
Authors: Marwa Hadj Salah, Didier Schwab, Hervé Blanchon, Mounir Zrigui
Date: 2018-02-06 00:00:00
Tasks: Machine Translation, Translation

Machine translation (MT) is the process of translating text written in a
source language into text in a target language. In this article, we present our
English-Arabic statistical machine translation system. First, we present the
general process for setting up a statistical machine translation system, then
we describe the tools as well as the different corpora we used to build our MT
system. Our system was evaluated in terms of the BLUE score (24.51%)
Compact Hash Code Learning with Binary Deep Neural Network
Authors: Thanh-Toan Do, Tuan Hoang, Dang-Khoa Le Tan, Anh-Dzung Doan, Ngai-Man Cheung
Date: 2017-12-08 00:00:00
Tasks: Binarization, Deep Hashing, Image Retrieval, Retrieval

Learning compact binary codes for image retrieval problem using deep neural networks has recently attracted increasing attention. However, training deep hashing networks is challenging due to the binary constraints on the hash codes. In this paper, we propose deep network models and learning algorithms for learning binary hash codes given image representations under both unsupervised and supervised manners. The novelty of our network design is that we constrain one hidden layer to directly output the binary codes. This design has overcome a challenging problem in some previous works: optimizing non-smooth objective functions because of binarization. In addition, we propose to incorporate independence and balance properties in the direct and strict forms into the learning schemes. We also include a similarity preserving property in our objective functions. The resulting optimizations involving these binary, independence, and balance constraints are difficult to solve. To tackle this difficulty, we propose to learn the networks with alternating optimization and careful relaxation. Furthermore, by leveraging the powerful capacity of convolutional neural networks, we propose an end-to-end architecture that jointly learns to extract visual features and produce binary hash codes. Experimental results for the benchmark datasets show that the proposed methods compare favorably or outperform the state of the art.
Multispectral Compressive Imaging Strategies using Fabry-Pérot Filtered Sensors
Authors: Kévin Degraux, Valerio Cambareri, Bert Geelen, Laurent Jacques, Gauthier Lafruit
Date: 2018-02-06 00:00:00
Tasks: compressed sensing, Super-Resolution

This paper introduces two acquisition device architectures for multispectral
compressive imaging. Unlike most existing methods, the proposed computational
imaging techniques do not include any dispersive element, as they use a
dedicated sensor which integrates narrowband Fabry-P\'erot spectral filters at
the pixel level. The first scheme leverages joint inpainting and
super-resolution to fill in those voxels that are missing due to the device's
limited pixel count. The second scheme, in link with compressed sensing,
introduces spatial random convolutions, but is more complex and may be affected
by diffraction. In both cases we solve the associated inverse problems by using
the same signal prior. Specifically, we propose a redundant analysis signal
prior in a convex formulation. Through numerical simulations, we explore
different realistic setups. Our objective is also to highlight some practical
guidelines and discuss their complexity trade-offs to integrate these schemes
into actual computational imaging systems. Our conclusion is that the second
technique performs best at high compression levels, in a properly sized and
calibrated setup. Otherwise, the first, simpler technique should be favored.
Improving Variational Encoder-Decoders in Dialogue Generation
Authors: Xiaoyu Shen, Hui Su, Shuzi Niu, Vera Demberg
Date: 2018-02-06 00:00:00
Tasks: Dialogue Generation

Variational encoder-decoders (VEDs) have shown promising results in dialogue
generation. However, the latent variable distributions are usually approximated
by a much simpler model than the powerful RNN structure used for encoding and
decoding, yielding the KL-vanishing problem and inconsistent training
objective. In this paper, we separate the training step into two phases: The
first phase learns to autoencode discrete texts into continuous embeddings,
from which the second phase learns to generalize latent representations by
reconstructing the encoded embedding. In this case, latent variables are
sampled by transforming Gaussian noise through multi-layer perceptrons and are
trained with a separate VED model, which has the potential of realizing a much
more flexible distribution. We compare our model with current popular models
and the experiment demonstrates substantial improvement in both metric-based
and human evaluations.
Brain-inspired photonic signal processor for periodic pattern generation and chaotic system emulation
Authors: Piotr Antonik, Marc Haelterman, Serge Massar
Date: 2018-02-06 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

Reservoir computing is a bio-inspired computing paradigm for processing
time-dependent signals. Its hardware implementations have received much
attention because of their simplicity and remarkable performance on a series of
benchmark tasks. In previous experiments the output was uncoupled from the
system and in most cases simply computed offline on a post-processing computer.
However, numerical investigations have shown that feeding the output back into
the reservoir would open the possibility of long-horizon time series
forecasting. Here we present a photonic reservoir computer with output
feedback, and demonstrate its capacity to generate periodic time series and to
emulate chaotic systems. We study in detail the effect of experimental noise on
system performance. In the case of chaotic systems, this leads us to introduce
several metrics, based on standard signal processing techniques, to evaluate
the quality of the emulation. Our work significantly enlarges the range of
tasks that can be solved by hardware reservoir computers, and therefore the
range of applications they could potentially tackle. It also raises novel
questions in nonlinear dynamics and chaos theory.
DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision
Authors: Hanyuan Zhang, Hao Wu, Weiwei Sun, Baihua Zheng
Date: 2018-02-06 00:00:00
Tasks: Travel Time Estimation

Estimating the travel time of a path is of great importance to smart urban
mobility. Existing approaches are either based on estimating the time cost of
each road segment which are not able to capture many cross-segment complex
factors, or designed heuristically in a non-learning-based way which fail to
utilize the existing abundant temporal labels of the data, i.e., the time stamp
of each trajectory point. In this paper, we leverage on new development of deep
neural networks and propose a novel auxiliary supervision model, namely
DeepTravel, that can automatically and effectively extract different features,
as well as make full use of the temporal labels of the trajectory data. We have
conducted comprehensive experiments on real datasets to demonstrate the
out-performance of DeepTravel over existing approaches.
Orthogonally Regularized Deep Networks For Image Super-resolution
Authors: Tiantong Guo, Hojjat S. Mousavi, Vishal Monga
Date: 2018-02-06 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Deep learning methods, in particular trained Convolutional Neural Networks
(CNNs) have recently been shown to produce compelling state-of-the-art results
for single image Super-Resolution (SR). Invariably, a CNN is learned to map the
low resolution (LR) image to its corresponding high resolution (HR) version in
the spatial domain. Aiming for faster inference and more efficient solutions
than solving the SR problem in the spatial domain, we propose a novel network
structure for learning the SR mapping function in an image transform domain,
specifically the Discrete Cosine Transform (DCT). As a first contribution, we
show that DCT can be integrated into the network structure as a Convolutional
DCT (CDCT) layer. We further extend the network to allow the CDCT layer to
become trainable (i.e. optimizable). Because this layer represents an image
transform, we enforce pairwise orthogonality constraints on the individual
basis functions/filters. This Orthogonally Regularized Deep SR network (ORDSR)
simplifies the SR task by taking advantage of image transform domain while
adapting the design of transform basis to the training image set.
Machine Translation of Low-Resource Spoken Dialects: Strategies for Normalizing Swiss German
Authors: Pierre-Edouard Honnet, Andrei Popescu-Belis, Claudiu Musat, Michael Baeriswyl
Date: 2017-10-30 00:00:00
Proceeding: LREC 2018 5
Tasks: Diversity, Machine Translation, Text Normalization, Translation

The goal of this work is to design a machine translation (MT) system for a
low-resource family of dialects, collectively known as Swiss German, which are
widely spoken in Switzerland but seldom written. We collected a significant
number of parallel written resources to start with, up to a total of about 60k
words. Moreover, we identified several other promising data sources for Swiss
German. Then, we designed and compared three strategies for normalizing Swiss
German input in order to address the regional diversity. We found that
character-based neural MT was the best solution for text normalization. In
combination with phrase-based statistical MT, our solution reached 36% BLEU
score when translating from the Bernese dialect. This value, however, decreases
as the testing data becomes more remote from the training one, geographically
and topically. These resources and normalization techniques are a first step
towards full MT of Swiss German dialects.
Multimodal Image Captioning for Marketing Analysis
Authors: Philipp Harzig, Stephan Brehm, Rainer Lienhart, Carolin Kaiser, René Schallner
Date: 2018-02-06 00:00:00
Tasks: Image Captioning, Marketing

Automatically captioning images with natural language sentences is an important research topic. State of the art models are able to produce human-like sentences. These models typically describe the depicted scene as a whole and do not target specific objects of interest or emotional relationships between these objects in the image. However, marketing companies require to describe these important attributes of a given scene. In our case, objects of interest are consumer goods, which are usually identifiable by a product logo and are associated with certain brands. From a marketing point of view, it is desirable to also evaluate the emotional context of a trademarked product, i.e., whether it appears in a positive or a negative connotation. We address the problem of finding brands in images and deriving corresponding captions by introducing a modified image captioning network. We also add a third output modality, which simultaneously produces real-valued image ratings. Our network is trained using a classification-aware loss function in order to stimulate the generation of sentences with an emphasis on words identifying the brand of a product. We evaluate our model on a dataset of images depicting interactions between humans and branded products. The introduced network improves mean class accuracy by 24.5 percent. Thanks to adding the third output modality, it also considerably improves the quality of generated captions for images depicting branded products.
Attribute-Guided Network for Cross-Modal Zero-Shot Hashing
Authors: Zhong Ji, Yuxin Sun, Yunlong Yu, Yanwei Pang, Jungong Han
Date: 2018-02-06 00:00:00
Tasks: Attribute, Cross-Modal Retrieval, Image Retrieval, Retrieval, Zero-shot Image Retrieval

Zero-Shot Hashing aims at learning a hashing model that is trained only by
instances from seen categories but can generate well to those of unseen
categories. Typically, it is achieved by utilizing a semantic embedding space
to transfer knowledge from seen domain to unseen domain. Existing efforts
mainly focus on single-modal retrieval task, especially Image-Based Image
Retrieval (IBIR). However, as a highlighted research topic in the field of
hashing, cross-modal retrieval is more common in real world applications. To
address the Cross-Modal Zero-Shot Hashing (CMZSH) retrieval task, we propose a
novel Attribute-Guided Network (AgNet), which can perform not only IBIR, but
also Text-Based Image Retrieval (TBIR). In particular, AgNet aligns different
modal data into a semantically rich attribute space, which bridges the gap
caused by modality heterogeneity and zero-shot setting. We also design an
effective strategy that exploits the attribute to guide the generation of hash
codes for image and text within the same network. Extensive experimental
results on three benchmark datasets (AwA, SUN, and ImageNet) demonstrate the
superiority of AgNet on both cross-modal and single-modal zero-shot image
retrieval tasks.
Tunneling Neural Perception and Logic Reasoning through Abductive Learning
Authors: Wang-Zhou Dai, Qiu-Ling Xu, Yang Yu, Zhi-Hua Zhou
Date: 2018-02-04 00:00:00

Perception and reasoning are basic human abilities that are seamlessly
connected as part of human intelligence. However, in current machine learning
systems, the perception and reasoning modules are incompatible. Tasks requiring
joint perception and reasoning ability are difficult to accomplish autonomously
and still demand human intervention. Inspired by the way language experts
decoded Mayan scripts by joining two abilities in an abductive manner, this
paper proposes the abductive learning framework. The framework learns
perception and reasoning simultaneously with the help of a trial-and-error
abductive process. We present the Neural-Logical Machine as an implementation
of this novel learning framework. We demonstrate that--using human-like
abductive learning--the machine learns from a small set of simple hand-written
equations and then generalizes well to complex equations, a feat that is beyond
the capability of state-of-the-art neural network models. The abductive
learning framework explores a new direction for approaching human-level
learning ability.
Deep Inference of Personality Traits by Integrating Image and Word Use in Social Networks
Authors: Guillem Cucurull, Pau Rodríguez, V. Oguz Yazici, Josep M. Gonfaus, F. Xavier Roca, Jordi Gonzàlez
Date: 2018-02-06 00:00:00

Social media, as a major platform for communication and information exchange,
is a rich repository of the opinions and sentiments of 2.3 billion users about
a vast spectrum of topics. To sense the whys of certain social user's demands
and cultural-driven interests, however, the knowledge embedded in the 1.8
billion pictures which are uploaded daily in public profiles has just started
to be exploited since this process has been typically been text-based.
Following this trend on visual-based social analysis, we present a novel
methodology based on Deep Learning to build a combined image-and-text based
personality trait model, trained with images posted together with words found
highly correlated to specific personality traits. So the key contribution here
is to explore whether OCEAN personality trait modeling can be addressed based
on images, here called \emph{Mind{P}ics}, appearing with certain tags with
psychological insights. We found that there is a correlation between those
posted images and their accompanying texts, which can be successfully modeled
using deep neural networks for personality estimation. The experimental results
are consistent with previous cyber-psychology results based on texts or images.
In addition, classification results on some traits show that some patterns
emerge in the set of images corresponding to a specific text, in essence to
those representing an abstract concept. These results open new avenues of
research for further refining the proposed personality model under the
supervision of psychology experts.
The steerable graph Laplacian and its application to filtering image data-sets
Authors: Boris Landa, Yoel Shkolnisky
Date: 2018-02-06 00:00:00

In recent years, improvements in various image acquisition techniques gave
rise to the need for adaptive processing methods, aimed particularly for large
datasets corrupted by noise and deformations. In this work, we consider
datasets of images sampled from a low-dimensional manifold (i.e. an
image-valued manifold), where the images can assume arbitrary planar rotations.
To derive an adaptive and rotation-invariant framework for processing such
datasets, we introduce a graph Laplacian (GL)-like operator over the dataset,
termed ${\textit{steerable graph Laplacian}}$. Essentially, the steerable GL
extends the standard GL by accounting for all (infinitely-many) planar
rotations of all images. As it turns out, similarly to the standard GL, a
properly normalized steerable GL converges to the Laplace-Beltrami operator on
the low-dimensional manifold. However, the steerable GL admits an improved
convergence rate compared to the GL, where the improved convergence behaves as
if the intrinsic dimension of the underlying manifold is lower by one.
Moreover, it is shown that the steerable GL admits eigenfunctions of the form
of Fourier modes (along the orbits of the images' rotations) multiplied by
eigenvectors of certain matrices, which can be computed efficiently by the FFT.
For image datasets corrupted by noise, we employ a subset of these
eigenfunctions to "filter" the dataset via a Fourier-like filtering scheme,
essentially using all images and their rotations simultaneously. We demonstrate
our filtering framework by de-noising simulated single-particle cryo-EM image
datasets.
Texygen: A Benchmarking Platform for Text Generation Models
Authors: Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Wei-Nan Zhang, Jun Wang, Yong Yu
Date: 2018-02-06 00:00:00
Tasks: Benchmarking, Diversity, Text Generation

We introduce Texygen, a benchmarking platform to support research on
open-domain text generation models. Texygen has not only implemented a majority
of text generation models, but also covered a set of metrics that evaluate the
diversity, the quality and the consistency of the generated texts. The Texygen
platform could help standardize the research on text generation and facilitate
the sharing of fine-tuned open-source implementations among researchers for
their work. As a consequence, this would help in improving the reproductivity
and reliability of future research work in text generation.
A Benchmark Environment Motivated by Industrial Control Problems
Authors: Daniel Hein, Stefan Depeweg, Michel Tokic, Steffen Udluft, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing
Date: 2017-09-27 00:00:00
Tasks: OpenAI Gym, Reinforcement Learning, Reinforcement Learning (RL)

In the research area of reinforcement learning (RL), frequently novel and promising methods are developed and introduced to the RL community. However, although many researchers are keen to apply their methods on real-world problems, implementing such methods in real industry environments often is a frustrating and tedious process. Generally, academic research groups have only limited access to real industrial data and applications. For this reason, new methods are usually developed, evaluated and compared by using artificial software benchmarks. On one hand, these benchmarks are designed to provide interpretable RL training scenarios and detailed insight into the learning process of the method on hand. On the other hand, they usually do not share much similarity with industrial real-world applications. For this reason we used our industry experience to design a benchmark which bridges the gap between freely available, documented, and motivated artificial benchmarks and properties of real industrial problems. The resulting industrial benchmark (IB) has been made publicly available to the RL community by publishing its Java and Python code, including an OpenAI Gym wrapper, on Github. In this paper we motivate and describe in detail the IB's dynamics and identify prototypic experimental settings that capture common situations in real-world industry control problems.