pwc-archive/papers-with-abstracts huggingface dataset:
Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation
Authors: Bohan Zhuang, Chunhua Shen, Mingkui Tan, Lingqiao Liu, Ian Reid
Date: 2018-11-22 00:00:00
Proceeding: CVPR 2019 6
Tasks: General Classification, image-classification, Image Classification, Quantization, Semantic Segmentation

In this paper, we propose to train convolutional neural networks (CNNs) with
both binarized weights and activations, leading to quantized models
specifically} for mobile devices with limited power capacity and computation
resources. Previous works on quantizing CNNs seek to approximate the
floating-point information using a set of discrete values, which we call value
approximation, but typically assume the same architecture as the full-precision
networks. In this paper, however, we take a novel 'structure approximation'
view for quantization---it is very likely that a different architecture may be
better for best performance. In particular, we propose a `network
decomposition' strategy, named \textbf{Group-Net}, in which we divide the
network into groups. In this way, each full-precision group can be effectively
reconstructed by aggregating a set of homogeneous binary branches.
  In addition, we learn effective connections among groups to improve the
representational capability. Moreover, the proposed Group-Net shows strong
generalization to other tasks. For instance, we extend Group-Net for highly
accurate semantic segmentation by embedding rich context into the binary
structure.
  Experiments on both classification and semantic segmentation tasks
demonstrate the superior performance of the proposed methods over various
popular architectures. In particular, we outperform the previous best binary
neural networks in terms of accuracy and major computation savings.
Bytes are All You Need: End-to-End Multilingual Speech Recognition and Synthesis with Bytes
Authors: Bo Li, Yu Zhang, Tara Sainath, Yonghui Wu, William Chan
Date: 2018-11-22 00:00:00
Tasks: All, speech-recognition, Speech Recognition, Speech Synthesis

We present two end-to-end models: Audio-to-Byte (A2B) and Byte-to-Audio
(B2A), for multilingual speech recognition and synthesis. Prior work has
predominantly used characters, sub-words or words as the unit of choice to
model text. These units are difficult to scale to languages with large
vocabularies, particularly in the case of multilingual processing. In this
work, we model text via a sequence of Unicode bytes, specifically, the UTF-8
variable length byte sequence for each character. Bytes allow us to avoid large
softmaxes in languages with large vocabularies, and share representations in
multilingual models. We show that bytes are superior to grapheme characters
over a wide variety of languages in monolingual end-to-end speech recognition.
Additionally, our multilingual byte model outperform each respective single
language baseline on average by 4.4% relatively. In Japanese-English
code-switching speech, our multilingual byte model outperform our monolingual
baseline by 38.6% relatively. Finally, we present an end-to-end multilingual
speech synthesis model using byte representations which matches the performance
of our monolingual baselines.
Towards Emotion Recognition: A Persistent Entropy Application
Authors: R. Gonzalez-Diaz, E. Paluzo-Hidalgo, J. F. Quesada
Date: 2018-11-21 00:00:00
Tasks: Classification, Emotion Classification, Emotion Recognition, General Classification

Emotion recognition and classification is a very active area of research. In
this paper, we present a first approach to emotion classification using
persistent entropy and support vector machines. A topology-based model is
applied to obtain a single real number from each raw signal. These data are
used as input of a support vector machine to classify signals into 8 different
emotions (calm, happy, sad, angry, fearful, disgust and surprised).
How to improve the interpretability of kernel learning
Authors: Jinwei Zhao, Qizhou Wang, YuFei Wang, Yu Liu, Zhenghao Shi, Xinhong Hei
Date: 2018-11-21 00:00:00
Tasks: BIG-bench Machine Learning

In recent years, machine learning researchers have focused on methods to construct flexible and interpretable prediction models. However, an interpretability evaluation, a relationship between generalization performance and an interpretability of the model and a method for improving the interpretability have to be considered. In this paper, a quantitative index of the interpretability is proposed and its rationality is proved, and equilibrium problem between the interpretability and the generalization performance is analyzed. Probability upper bound of the sum of the two performances is analyzed. For traditional supervised kernel machine learning problem, a universal learning framework is put forward to solve the equilibrium problem between the two performances. The condition for global optimal solution based on the framework is deduced. The learning framework is applied to the least-squares support vector machine and is evaluated by some experiments.
Structure-Based Networks for Drug Validation
Authors: Cătălina Cangea, Arturas Grauslys, Pietro Liò, Francesco Falciani
Date: 2018-11-21 00:00:00

Classifying chemicals according to putative modes of action (MOAs) is of
paramount importance in the context of risk assessment. However, current
methods are only able to handle a very small proportion of the existing
chemicals. We address this issue by proposing an integrative deep learning
architecture that learns a joint representation from molecular structures of
drugs and their effects on human cells. Our choice of architecture is motivated
by the significant influence of a drug's chemical structure on its MOA. We
improve on the strong ability of a unimodal architecture (F1 score of 0.803) to
classify drugs by their toxic MOAs (Verhaar scheme) through adding another
learning stream that processes transcriptional responses of human cells
affected by drugs. Our integrative model achieves an even higher classification
performance on the LINCS L1000 dataset - the error is reduced by 4.6%. We
believe that our method can be used to extend the current Verhaar scheme and
constitute a basis for fast drug validation and risk assessment.
Group induced graphical lasso allows for discovery of molecular pathways-pathways interactions
Authors: Veronica Tozzo, Federico Tomasi, Margherita Squillario, Annalisa Barla
Date: 2018-11-21 00:00:00

Complex systems may contain heterogeneous types of variables that interact in
a multi-level and multi-scale manner. In this context, high-level layers may
considered as groups of variables interacting in lower-level layers. This is
particularly true in biology, where, for example, genes are grouped in pathways
and two types of interactions are present: pathway-pathway interactions and
gene-gene interactions. However, from data it is only possible to measure the
expression of genes while it is impossible to directly measure the activity of
pathways. Nevertheless, the knowledge on the inter-dependence between the
groups and the variables allows for a multi-layer network inference, on both
observed variables and groups, even if no direct information on the latter is
present in the data (hence groups are considered as latent). In this paper, we
propose an extension of the latent graphical lasso method that leverages on the
knowledge of the inter-links between the hidden (groups) and observed layers.
The method exploits the knowledge of group structure that influence the
behaviour of observed variables to retrieve a two layers network. Its efficacy
was tested on synthetic data to check its ability in retrieving the network
structure compared to the ground truth. We present a case study on
Neuroblastoma, which shows how our multi-level inference is relevant in real
contexts to infer biologically meaningful connections.
Speech recognition with quaternion neural networks
Authors: Titouan Parcollet, Mirco Ravanelli, Mohamed Morchid, Georges Linarès, Renato de Mori
Date: 2018-11-21 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Neural network architectures are at the core of powerful automatic speech
recognition systems (ASR). However, while recent researches focus on novel
model architectures, the acoustic input features remain almost unchanged.
Traditional ASR systems rely on multidimensional acoustic features such as the
Mel filter bank energies alongside with the first, and second order derivatives
to characterize time-frames that compose the signal sequence. Considering that
these components describe three different views of the same element, neural
networks have to learn both the internal relations that exist within these
features, and external or global dependencies that exist between the
time-frames. Quaternion-valued neural networks (QNN), recently received an
important interest from researchers to process and learn such relations in
multidimensional spaces. Indeed, quaternion numbers and QNNs have shown their
efficiency to process multidimensional inputs as entities, to encode internal
dependencies, and to solve many tasks with up to four times less learning
parameters than real-valued models. We propose to investigate modern
quaternion-valued models such as convolutional and recurrent quaternion neural
networks in the context of speech recognition with the TIMIT dataset. The
experiments show that QNNs always outperform real-valued equivalent models with
way less free parameters, leading to a more efficient, compact, and expressive
representation of the relevant information.
Physics-aware Deep Generative Models for Creating Synthetic Microstructures
Authors: Rahul Singh, Viraj Shah, Balaji Pokuri, Soumik Sarkar, Baskar Ganapathysubramanian, Chinmay Hegde
Date: 2018-11-21 00:00:00
Tasks: Stochastic Optimization

A key problem in computational material science deals with understanding the
effect of material distribution (i.e., microstructure) on material performance.
The challenge is to synthesize microstructures, given a finite number of
microstructure images, and/or some physical invariances that the microstructure
exhibits. Conventional approaches are based on stochastic optimization and are
computationally intensive. We introduce three generative models for the fast
synthesis of binary microstructure images. The first model is a WGAN model that
uses a finite number of training images to synthesize new microstructures that
weakly satisfy the physical invariances respected by the original data. The
second model explicitly enforces known physical invariances by replacing the
traditional discriminator in a GAN with an invariance checker. Our third model
combines the first two models to reconstruct microstructures that respect both
explicit physics invariances as well as implicit constraints learned from the
image data. We illustrate these models by reconstructing two-phase
microstructures that exhibit coarsening behavior. The trained models also
exhibit interesting latent variable interpolation behavior, and the results
indicate considerable promise for enforcing user-defined physics constraints
during microstructure synthesis.
CNN based dense underwater 3D scene reconstruction by transfer learning using bubble database
Authors: Kazuto Ichimaru, Ryo Furukawa, Hiroshi Kawasaki
Date: 2018-11-21 00:00:00
Tasks: 3D Scene Reconstruction, Transfer Learning, Underwater 3D Scene Reconstruction

Dense 3D shape acquisition of swimming human or live fish is an important
research topic for sports, biological science and so on. For this purpose,
active stereo sensor is usually used in the air, however it cannot be applied
to the underwater environment because of refraction, strong light attenuation
and severe interference of bubbles. Passive stereo is a simple solution for
capturing dynamic scenes at underwater environment, however the shape with
textureless surfaces or irregular reflections cannot be recovered. Recently,
the stereo camera pair with a pattern projector for adding artificial textures
on the objects is proposed. However, to use the system for underwater
environment, several problems should be compensated, i.e., disturbance by
fluctuation and bubbles. Simple solution is to use convolutional neural network
for stereo to cancel the effects of bubbles and/or water fluctuation. Since it
is not easy to train CNN with small size of database with large variation, we
develop a special bubble generation device to efficiently create real bubble
database of multiple size and density. In addition, we propose a transfer
learning technique for multi-scale CNN to effectively remove bubbles and
projected-patterns on the object. Further, we develop a real system and
actually captured live swimming human, which has not been done before.
Experiments are conducted to show the effectiveness of our method compared with
the state of the art techniques.
State of the Art in Fair ML: From Moral Philosophy and Legislation to Fair Classifiers
Authors: Elias Baumann, Josef Lorenz Rumberger
Date: 2018-11-20 00:00:00
Tasks: BIG-bench Machine Learning, Philosophy

Machine learning is becoming an ever present part in our lives as many decisions, e.g. to lend a credit, are no longer made by humans but by machine learning algorithms. However those decisions are often unfair and discriminating individuals belonging to protected groups based on race or gender. With the recent General Data Protection Regulation (GDPR) coming into effect, new awareness has been raised for such issues and with computer scientists having such a large impact on peoples lives it is necessary that actions are taken to discover and prevent discrimination. This work aims to give an introduction into discrimination, legislative foundations to counter it and strategies to detect and prevent machine learning algorithms from showing such behavior.
Community detection in networks: A user guide
Authors: Santo Fortunato, Darko Hric
Date: 2016-07-30 00:00:00
Tasks: Community Detection, Misconceptions

Community detection in networks is one of the most popular topics of modern
network science. Communities, or clusters, are usually groups of vertices
having higher probability of being connected to each other than to members of
other groups, though other patterns are possible. Identifying communities is an
ill-defined problem. There are no universal protocols on the fundamental
ingredients, like the definition of community itself, nor on other crucial
issues, like the validation of algorithms and the comparison of their
performances. This has generated a number of confusions and misconceptions,
which undermine the progress in the field. We offer a guided tour through the
main aspects of the problem. We also point out strengths and weaknesses of
popular methods, and give directions to their use.
Deformable ConvNets v2: More Deformable, Better Results
Authors: Xizhou Zhu, Han Hu, Stephen Lin, Jifeng Dai
Date: 2018-11-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Instance Segmentation, Object, Object Detection, Semantic Segmentation

The superior performance of Deformable Convolutional Networks arises from its
ability to adapt to the geometric variations of objects. Through an examination
of its adaptive behavior, we observe that while the spatial support for its
neural features conforms more closely than regular ConvNets to object
structure, this support may nevertheless extend well beyond the region of
interest, causing features to be influenced by irrelevant image content. To
address this problem, we present a reformulation of Deformable ConvNets that
improves its ability to focus on pertinent image regions, through increased
modeling power and stronger training. The modeling power is enhanced through a
more comprehensive integration of deformable convolution within the network,
and by introducing a modulation mechanism that expands the scope of deformation
modeling. To effectively harness this enriched modeling capability, we guide
network training via a proposed feature mimicking scheme that helps the network
to learn features that reflect the object focus and classification power of
R-CNN features. With the proposed contributions, this new version of Deformable
ConvNets yields significant performance gains over the original model and
produces leading results on the COCO benchmark for object detection and
instance segmentation.
Integrated Object Detection and Tracking with Tracklet-Conditioned Detection
Authors: Zheng Zhang, Dazhi Cheng, Xizhou Zhu, Stephen Lin, Jifeng Dai
Date: 2018-11-27 00:00:00
Tasks: Object, object-detection, Object Detection, Video Object Detection, Video Understanding

Accurate detection and tracking of objects is vital for effective video
understanding. In previous work, the two tasks have been combined in a way that
tracking is based heavily on detection, but the detection benefits marginally
from the tracking. To increase synergy, we propose to more tightly integrate
the tasks by conditioning the object detection in the current frame on
tracklets computed in prior frames. With this approach, the object detection
results not only have high detection responses, but also improved coherence
with the existing tracklets. This greater coherence leads to estimated object
trajectories that are smoother and more stable than the jittered paths obtained
without tracklet-conditioned detection. Over extensive experiments, this
approach is shown to achieve state-of-the-art performance in terms of both
detection and tracking accuracy, as well as noticeable improvements in tracking
stability.
Label-Noise Robust Generative Adversarial Networks
Authors: Takuhiro Kaneko, Yoshitaka Ushiku, Tatsuya Harada
Date: 2018-11-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Robust classification

Generative adversarial networks (GANs) are a framework that learns a generative distribution through adversarial training. Recently, their class-conditional extensions (e.g., conditional GAN (cGAN) and auxiliary classifier GAN (AC-GAN)) have attracted much attention owing to their ability to learn the disentangled representations and to improve the training stability. However, their training requires the availability of large-scale accurate class-labeled data, which are often laborious or impractical to collect in a real-world scenario. To remedy this, we propose a novel family of GANs called label-noise robust GANs (rGANs), which, by incorporating a noise transition model, can learn a clean label conditional generative distribution even when training labels are noisy. In particular, we propose two variants: rAC-GAN, which is a bridging model between AC-GAN and the label-noise robust classification model, and rcGAN, which is an extension of cGAN and solves this problem with no reliance on any classifier. In addition to providing the theoretical background, we demonstrate the effectiveness of our models through extensive experiments using diverse GAN configurations, various noise settings, and multiple evaluation metrics (in which we tested 402 conditions in total). Our code is available at https://github.com/takuhirok/rGAN/.
Class-Distinct and Class-Mutual Image Generation with GANs
Authors: Takuhiro Kaneko, Yoshitaka Ushiku, Tatsuya Harada
Date: 2018-11-27 00:00:00
Tasks: Conditional Image Generation, Image Generation, Image-to-Image Translation, Specificity

Class-conditional extensions of generative adversarial networks (GANs), such as auxiliary classifier GAN (AC-GAN) and conditional GAN (cGAN), have garnered attention owing to their ability to decompose representations into class labels and other factors and to boost the training stability. However, a limitation is that they assume that each class is separable and ignore the relationship between classes even though class overlapping frequently occurs in a real-world scenario when data are collected on the basis of diverse or ambiguous criteria. To overcome this limitation, we address a novel problem called class-distinct and class-mutual image generation, in which the goal is to construct a generator that can capture between-class relationships and generate an image selectively conditioned on the class specificity. To solve this problem without additional supervision, we propose classifier's posterior GAN (CP-GAN), in which we redesign the generator input and the objective function of AC-GAN for class-overlapping data. Precisely, we incorporate the classifier's posterior into the generator input and optimize the generator so that the classifier's posterior of generated data corresponds with that of real data. We demonstrate the effectiveness of CP-GAN using both controlled and real-world class-overlapping data with a model configuration analysis and comparative study. Our code is available at https://github.com/takuhirok/CP-GAN/.
Cross-Lingual Approaches to Reference Resolution in Dialogue Systems
Authors: Amr Sharaf, Arpit Gupta, Hancheng Ge, Chetan Naik, Lambert Mathias
Date: 2018-11-27 00:00:00
Tasks: Cross-Lingual Transfer, Data Augmentation, Machine Translation, slot-filling, Slot Filling, Translation

In the slot-filling paradigm, where a user can refer back to slots in the
context during the conversation, the goal of the contextual understanding
system is to resolve the referring expressions to the appropriate slots in the
context. In this paper, we build on the context carryover
system~\citep{Naik2018ContextualSC}, which provides a scalable multi-domain
framework for resolving references. However, scaling this approach across
languages is not a trivial task, due to the large demand on acquisition of
annotated data in the target language. Our main focus is on cross-lingual
methods for reference resolution as a way to alleviate the need for annotated
data in the target language. In the cross-lingual setup, we assume there is
access to annotated resources as well as a well trained model in the source
language and little to no annotated data in the target language. In this paper,
we explore three different approaches for cross-lingual transfer \textemdash~\
delexicalization as data augmentation, multilingual embeddings and machine
translation. We compare these approaches both on a low resource setting as well
as a large resource setting. Our experiments show that multilingual embeddings
and delexicalization via data augmentation have a significant impact in the low
resource setting, but the gains diminish as the amount of available data in the
target language increases. Furthermore, when combined with machine translation
we can get performance very close to actual live data in the target language,
with only 25\% of the data projected into the target language.
FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery
Authors: Krishna Kumar Singh, Utkarsh Ojha, Yong Jae Lee
Date: 2018-11-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Conditional Image Generation, Disentanglement, Fine-Grained Visual Categorization, Image Clustering, Object

We propose FineGAN, a novel unsupervised GAN framework, which disentangles
the background, object shape, and object appearance to hierarchically generate
images of fine-grained object categories. To disentangle the factors without
supervision, our key idea is to use information theory to associate each factor
to a latent code, and to condition the relationships between the codes in a
specific way to induce the desired hierarchy. Through extensive experiments, we
show that FineGAN achieves the desired disentanglement to generate realistic
and diverse images belonging to fine-grained classes of birds, dogs, and cars.
Using FineGAN's automatically learned features, we also cluster real images as
a first attempt at solving the novel problem of unsupervised fine-grained
object category discovery. Our code/models/demo can be found at
https://github.com/kkanshul/finegan