pwc-archive/papers-with-abstracts huggingface dataset:
An Application-Specific VLIW Processor with Vector Instruction Set for CNN Acceleration
Authors: Andreas Bytyn, Rainer Leupers, Gerd Ascheid
Date: 2019-04-10 00:00:00
Tasks: Object Recognition

In recent years, neural networks have surpassed classical algorithms in areas
such as object recognition, e.g. in the well-known ImageNet challenge. As a
result, great effort is being put into developing fast and efficient
accelerators, especially for Convolutional Neural Networks (CNNs). In this work
we present ConvAix, a fully C-programmable processor, which -- contrary to many
existing architectures -- does not rely on a hard-wired array of
multiply-and-accumulate (MAC) units. Instead it maps computations onto
independent vector lanes making use of a carefully designed vector instruction
set. The presented processor is targeted towards latency-sensitive applications
and is capable of executing up to 192 MAC operations per cycle. ConvAix
operates at a target clock frequency of 400 MHz in 28nm CMOS, thereby offering
state-of-the-art performance with proper flexibility within its target domain.
Simulation results for several 2D convolutional layers from well known CNNs
(AlexNet, VGG-16) show an average ALU utilization of 72.5% using vector
instructions with 16 bit fixed-point arithmetic. Compared to other well-known
designs which are less flexible, ConvAix offers competitive energy efficiency
of up to 497 GOP/s/W while even surpassing them in terms of area efficiency and
processing speed.
Knowledge Squeezed Adversarial Network Compression
Authors: Shu Changyong, Li Peng, Xie Yuan, Qu Yanyun, Dai Longquan, Ma Lizhuang
Date: 2019-04-10 00:00:00
Tasks: Knowledge Distillation, Transfer Learning

Deep network compression has been achieved notable progress via knowledge
distillation, where a teacher-student learning manner is adopted by using
predetermined loss. Recently, more focuses have been transferred to employ the
adversarial training to minimize the discrepancy between distributions of
output from two networks. However, they always emphasize on result-oriented
learning while neglecting the scheme of process-oriented learning, leading to
the loss of rich information contained in the whole network pipeline. Inspired
by the assumption that, the small network can not perfectly mimic a large one
due to the huge gap of network scale, we propose a knowledge transfer method,
involving effective intermediate supervision, under the adversarial training
framework to learn the student network. To achieve powerful but highly compact
intermediate information representation, the squeezed knowledge is realized by
task-driven attention mechanism. Then, the transferred knowledge from teacher
network could accommodate the size of student network. As a result, the
proposed method integrates merits from both process-oriented and
result-oriented learning. Extensive experimental results on three typical
benchmark datasets, i.e., CIFAR-10, CIFAR-100, and ImageNet, demonstrate that
our method achieves highly superior performances against other state-of-the-art
methods.
Multitask Hopfield Networks
Authors: Marco Frasca, Giuliano Grossi, Giorgio Valentini
Date: 2019-04-10 00:00:00
Tasks: General Classification

Multitask algorithms typically use task similarity information as a bias to
speed up and improve the performance of learning processes. Tasks are learned
jointly, sharing information across them, in order to construct models more
accurate than those learned separately over single tasks. In this contribution,
we present the first multitask model, to our knowledge, based on Hopfield
Networks (HNs), named HoMTask. We show that by appropriately building a unique
HN embedding all tasks, a more robust and effective classification model can be
learned. HoMTask is a transductive semi-supervised parametric HN, that
minimizes an energy function extended to all nodes and to all tasks under
study. We provide theoretical evidence that the optimal parameters
automatically estimated by HoMTask make coherent the model itself with the
prior knowledge (connection weights and node labels). The convergence
properties of HNs are preserved, and the fixed point reached by the network
dynamics gives rise to the prediction of unlabeled nodes. The proposed model
improves the classification abilities of singletask HNs on a preliminary
benchmark comparison, and achieves competitive performance with
state-of-the-art semi-supervised graph-based algorithms.
Cross-lingual Visual Verb Sense Disambiguation
Authors: Spandana Gella, Desmond Elliott, Frank Keller
Date: 2019-04-10 00:00:00
Proceeding: NAACL 2019 6
Tasks: Machine Translation, Translation

Recent work has shown that visual context improves cross-lingual sense
disambiguation for nouns. We extend this line of work to the more challenging
task of cross-lingual verb sense disambiguation, introducing the MultiSense
dataset of 9,504 images annotated with English, German, and Spanish verbs. Each
image in MultiSense is annotated with an English verb and its translation in
German or Spanish. We show that cross-lingual verb sense disambiguation models
benefit from visual context, compared to unimodal baselines. We also show that
the verb sense predicted by our best disambiguation model can improve the
results of a text-only machine translation system when used for a multimodal
translation task.
A Framework for Multi-f0 Modeling in SATB Choir Recordings
Authors: Helena Cuesta, Emilia Gómez, Pritish Chandna
Date: 2019-04-10 00:00:00

Fundamental frequency (f0) modeling is an important but relatively unexplored
aspect of choir singing. Performance evaluation as well as auditory analysis of
singing, whether individually or in a choir, often depend on extracting f0
contours for the singing voice. However, due to the large number of singers,
singing at a similar frequency range, extracting the exact individual pitch
contours from choir recordings is a challenging task. In this paper, we address
this task and develop a methodology for modeling pitch contours of SATB choir
recordings. A typical SATB choir consists of four parts, each covering a
distinct range of pitches and often with multiple singers each. We first
evaluate some state-of-the-art multi-f0 estimation systems for the particular
case of choirs with a single singer per part, and observe that the pitch of
individual singers can be estimated to a relatively high degree of accuracy. We
observe, however, that the scenario of multiple singers for each choir part
(i.e. unison singing) is far more challenging. In this work we propose a
methodology based on combining a multi-f0 estimation methodology based on deep
learning followed by a set of traditional DSP techniques to model f0 and its
dispersion instead of a single f0 trajectory for each choir part. We present
and discuss our observations and test our framework with different singer
configurations.
From Semi-supervised to Almost-unsupervised Speech Recognition with Very-low Resource by Jointly Learning Phonetic Structures from Audio and Text Embeddings
Authors: Yi-Chen Chen, Sung-Feng Huang, Hung-Yi Lee, Lin-shan Lee
Date: 2019-04-10 00:00:00
Tasks: speech-recognition, Speech Recognition, Unsupervised Speech Recognition

Producing a large amount of annotated speech data for training ASR systems
remains difficult for more than 95% of languages all over the world which are
low-resourced. However, we note human babies start to learn the language by the
sounds (or phonetic structures) of a small number of exemplar words, and
"generalize" such knowledge to other words without hearing a large amount of
data. We initiate some preliminary work in this direction. Audio Word2Vec is
used to learn the phonetic structures from spoken words (signal segments),
while another autoencoder is used to learn the phonetic structures from text
words. The relationships among the above two can be learned jointly, or
separately after the above two are well trained. This relationship can be used
in speech recognition with very low resource. In the initial experiments on the
TIMIT dataset, only 2.1 hours of speech data (in which 2500 spoken words were
annotated and the rest unlabeled) gave a word error rate of 44.6%, and this
number can be reduced to 34.2% if 4.1 hr of speech data (in which 20000 spoken
words were annotated) were given. These results are not satisfactory, but a
good starting point.
Neuralogram: A Deep Neural Network Based Representation for Audio Signals
Authors: Prateek Verma, Chris Chafe, Jonathan Berger
Date: 2019-04-10 00:00:00
Tasks: Music Recommendation, Rhythm

We propose the Neuralogram -- a deep neural network based representation for
understanding audio signals which, as the name suggests, transforms an audio
signal to a dense, compact representation based upon embeddings learned via a
neural architecture. Through a series of probing signals, we show how our
representation can encapsulate pitch, timbre and rhythm-based information, and
other attributes. This representation suggests a method for revealing
meaningful relationships in arbitrarily long audio signals that are not readily
represented by existing algorithms. This has the potential for numerous
applications in audio understanding, music recommendation, meta-data extraction
to name a few.
Differential Dynamic Programming for Multi-Phase Rigid Contact Dynamics
Authors: Rohan Budhiraja, Justin Carpentier, Carlos Mastalli, Nicolas Mansard
Date: 2019-04-10 00:00:00

A common strategy today to generate efficient locomotion movements is to
split the problem into two consecutive steps: the first one generates the
contact sequence together with the centroidal trajectory, while the second one
computes the whole-body trajectory that follows the centroidal pattern. Yet the
second step is generally handled by a simple program such as an inverse
kinematics solver. In contrast, we propose to compute the whole-body trajectory
by using a local optimal control solver, namely Differential Dynamic
Programming (DDP). Our method produces more efficient motions, with lower
forces and smaller impacts, by exploiting the Angular Momentum (AM). With this
aim, we propose an original DDP formulation exploiting the Karush-Kuhn-Tucker
constraint of the rigid contact model. We experimentally show the importance of
this approach by executing large steps walking on the real HRP-2 robot, and by
solving the problem of attitude control under the absence of external forces.
Relational Knowledge Distillation
Authors: Wonpyo Park, Dongju Kim, Yan Lu, Minsu Cho
Date: 2019-04-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Knowledge Distillation, Metric Learning

Knowledge distillation aims at transferring knowledge acquired in one model
(a teacher) to another model (a student) that is typically smaller. Previous
approaches can be expressed as a form of training the student to mimic output
activations of individual data examples represented by the teacher. We
introduce a novel approach, dubbed relational knowledge distillation (RKD),
that transfers mutual relations of data examples instead. For concrete
realizations of RKD, we propose distance-wise and angle-wise distillation
losses that penalize structural differences in relations. Experiments conducted
on different tasks show that the proposed method improves educated student
models with a significant margin. In particular for metric learning, it allows
students to outperform their teachers' performance, achieving the state of the
arts on standard benchmark datasets.
DAVANet: Stereo Deblurring with View Aggregation
Authors: Shangchen Zhou, Jiawei Zhang, WangMeng Zuo, Haozhe Xie, Jinshan Pan, Jimmy Ren
Date: 2019-04-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Deblurring, Image Deblurring

Nowadays stereo cameras are more commonly adopted in emerging devices such as
dual-lens smartphones and unmanned aerial vehicles. However, they also suffer
from blurry images in dynamic scenes which leads to visual discomfort and
hampers further image processing. Previous works have succeeded in monocular
deblurring, yet there are few studies on deblurring for stereoscopic images. By
exploiting the two-view nature of stereo images, we propose a novel stereo
image deblurring network with Depth Awareness and View Aggregation, named
DAVANet. In our proposed network, 3D scene cues from the depth and varying
information from two views are incorporated, which help to remove complex
spatially-varying blur in dynamic scenes. Specifically, with our proposed
fusion network, we integrate the bidirectional disparities estimation and
deblurring into a unified framework. Moreover, we present a large-scale
multi-scene dataset for stereo deblurring, containing 20,637 blurry-sharp
stereo image pairs from 135 diverse sequences and their corresponding
bidirectional disparities. The experimental results on our dataset demonstrate
that DAVANet outperforms state-of-the-art methods in terms of accuracy, speed,
and model size.
A review on Neural Turing Machine
Authors: Soroor Malekmohammadi Faradonbeh, Faramarz Safi-Esfahani
Date: 2019-04-10 00:00:00
Tasks: BIG-bench Machine Learning

One of the major objectives of Artificial Intelligence is to design learning algorithms that are executed on a general purposes computational machines such as human brain. Neural Turing Machine (NTM) is a step towards realizing such a computational machine. The attempt is made here to run a systematic review on Neural Turing Machine. First, the mind-map and taxonomy of machine learning, neural networks, and Turing machine are introduced. Next, NTM is inspected in terms of concepts, structure, variety of versions, implemented tasks, comparisons, etc. Finally, the paper discusses on issues and ends up with several future works.
C3AE: Exploring the Limits of Compact Model for Age Estimation
Authors: Chao Zhang, Shuaicheng Liu, Xun Xu, Ce Zhu
Date: 2019-04-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Age Estimation

Age estimation is a classic learning problem in computer vision. Many larger
and deeper CNNs have been proposed with promising performance, such as AlexNet,
VggNet, GoogLeNet and ResNet. However, these models are not practical for the
embedded/mobile devices. Recently, MobileNets and ShuffleNets have been
proposed to reduce the number of parameters, yielding lightweight models.
However, their representation has been weakened because of the adoption of
depth-wise separable convolution. In this work, we investigate the limits of
compact model for small-scale image and propose an extremely Compact yet
efficient Cascade Context-based Age Estimation model(C3AE). This model
possesses only 1/9 and 1/2000 parameters compared with MobileNets/ShuffleNets
and VggNet, while achieves competitive performance. In particular, we re-define
age estimation problem by two-points representation, which is implemented by a
cascade model. Moreover, to fully utilize the facial context information,
multi-branch CNN network is proposed to aggregate multi-scale context.
Experiments are carried out on three age estimation datasets. The
state-of-the-art performance on compact model has been achieved with a
relatively large margin.
A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification
Authors: Ziqian Zeng, Wenxuan Zhou, Xin Liu, Yangqiu Song
Date: 2019-04-10 00:00:00
Proceeding: NAACL 2019 6
Tasks: General Classification, Sentiment Analysis, Sentiment Classification

In this paper, we propose a variational approach to weakly supervised
document-level multi-aspect sentiment classification. Instead of using
user-generated ratings or annotations provided by domain experts, we use
target-opinion word pairs as "supervision." These word pairs can be extracted
by using dependency parsers and simple rules. Our objective is to predict an
opinion word given a target word while our ultimate goal is to learn a
sentiment polarity classifier to predict the sentiment polarity of each aspect
given a document. By introducing a latent variable, i.e., the sentiment
polarity, to the objective function, we can inject the sentiment polarity
classifier to the objective via the variational lower bound. We can learn a
sentiment polarity classifier by optimizing the lower bound. We show that our
method can outperform weakly supervised baselines on TripAdvisor and
BeerAdvocate datasets and can be comparable to the state-of-the-art supervised
method with hundreds of labels per aspect.
Detecting Cybersecurity Events from Noisy Short Text
Authors: Semih Yagcioglu, Mehmet Saygin Seyfioglu, Begum Citamak, Batuhan Bardak, Seren Guldamlasioglu, Azmi Yuksel, Emin Islam Tatli
Date: 2019-04-10 00:00:00
Proceeding: NAACL 2019 6
Tasks: 2k, Word Embeddings

It is very critical to analyze messages shared over social networks for cyber threat intelligence and cyber-crime prevention. In this study, we propose a method that leverages both domain-specific word embeddings and task-specific features to detect cyber security events from tweets. Our model employs a convolutional neural network (CNN) and a long short-term memory (LSTM) recurrent neural network which takes word level meta-embeddings as inputs and incorporates contextual embeddings to classify noisy short text. We collected a new dataset of cyber security related tweets from Twitter and manually annotated a subset of 2K of them. We experimented with this dataset and concluded that the proposed model outperforms both traditional and neural baselines. The results suggest that our method works well for detecting cyber security events from noisy short text.
Classification of signaling proteins based on molecular star graph descriptors using Machine Learning models
Authors: Carlos Fernandez-Lozano, Ruben F. Cuinas, Jose A. Seoane, Enrique Fernandez-Blanco, Julian Dorado, Cristian R. Munteanu
Date: 2019-04-10 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Prediction

Signaling proteins are an important topic in drug development due to the
increased importance of finding fast, accurate and cheap methods to evaluate
new molecular targets involved in specific diseases. The complexity of the
protein structure hinders the direct association of the signaling activity with
the molecular structure. Therefore, the proposed solution involves the use of
protein star graphs for the peptide sequence information encoding into specific
topological indices calculated with S2SNet tool. The Quantitative
Structure-Activity Relationship classification model obtained with Machine
Learning techniques is able to predict new signaling peptides. The best
classification model is the first signaling prediction model, which is based on
eleven descriptors and it was obtained using the Support Vector Machines -
Recursive Feature Elimination (SVM-RFE) technique with the Laplacian kernel
(RFE-LAP) and an AUROC of 0.961. Testing a set of 3114 proteins of unknown
function from the PDB database assessed the prediction performance of the
model. Important signaling pathways are presented for three UniprotIDs (34
PDBs) with a signaling prediction greater than 98.0%.
Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning
Authors: Ruotent Li, Loong Fah Cheong, Robby T. Tan
Date: 2019-04-10 00:00:00
Tasks: Image Restoration, Rain Removal

Most deraining works focus on rain streaks removal but they cannot deal
adequately with heavy rain images. In heavy rain, streaks are strongly visible,
dense rain accumulation or rain veiling effect significantly washes out the
image, further scenes are relatively more blurry, etc. In this paper, we
propose a novel method to address these problems. We put forth a 2-stage
network: a physics-based backbone followed by a depth-guided GAN refinement.
The first stage estimates the rain streaks, the transmission, and the
atmospheric light governed by the underlying physics. To tease out these
components more reliably, a guided filtering framework is used to decompose the
image into its low- and high-frequency components. This filtering is guided by
a rain-free residue image --- its content is used to set the passbands for the
two channels in a spatially-variant manner so that the background details do
not get mixed up with the rain-streaks. For the second stage, the refinement
stage, we put forth a depth-guided GAN to recover the background details failed
to be retrieved by the first stage, as well as correcting artefacts introduced
by that stage. We have evaluated our method against the state of the art
methods. Extensive experiments show that our method outperforms them on real
rain image data, recovering visually clean images with good details.
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
Authors: Yunpeng Chen, Haoqi Fan, Bing Xu, Zhicheng Yan, Yannis Kalantidis, Marcus Rohrbach, Shuicheng Yan, Jiashi Feng
Date: 2019-04-10 00:00:00
Proceeding: ICCV 2019 10
Tasks: Action Classification, Image Classification, Video Recognition

In natural images, information is conveyed at different frequencies where higher frequencies are usually encoded with fine details and lower frequencies are usually encoded with global structures. Similarly, the output feature maps of a convolution layer can also be seen as a mixture of information at different frequencies. In this work, we propose to factorize the mixed feature maps by their frequencies, and design a novel Octave Convolution (OctConv) operation to store and process feature maps that vary spatially "slower" at a lower spatial resolution reducing both memory and computation cost. Unlike existing multi-scale methods, OctConv is formulated as a single, generic, plug-and-play convolutional unit that can be used as a direct replacement of (vanilla) convolutions without any adjustments in the network architecture. It is also orthogonal and complementary to methods that suggest better topologies or reduce channel-wise redundancy like group or depth-wise convolutions. We experimentally show that by simply replacing convolutions with OctConv, we can consistently boost accuracy for both image and video recognition tasks, while reducing memory and computational cost. An OctConv-equipped ResNet-152 can achieve 82.9% top-1 classification accuracy on ImageNet with merely 22.2 GFLOPs.
Generalizing from a Few Examples: A Survey on Few-Shot Learning
Authors: Yaqing Wang, Quanming Yao, James Kwok, Lionel M. Ni
Date: 2019-04-10 00:00:00
Tasks: BIG-bench Machine Learning, Few-Shot Learning

Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small. Recently, Few-Shot Learning (FSL) is proposed to tackle this problem. Using prior knowledge, FSL can rapidly generalize to new tasks containing only a few samples with supervised information. In this paper, we conduct a thorough survey to fully understand FSL. Starting from a formal definition of FSL, we distinguish FSL from several relevant machine learning problems. We then point out that the core issue in FSL is that the empirical risk minimized is unreliable. Based on how prior knowledge can be used to handle this core issue, we categorize FSL methods from three perspectives: (i) data, which uses prior knowledge to augment the supervised experience; (ii) model, which uses prior knowledge to reduce the size of the hypothesis space; and (iii) algorithm, which uses prior knowledge to alter the search for the best hypothesis in the given hypothesis space. With this taxonomy, we review and discuss the pros and cons of each category. Promising directions, in the aspects of the FSL problem setups, techniques, applications and theories, are also proposed to provide insights for future research.