pwc-archive/papers-with-abstracts huggingface dataset:
Universal Approximation with Quadratic Deep Networks
Authors: Fenglei Fan, JinJun Xiong, Ge Wang
Date: 2018-07-31 00:00:00
Tasks: Speech Recognition

Recently, deep learning has achieved huge successes in many important applications. In our previous studies, we proposed quadratic/second-order neurons and deep quadratic neural networks. In a quadratic neuron, the inner product of a vector of data and the corresponding weights in a conventional neuron is replaced with a quadratic function. The resultant quadratic neuron enjoys an enhanced expressive capability over the conventional neuron. However, how quadratic neurons improve the expressing capability of a deep quadratic network has not been studied up to now, preferably in relation to that of a conventional neural network. Regarding this, we ask four basic questions in this paper: (1) for the one-hidden-layer network structure, is there any function that a quadratic network can approximate much more efficiently than a conventional network? (2) for the same multi-layer network structure, is there any function that can be expressed by a quadratic network but cannot be expressed with conventional neurons in the same structure? (3) Does a quadratic network give a new insight into universal approximation? (4) To approximate the same class of functions with the same error bound, is a quantized quadratic network able to enjoy a lower number of weights than a quantized conventional network? Our main contributions are the four interconnected theorems shedding light upon these four questions and demonstrating the merits of a quadratic network in terms of expressive efficiency, unique capability, compact architecture and computational capacity respectively.
Towards Composable Bias Rating of AI Services
Authors: Biplav Srivastava, Francesca Rossi
Date: 2018-07-31 00:00:00
Tasks: Translation

A new wave of decision-support systems are being built today using AI
services that draw insights from data (like text and video) and incorporate
them in human-in-the-loop assistance. However, just as we expect humans to be
ethical, the same expectation needs to be met by automated systems that
increasingly get delegated to act on their behalf. A very important aspect of
an ethical behavior is to avoid (intended, perceived, or accidental) bias. Bias
occurs when the data distribution is not representative enough of the natural
phenomenon one wants to model and reason about. The possibly biased behavior of
a service is hard to detect and handle if the AI service is merely being used
and not developed from scratch, since the training data set is not available.
In this situation, we envisage a 3rd party rating agency that is independent of
the API producer or consumer and has its own set of biased and unbiased data,
with customizable distributions. We propose a 2-step rating approach that
generates bias ratings signifying whether the AI service is unbiased
compensating, data-sensitive biased, or biased. The approach also works on
composite services. We implement it in the context of text translation and
report interesting results.
Subsampled Rényi Differential Privacy and Analytical Moments Accountant
Authors: Yu-Xiang Wang, Borja Balle, Shiva Kasiviswanathan
Date: 2018-07-31 00:00:00
Tasks: BIG-bench Machine Learning

We study the problem of subsampling in differential privacy (DP), a question
that is the centerpiece behind many successful differentially private machine
learning algorithms. Specifically, we provide a tight upper bound on the
R\'enyi Differential Privacy (RDP) (Mironov, 2017) parameters for algorithms
that: (1) subsample the dataset, and then (2) applies a randomized mechanism M
to the subsample, in terms of the RDP parameters of M and the subsampling
probability parameter. Our results generalize the moments accounting technique,
developed by Abadi et al. (2016) for the Gaussian mechanism, to any subsampled
RDP mechanism.
Optimal Gradient Checkpoint Search for Arbitrary Computation Graphs
Authors: Jianwei Feng, Dong Huang
Date: 2018-07-31 00:00:00
Proceeding: CVPR 2021 1
Tasks: GPU

Deep Neural Networks(DNNs) require huge GPU memory when training on modern image/video databases. Unfortunately, the GPU memory is physically finite, which limits the image resolutions and batch sizes that could be used in training for better DNN performance. Unlike solutions that require physically upgrade GPUs, the Gradient CheckPointing(GCP) training trades computation for more memory beyond existing GPU hardware. GCP only stores a subset of intermediate tensors, called Gradient Checkpoints (GCs), during forward. Then during backward, extra local forwards are conducted to compute the missing tensors. The total training memory cost becomes the sum of (1) the memory cost of the gradient checkpoints and (2) the maximum memory cost of local forwards. To achieve maximal memory cut-offs, one needs optimal algorithms to select GCs. Existing GCP approaches rely on either manual input of GCs or heuristics-based GC search on Linear Computation Graphs (LCGs), and cannot apply to Arbitrary Computation Graphs(ACGs). In this paper, we present theories and optimal algorithms on GC selection that, for the first time, are applicable to ACGs and achieve the maximal memory cut-offs. Extensive experiments show that our approach not only outperforms existing approaches (only applicable on LCGs), and is applicable to a vast family of LCG and ACG networks, such as Alexnet, VGG, ResNet, Densenet, Inception Net and highly complicated DNNs by Network Architecture Search. Our work enables GCP training on ACGs, and cuts off up-to 80% of training memory with a moderate time overhead (~30%-50%). Codes are available
News Session-Based Recommendations using Deep Neural Networks
Authors: Gabriel de Souza P. Moreira, Felipe Ferreira, Adilson Marques da Cunha
Date: 2018-07-31 00:00:00
Tasks: Articles, News Recommendation, Recommendation Systems, Session-Based Recommendations

News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.
A Fuzzy-Rough based Binary Shuffled Frog Leaping Algorithm for Feature Selection
Authors: Javad Rahimipour Anaraki, Saeed Samet, Mahdi Eftekhari, Chang Wook Ahn
Date: 2018-07-31 00:00:00
Tasks: Attribute, feature selection

Feature selection and attribute reduction are crucial problems, and widely
used techniques in the field of machine learning, data mining and pattern
recognition to overcome the well-known phenomenon of the Curse of
Dimensionality, by either selecting a subset of features or removing unrelated
ones. This paper presents a new feature selection method that efficiently
carries out attribute reduction, thereby selecting the most informative
features of a dataset. It consists of two components: 1) a measure for feature
subset evaluation, and 2) a search strategy. For the evaluation measure, we
have employed the fuzzy-rough dependency degree (FRFDD) in the lower
approximation-based fuzzy-rough feature selection (L-FRFS) due to its
effectiveness in feature selection. As for the search strategy, a new version
of a binary shuffled frog leaping algorithm is proposed (B-SFLA). The new
feature selection method is obtained by hybridizing the B-SFLA with the FRDD.
Non-parametric statistical tests are conducted to compare the proposed approach
with several existing methods over twenty two datasets, including nine high
dimensional and large ones, from the UCI repository. The experimental results
demonstrate that the B-SFLA approach significantly outperforms other
metaheuristic methods in terms of the number of selected features and the
classification accuracy.
DNN driven Speaker Independent Audio-Visual Mask Estimation for Speech Separation
Authors: Mandar Gogate, Ahsan Adeel, Ricard Marxer, Jon Barker, Amir Hussain
Date: 2018-07-31 00:00:00
Tasks: Speech Separation

Human auditory cortex excels at selectively suppressing background noise to
focus on a target speaker. The process of selective attention in the brain is
known to contextually exploit the available audio and visual cues to better
focus on target speaker while filtering out other noises. In this study, we
propose a novel deep neural network (DNN) based audiovisual (AV) mask
estimation model. The proposed AV mask estimation model contextually integrates
the temporal dynamics of both audio and noise-immune visual features for
improved mask estimation and speech separation. For optimal AV features
extraction and ideal binary mask (IBM) estimation, a hybrid DNN architecture is
exploited to leverages the complementary strengths of a stacked long short term
memory (LSTM) and convolution LSTM network. The comparative simulation results
in terms of speech quality and intelligibility demonstrate significant
performance improvement of our proposed AV mask estimation model as compared to
audio-only and visual-only mask estimation approaches for both speaker
dependent and independent scenarios.
Deep Sketch-Photo Face Recognition Assisted by Facial Attributes
Authors: Seyed Mehdi Iranmanesh, Hadi Kazemi, Sobhan Soleymani, Ali Dabouei, Nasser M. Nasrabadi
Date: 2018-07-31 00:00:00
Tasks: Attribute, Face Recognition, Face Verification

In this paper, we present a deep coupled framework to address the problem of
matching sketch image against a gallery of mugshots. Face sketches have the
essential in- formation about the spatial topology and geometric details of
faces while missing some important facial attributes such as ethnicity, hair,
eye, and skin color. We propose a cou- pled deep neural network architecture
which utilizes facial attributes in order to improve the sketch-photo
recognition performance. The proposed Attribute-Assisted Deep Con- volutional
Neural Network (AADCNN) method exploits the facial attributes and leverages the
loss functions from the facial attributes identification and face verification
tasks in order to learn rich discriminative features in a common em- bedding
subspace. The facial attribute identification task increases the inter-personal
variations by pushing apart the embedded features extracted from individuals
with differ- ent facial attributes, while the verification task reduces the
intra-personal variations by pulling together all the fea- tures that are
related to one person. The learned discrim- inative features can be well
generalized to new identities not seen in the training data. The proposed
architecture is able to make full use of the sketch and complementary fa- cial
attribute information to train a deep model compared to the conventional
sketch-photo recognition methods. Exten- sive experiments are performed on
composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The
results show the superiority of our method compared to the state- of-the-art
models in sketch-photo recognition algorithms
A Unified Framework for Joint Mobility Prediction and Object Profiling of Drones in UAV Networks
Authors: Han Peng, Abolfazl Razi, Fatemeh Afghah, Jonathan Ashdown
Date: 2018-07-31 00:00:00
Tasks: Management

In recent years, using a network of autonomous and cooperative unmanned
aerial vehicles (UAVs) without command and communication from the ground
station has become more imperative, in particular in search-and-rescue
operations, disaster management, and other applications where human
intervention is limited. In such scenarios, UAVs can make more efficient
decisions if they acquire more information about the mobility, sensing and
actuation capabilities of their neighbor nodes. In this paper, we develop an
unsupervised online learning algorithm for joint mobility prediction and object
profiling of UAVs to facilitate control and communication protocols. The
proposed method not only predicts the future locations of the surrounding
flying objects, but also classifies them into different groups with similar
levels of maneuverability (e.g. rotatory, and fixed-wing UAVs) without prior
knowledge about these classes. This method is flexible in admitting new object
types with unknown mobility profiles, thereby applicable to emerging flying
Ad-hoc networks with heterogeneous nodes.
Learning to See Forces: Surgical Force Prediction with RGB-Point Cloud Temporal Convolutional Networks
Authors: Cong Gao, Xingtong Liu, Michael Peven, Mathias Unberath, Austin Reiter
Date: 2018-07-31 00:00:00

Robotic surgery has been proven to offer clear advantages during surgical
procedures, however, one of the major limitations is obtaining haptic feedback.
Since it is often challenging to devise a hardware solution with accurate force
feedback, we propose the use of "visual cues" to infer forces from tissue
deformation. Endoscopic video is a passive sensor that is freely available, in
the sense that any minimally-invasive procedure already utilizes it. To this
end, we employ deep learning to infer forces from video as an attractive
low-cost and accurate alternative to typically complex and expensive hardware
solutions. First, we demonstrate our approach in a phantom setting using the da
Vinci Surgical System affixed with an OptoForce sensor. Second, we then
validate our method on an ex vivo liver organ. Our method results in a mean
absolute error of 0.814 N in the ex vivo study, suggesting that it may be a
promising alternative to hardware based surgical force feedback in endoscopic
procedures.
Modeling Task Effects in Human Reading with Neural Network-based Attention
Authors: Michael Hahn, Frank Keller
Date: 2018-07-31 00:00:00
Tasks: Question Answering, Reading Comprehension

Research on human reading has long documented that reading behavior shows task-specific effects, but it has been challenging to build general models predicting what reading behavior humans will show in a given task. We introduce NEAT, a computational model of the allocation of attention in human reading, based on the hypothesis that human reading optimizes a tradeoff between economy of attention and success at a task. Our model is implemented using contemporary neural network modeling techniques, and makes explicit and testable predictions about how the allocation of attention varies across different tasks. We test this in an eyetracking study comparing two versions of a reading comprehension task, finding that our model successfully accounts for reading behavior across the tasks. Our work thus provides evidence that task effects can be modeled as optimal adaptation to task demands.
Lip-Reading Driven Deep Learning Approach for Speech Enhancement
Authors: Ahsan Adeel, Mandar Gogate, Amir Hussain, William M. Whitmer
Date: 2018-07-31 00:00:00
Tasks: Acoustic Modelling, Deep Learning, Lip Reading, regression, Speech Enhancement

This paper proposes a novel lip-reading driven deep learning framework for
speech enhancement. The proposed approach leverages the complementary strengths
of both deep learning and analytical acoustic modelling (filtering based
approach) as compared to recently published, comparatively simpler benchmark
approaches that rely only on deep learning. The proposed audio-visual (AV)
speech enhancement framework operates at two levels. In the first level, a
novel deep learning-based lip-reading regression model is employed. In the
second level, lip-reading approximated clean-audio features are exploited,
using an enhanced, visually-derived Wiener filter (EVWF), for the clean audio
power spectrum estimation. Specifically, a stacked long-short-term memory
(LSTM) based lip-reading regression model is designed for clean audio features
estimation using only temporal visual features considering different number of
prior visual frames. For clean speech spectrum estimation, a new
filterbank-domain EVWF is formulated, which exploits estimated speech features.
The proposed EVWF is compared with conventional Spectral Subtraction and
Log-Minimum Mean-Square Error methods using both ideal AV mapping and LSTM
driven AV mapping. The potential of the proposed speech enhancement framework
is evaluated under different dynamic real-world commercially-motivated
scenarios (e.g. cafe, public transport, pedestrian area) at different SNR
levels (ranging from low to high SNRs) using benchmark Grid and ChiME3 corpora.
For objective testing, perceptual evaluation of speech quality is used to
evaluate the quality of restored speech. For subjective testing, the standard
mean-opinion-score method is used with inferential statistics. Comparative
simulation results demonstrate significant lip-reading and speech enhancement
improvement in terms of both speech quality and speech intelligibility.
The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolution
Authors: Muhammad Waleed Gondal, Bernhard Schölkopf, Michael Hirsch
Date: 2018-07-31 00:00:00
Tasks: General Classification, Image Reconstruction, Image Super-Resolution, Super-Resolution

While implicit generative models such as GANs have shown impressive results
in high quality image reconstruction and manipulation using a combination of
various losses, we consider a simpler approach leading to surprisingly strong
results. We show that texture loss alone allows the generation of perceptually
high quality images. We provide a better understanding of texture constraining
mechanism and develop a novel semantically guided texture constraining method
for further improvement. Using a recently developed perceptual metric employing
"deep features" and termed LPIPS, the method obtains state-of-the-art results.
Moreover, we show that a texture representation of those deep features better
capture the perceptual quality of an image than the original deep features.
Using texture information, off-the-shelf deep classification networks (without
training) perform as well as the best performing (tuned and calibrated) LPIPS
metrics. The code is publicly available.
Scalable Multi-Task Gaussian Process Tensor Regression for Normative Modeling of Structured Variation in Neuroimaging Data
Authors: Seyed Mostafa Kia, Christian F. Beckmann, Andre F. Marquand
Date: 2018-07-31 00:00:00
Tasks: Anomaly Detection, Multi-Task Learning

Most brain disorders are very heterogeneous in terms of their underlying
biology and developing analysis methods to model such heterogeneity is a major
challenge. A promising approach is to use probabilistic regression methods to
estimate normative models of brain function using (f)MRI data then use these to
map variation across individuals in clinical populations (e.g., via anomaly
detection). To fully capture individual differences, it is crucial to
statistically model the patterns of correlation across different brain regions
and individuals. However, this is very challenging for neuroimaging data
because of high-dimensionality and highly structured patterns of correlation
across multiple axes. Here, we propose a general and flexible multi-task
learning framework to address this problem. Our model uses a tensor-variate
Gaussian process in a Bayesian mixed-effects model and makes use of Kronecker
algebra and a low-rank approximation to scale efficiently to multi-way
neuroimaging data at the whole brain level. On a publicly available clinical
fMRI dataset, we show that our computationally affordable approach
substantially improves detection sensitivity over both a mass-univariate
normative model and a classifier that --unlike our approach-- has full access
to the clinical labels.
ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction
Authors: Ali Dabouei, Sobhan Soleymani, Hadi Kazemi, Seyed Mehdi Iranmanesh, Jeremy Dawson, Nasser M. Nasrabadi
Date: 2018-07-31 00:00:00
Tasks: Generative Adversarial Network

Performing recognition tasks using latent fingerprint samples is often
challenging for automated identification systems due to poor quality,
distortion, and partially missing information from the input samples. We
propose a direct latent fingerprint reconstruction model based on conditional
generative adversarial networks (cGANs). Two modifications are applied to the
cGAN to adapt it for the task of latent fingerprint reconstruction. First, the
model is forced to generate three additional maps to the ridge map to ensure
that the orientation and frequency information is considered in the generation
process, and prevent the model from filling large missing areas and generating
erroneous minutiae. Second, a perceptual ID preservation approach is developed
to force the generator to preserve the ID information during the reconstruction
process. Using a synthetically generated database of latent fingerprints, the
deep network learns to predict missing information from the input latent
samples. We evaluate the proposed method in combination with two different
fingerprint matching algorithms on several publicly available latent
fingerprint datasets. We achieved the rank-10 accuracy of 88.02\% on the
IIIT-Delhi latent fingerprint database for the task of latent-to-latent
matching and rank-50 accuracy of 70.89\% on the IIIT-Delhi MOLF database for
the task of latent-to-sensor matching. Experimental results of matching
reconstructed samples in both latent-to-sensor and latent-to-latent frameworks
indicate that the proposed method significantly increases the matching accuracy
of the fingerprint recognition systems for the latent samples.
Techniques for Interpretable Machine Learning
Authors: Mengnan Du, Ninghao Liu, Xia Hu
Date: 2018-07-31 00:00:00
Tasks: BIG-bench Machine Learning, Interpretable Machine Learning

Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particular decision. Although many approaches have been proposed, a comprehensive understanding of the achievements and challenges is still lacking. We provide a survey covering existing techniques to increase the interpretability of machine learning models. We also discuss crucial issues that the community should consider in future work such as designing user-friendly explanations and developing comprehensive evaluation metrics to further push forward the area of interpretable machine learning.