pwc-archive/papers-with-abstracts huggingface dataset:
Automated segmentaiton and classification of arterioles and venules using Cascading Dilated Convolutional Neural Networks
Authors: Meng Li, Yan Zhang, Haicheng She, Jinqiong Zhou, Jia Jia, Danmei He, Li Zhang
Date: 2018-12-01 00:00:00
Tasks: General Classification

The change of retinal vasculature is an early sign of many vascular and
systematic diseases, such as diabetes and hypertension. Different behaviors of
retinal arterioles and venules form an important metric to measure the disease
severity. Therefore, an accurate classification of arterioles and venules is of
great necessity. In this work, we propose a novel architecture of deep
convolutional neural network for segmenting and classifying arterioles and
venules on retinal fundus images. This network takes the original color fundus
image as inputs and multi-class labels as outputs. We adopt the
encoding-decoding structure (Unet) as the backbone network of our proposed
model. To improve the classification accuracy, we develop a special encoding
path that couples InceptionV4 modules and Cascading Dilated Convolutions (CDCs)
on top of the backbone network. The model is thus able to extract and fuse
high-level semantic features from multi-scale receptive fields. The proposed
method has outperformed the previous state-of-the-art method on DRIVE dataset
with an accuracy of 0.955 $\pm$ 0.002.
Theory of Cognitive Relativity: A Promising Paradigm for True AI
Authors: Yujian Li
Date: 2018-12-01 00:00:00

The rise of deep learning has brought artificial intelligence (AI) to the
forefront. The ultimate goal of AI is to realize machines with human mind and
consciousness, but existing achievements mainly simulate intelligent behavior
on computer platforms. These achievements all belong to weak AI rather than
strong AI. How to achieve strong AI is not known yet in the field of
intelligence science. Currently, this field is calling for a new paradigm,
especially Theory of Cognitive Relativity (TCR). The TCR aims to summarize a
simple and elegant set of first principles about the nature of intelligence, at
least including the Principle of World's Relativity and the Principle of
Symbol's Relativity. The Principle of World's Relativity states that the
subjective world an intelligent agent can observe is strongly constrained by
the way it perceives the objective world. The Principle of Symbol's Relativity
states that an intelligent agent can use any physical symbol system to express
what it observes in its subjective world. The two principles are derived from
scientific facts and life experience. Thought experiments show that they are
important to understand high-level intelligence and necessary to establish a
scientific theory of mind and consciousness. Rather than brain-like
intelligence, the TCR indeed advocates a promising change in direction to
realize true AI, i.e. artificial general intelligence or artificial
consciousness, particularly different from humans' and animals'. Furthermore, a
TCR creed has been presented and extended to reveal the secrets of
consciousness and to guide realization of conscious machines. In the sense that
true AI could be diversely implemented in a brain-different way, the TCR would
probably drive an intelligence revolution in combination with some additional
first principles.
Deep Inception Generative Network for Cognitive Image Inpainting
Authors: Qingguo Xiao, Guangyao Li, Qiaochuan Chen
Date: 2018-12-01 00:00:00
Tasks: Attribute, Image Inpainting

Recent advances in deep learning have shown exciting promise in filling large
holes and lead to another orientation for image inpainting. However, existing
learning-based methods often create artifacts and fallacious textures because
of insufficient cognition understanding. Previous generative networks are
limited with single receptive type and give up pooling in consideration of
detail sharpness. Human cognition is constant regardless of the target
attribute. As multiple receptive fields improve the ability of abstract image
characterization and pooling can keep feature invariant, specifically, deep
inception learning is adopted to promote high-level feature representation and
enhance model learning capacity for local patches. Moreover, approaches for
generating diverse mask images are introduced and a random mask dataset is
created. We benchmark our methods on ImageNet, Places2 dataset, and CelebA-HQ.
Experiments for regular, irregular, and custom regions completion are all
performed and free-style image inpainting is also presented. Quantitative
comparisons with previous state-of-the-art methods show that ours obtain much
more natural image completions.
How to Profile Privacy-Conscious Users in Recommender Systems
Authors: Fabrice Benhamouda, Marc Joye
Date: 2018-12-01 00:00:00
Tasks: Recommendation Systems

Matrix factorization is a popular method to build a recommender system. In
such a system, existing users and items are associated to a low-dimension
vector called a profile. The profiles of a user and of an item can be combined
(via inner product) to predict the rating that the user would get on the item.
One important issue of such a system is the so-called cold-start problem: how
to allow a user to learn her profile, so that she can then get accurate
recommendations?
  While a profile can be computed if the user is willing to rate well-chosen
items and/or provide supplemental attributes or demographics (such as gender),
revealing this additional information is known to allow the analyst of the
recommender system to infer many more personal sensitive information. We design
a protocol to allow privacy-conscious users to benefit from
matrix-factorization-based recommender systems while preserving their privacy.
More precisely, our protocol enables a user to learn her profile, and from that
to predict ratings without the user revealing any personal information. The
protocol is secure in the standard model against semi-honest adversaries.
Snapshot Distillation: Teacher-Student Optimization in One Generation
Authors: Chenglin Yang, Lingxi Xie, Chi Su, Alan L. Yuille
Date: 2018-12-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: image-classification, Image Classification, object-detection, Object Detection, Semantic Segmentation

Optimizing a deep neural network is a fundamental task in computer vision,
yet direct training methods often suffer from over-fitting. Teacher-student
optimization aims at providing complementary cues from a model trained
previously, but these approaches are often considerably slow due to the
pipeline of training a few generations in sequence, i.e., time complexity is
increased by several times.
  This paper presents snapshot distillation (SD), the first framework which
enables teacher-student optimization in one generation. The idea of SD is very
simple: instead of borrowing supervision signals from previous generations, we
extract such information from earlier epochs in the same generation, meanwhile
make sure that the difference between teacher and student is sufficiently large
so as to prevent under-fitting. To achieve this goal, we implement SD in a
cyclic learning rate policy, in which the last snapshot of each cycle is used
as the teacher for all iterations in the next cycle, and the teacher signal is
smoothed to provide richer information. In standard image classification
benchmarks such as CIFAR100 and ILSVRC2012, SD achieves consistent accuracy
gain without heavy computational overheads. We also verify that models
pre-trained with SD transfers well to object detection and semantic
segmentation in the PascalVOC dataset.
Explore-Exploit: A Framework for Interactive and Online Learning
Authors: Honglei Liu, Anuj Kumar, Wenhai Yang, Benoit Dumoulin
Date: 2018-12-01 00:00:00
Tasks: Active Learning

Interactive user interfaces need to continuously evolve based on the
interactions that a user has (or does not have) with the system. This may
require constant exploration of various options that the system may have for
the user and obtaining signals of user preferences on those. However, such an
exploration, especially when the set of available options itself can change
frequently, can lead to sub-optimal user experiences. We present
Explore-Exploit: a framework designed to collect and utilize user feedback in
an interactive and online setting that minimizes regressions in end-user
experience. This framework provides a suite of online learning operators for
various tasks such as personalization ranking, candidate selection and active
learning. We demonstrate how to integrate this framework with run-time services
to leverage online and interactive machine learning out-of-the-box. We also
present results demonstrating the efficiencies that can be achieved using the
Explore-Exploit framework.
A Big Data Architecture for Log Data Storage and Analysis
Authors: Swapneel Mehta, Prasanth Kothuri, Daniel Lanza Garcia
Date: 2018-12-01 00:00:00
Tasks: BIG-bench Machine Learning

We propose an architecture for analysing database connection logs across
different instances of databases within an intranet comprising over 10,000
users and associated devices. Our system uses Flume agents to send
notifications to a Hadoop Distributed File System for long-term storage and
ElasticSearch and Kibana for short-term visualisation, effectively creating a
data lake for the extraction of log data. We adopt machine learning models with
an ensemble of approaches to filter and process the indicators within the data
and aim to predict anomalies or outliers using feature vectors built from this
log data.
Multi-Stream Dynamic Video Summarization
Authors: Mohamed Elfeki, Liqiang Wang, Ali Borji
Date: 2018-12-01 00:00:00
Tasks: Video Summarization

With vast amounts of video content being uploaded to the Internet every minute, video summarization becomes critical for efficient browsing, searching, and indexing of visual content. Nonetheless, the spread of social and egocentric cameras creates an abundance of sparse scenarios captured by several devices, and ultimately required to be jointly summarized. In this paper, we discuss the problem of summarizing videos recorded independently by several dynamic cameras that intermittently share the field of view. We present a robust framework that (a) identifies a diverse set of important events among moving cameras that often are not capturing the same scene, and (b) selects the most representative view(s) at each event to be included in a universal summary. Due to the lack of an applicable alternative, we collected a new multi-view egocentric dataset, Multi-Ego. Our dataset is recorded simultaneously by three cameras, covering a wide variety of real-life scenarios. The footage is annotated by multiple individuals under various summarization configurations, with a consensus analysis ensuring a reliable ground truth. We conduct extensive experiments on the compiled dataset in addition to three other standard benchmarks that show the robustness and the advantage of our approach in both supervised and unsupervised settings. Additionally, we show that our approach learns collectively from data of varied number-of-views and orthogonal to other summarization methods, deeming it scalable and generic.
From Third Person to First Person: Dataset and Baselines for Synthesis and Retrieval
Authors: Mohamed Elfeki, Krishna Regmi, Shervin Ardeshir, Ali Borji
Date: 2018-12-01 00:00:00
Tasks: Domain Adaptation, Generative Adversarial Network, Optical Flow Estimation, Retrieval

First-person (egocentric) and third person (exocentric) videos are
drastically different in nature. The relationship between these two views have
been studied in recent years, however, it has yet to be fully explored. In this
work, we introduce two datasets (synthetic and natural/real) containing
simultaneously recorded egocentric and exocentric videos. We also explore
relating the two domains (egocentric and exocentric) in two aspects. First, we
synthesize images in the egocentric domain from the exocentric domain using a
conditional generative adversarial network (cGAN). We show that with enough
training data, our network is capable of hallucinating how the world would look
like from an egocentric perspective, given an exocentric video. Second, we
address the cross-view retrieval problem across the two views. Given an
egocentric query frame (or its momentary optical flow), we retrieve its
corresponding exocentric frame (or optical flow) from a gallery set. We show
that using synthetic data could be beneficial in retrieving real data. We show
that performing domain adaptation from the synthetic domain to the natural/real
domain, is helpful in tasks such as retrieval. We believe that the presented
datasets and the proposed baselines offer new opportunities for further
research in this direction. The code and dataset are publicly available.
DVC: An End-to-end Deep Video Compression Framework
Authors: Guo Lu, Wanli Ouyang, Dong Xu, Xiaoyun Zhang, Chunlei Cai, Zhiyong Gao
Date: 2018-11-30 00:00:00
Proceeding: CVPR 2019 6
Tasks: MS-SSIM, Optical Flow Estimation, SSIM, Video Compression

Conventional video compression approaches use the predictive coding
architecture and encode the corresponding motion information and residual
information. In this paper, taking advantage of both classical architecture in
the conventional video compression method and the powerful non-linear
representation ability of neural networks, we propose the first end-to-end
video compression deep model that jointly optimizes all the components for
video compression. Specifically, learning based optical flow estimation is
utilized to obtain the motion information and reconstruct the current frames.
Then we employ two auto-encoder style neural networks to compress the
corresponding motion and residual information. All the modules are jointly
learned through a single loss function, in which they collaborate with each
other by considering the trade-off between reducing the number of compression
bits and improving quality of the decoded video. Experimental results show that
the proposed approach can outperform the widely used video coding standard
H.264 in terms of PSNR and be even on par with the latest standard H.265 in
terms of MS-SSIM. Code is released at https://github.com/GuoLusjtu/DVC.
Understanding Unequal Gender Classification Accuracy from Face Images
Authors: Vidya Muthukumar, Tejaswini Pedapati, Nalini Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilovic, Kush R. Varshney
Date: 2018-11-30 00:00:00
Tasks: Classification, Gender Classification, General Classification

Recent work shows unequal performance of commercial face classification
services in the gender classification task across intersectional groups defined
by skin type and gender. Accuracy on dark-skinned females is significantly
worse than on any other group. In this paper, we conduct several analyses to
try to uncover the reason for this gap. The main finding, perhaps surprisingly,
is that skin type is not the driver. This conclusion is reached via stability
experiments that vary an image's skin type via color-theoretic methods, namely
luminance mode-shift and optimal transport. A second suspect, hair length, is
also shown not to be the driver via experiments on face images cropped to
exclude the hair. Finally, using contrastive post-hoc explanation techniques
for neural networks, we bring forth evidence suggesting that differences in
lip, eye and cheek structure across ethnicity lead to the differences. Further,
lip and eye makeup are seen as strong predictors for a female face, which is a
troubling propagation of a gender stereotype.
Deep Factors with Gaussian Processes for Forecasting
Authors: Danielle C. Maddix, Yuyang Wang, Alex Smola
Date: 2018-11-30 00:00:00
Tasks: Gaussian Processes, Time Series, Time Series Analysis

A large collection of time series poses significant challenges for classical
and neural forecasting approaches. Classical time series models fail to fit
data well and to scale to large problems, but succeed at providing uncertainty
estimates. The converse is true for deep neural networks. In this paper, we
propose a hybrid model that incorporates the benefits of both approaches. Our
new method is data-driven and scalable via a latent, global, deep component. It
also handles uncertainty through a local classical Gaussian Process model. Our
experiments demonstrate that our method obtains higher accuracy than
state-of-the-art methods.
BlockPuzzle - A Challenge in Physical Reasoning and Generalization for Robot Learning
Authors: Yixiu Zhao, Ziyin Liu
Date: 2018-11-30 00:00:00
Tasks: Imitation Learning, MuJoCo, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this work we propose a novel task framework under which a variety of
physical reasoning puzzles can be constructed using very simple rules. Under
sparse reward settings, most of these tasks can be very challenging for a
reinforcement learning agent to learn. We build several simple environments
with this task framework in Mujoco and OpenAI gym and attempt to solve them. We
are able to solve the environments by designing curricula to guide the agent in
learning and using imitation learning methods to transfer knowledge from a
simpler environment. This is only a first step for the task framework, and
further research on how to solve the harder tasks and transfer knowledge
between tasks is needed.
Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search
Authors: Bichen Wu, Yanghan Wang, Peizhao Zhang, Yuandong Tian, Peter Vajda, Kurt Keutzer
Date: 2018-11-30 00:00:00
Proceeding: ICLR 2019 5
Tasks: Neural Architecture Search, Quantization

Recent work in network quantization has substantially reduced the time and
space complexity of neural network inference, enabling their deployment on
embedded and mobile devices with limited computational and memory resources.
However, existing quantization methods often represent all weights and
activations with the same precision (bit-width). In this paper, we explore a
new dimension of the design space: quantizing different layers with different
bit-widths. We formulate this problem as a neural architecture search problem
and propose a novel differentiable neural architecture search (DNAS) framework
to efficiently explore its exponential search space with gradient-based
optimization. Experiments show we surpass the state-of-the-art compression of
ResNet on CIFAR-10 and ImageNet. Our quantized models with 21.1x smaller model
size or 103.9x lower computational cost can still outperform baseline quantized
or even full precision models.
MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment
Authors: Da Zhang, Xiyang Dai, Xin Wang, Yuan-Fang Wang, Larry S. Davis
Date: 2018-11-30 00:00:00
Proceeding: CVPR 2019 6
Tasks: Moment Retrieval, Natural Language Moment Retrieval, Retrieval

This research strives for natural language moment retrieval in long, untrimmed video streams. The problem is not trivial especially when a video contains multiple moments of interests and the language describes complex temporal dependencies, which often happens in real scenarios. We identify two crucial challenges: semantic misalignment and structural misalignment. However, existing approaches treat different moments separately and do not explicitly model complex moment-wise temporal relations. In this paper, we present Moment Alignment Network (MAN), a novel framework that unifies the candidate moment encoding and temporal structural reasoning in a single-shot feed-forward network. MAN naturally assigns candidate moment representations aligned with language semantics over different temporal locations and scales. Most importantly, we propose to explicitly model moment-wise temporal relations as a structured graph and devise an iterative graph adjustment network to jointly learn the best structure in an end-to-end manner. We evaluate the proposed approach on two challenging public benchmarks DiDeMo and Charades-STA, where our MAN significantly outperforms the state-of-the-art by a large margin.
Graph Node-Feature Convolution for Representation Learning
Authors: Li Zhang, Heda Song, Nikolaos Aletras, Haiping Lu
Date: 2018-11-30 00:00:00
Tasks: Node Classification, Representation Learning

Graph convolutional network (GCN) is an emerging neural network approach. It learns new representation of a node by aggregating feature vectors of all neighbors in the aggregation process without considering whether the neighbors or features are useful or not. Recent methods have improved solutions by sampling a fixed size set of neighbors, or assigning different weights to different neighbors in the aggregation process, but features within a feature vector are still treated equally in the aggregation process. In this paper, we introduce a new convolution operation on regular size feature maps constructed from features of a fixed node bandwidth via sampling to get the first-level node representation, which is then passed to a standard GCN to learn the second-level node representation. Experiments show that our method outperforms competing methods in semi-supervised node classification tasks. Furthermore, our method opens new doors for exploring new GCN architectures, particularly deeper GCN models.
Scalable Graph Learning for Anti-Money Laundering: A First Look
Authors: Mark Weber, Jie Chen, Toyotaro Suzumura, Aldo Pareja, Tengfei Ma, Hiroki Kanezashi, Tim Kaler, Charles E. Leiserson, Tao B. Schardl
Date: 2018-11-30 00:00:00
Tasks: Graph Learning

Organized crime inflicts human suffering on a genocidal scale: the Mexican
drug cartels have murdered 150,000 people since 2006, upwards of 700,000 people
per year are "exported" in a human trafficking industry enslaving an estimated
40 million people. These nefarious industries rely on sophisticated money
laundering schemes to operate. Despite tremendous resources dedicated to
anti-money laundering (AML) only a tiny fraction of illicit activity is
prevented. The research community can help. In this brief paper, we map the
structural and behavioral dynamics driving the technical challenge. We review
AML methods, current and emergent. We provide a first look at scalable graph
convolutional neural networks for forensic analysis of financial data, which is
massive, dense, and dynamic. We report preliminary experimental results using a
large synthetic graph (1M nodes, 9M edges) generated by a data simulator we
created called AMLSim. We consider opportunities for high performance
efficiency, in terms of computation and memory, and we share results from a
simple graph compression experiment. Our results support our working hypothesis
that graph deep learning for AML bears great promise in the fight against
criminal financial activity.
Stochastic Gradient MCMC with Repulsive Forces
Authors: Victor Gallego, David Rios Insua
Date: 2018-11-30 00:00:00
Tasks: Bayesian Inference, valid

We propose a unifying view of two different Bayesian inference algorithms, Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) and Stein Variational Gradient Descent (SVGD), leading to improved and efficient novel sampling schemes. We show that SVGD combined with a noise term can be framed as a multiple chain SG-MCMC method. Instead of treating each parallel chain independently from others, our proposed algorithm implements a repulsive force between particles, avoiding collapse and facilitating a better exploration of the parameter space. We also show how the addition of this noise term is necessary to obtain a valid SG-MCMC sampler, a significant difference with SVGD. Experiments with both synthetic distributions and real datasets illustrate the benefits of the proposed scheme.