pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Tracklet Person Re-Identification
Authors: Minxian Li, Xiatian Zhu, Shaogang Gong
Date: 2019-03-01 00:00:00
Tasks: Benchmarking, Domain Adaptation, Person Re-Identification

Most existing person re-identification (re-id) methods rely on supervised
model learning on per-camera-pair manually labelled pairwise training data.
This leads to poor scalability in a practical re-id deployment, due to the lack
of exhaustive identity labelling of positive and negative image pairs for every
camera-pair. In this work, we present an unsupervised re-id deep learning
approach. It is capable of incrementally discovering and exploiting the
underlying re-id discriminative information from automatically generated person
tracklet data end-to-end. We formulate an Unsupervised Tracklet Association
Learning (UTAL) framework. This is by jointly learning within-camera tracklet
discrimination and cross-camera tracklet association in order to maximise the
discovery of tracklet identity matching both within and across camera views.
Extensive experiments demonstrate the superiority of the proposed model over
the state-of-the-art unsupervised learning and domain adaptation person re-id
methods on eight benchmarking datasets.
Improved Differentially Private Analysis of Variance
Authors: Marika Swanberg, Ira Globus-Harris, Iris Griffith, Anna Ritz, Adam Groce, Andrew Bray
Date: 2019-03-01 00:00:00
Tasks: Two-sample testing

Hypothesis testing is one of the most common types of data analysis and forms
the backbone of scientific research in many disciplines. Analysis of variance
(ANOVA) in particular is used to detect dependence between a categorical and a
numerical variable. Here we show how one can carry out this hypothesis test
under the restrictions of differential privacy. We show that the $F$-statistic,
the optimal test statistic in the public setting, is no longer optimal in the
private setting, and we develop a new test statistic $F_1$ with much higher
statistical power. We show how to rigorously compute a reference distribution
for the $F_1$ statistic and give an algorithm that outputs accurate $p$-values.
We implement our test and experimentally optimize several parameters. We then
compare our test to the only previous work on private ANOVA testing, using the
same effect size as that work. We see an order of magnitude improvement, with
our test requiring only 7% as much data to detect the effect.
Aggregating explanation methods for stable and robust explainability
Authors: Laura Rieger, Lars Kai Hansen
Date: 2019-03-01 00:00:00

Despite a growing literature on explaining neural networks, no consensus has been reached on how to explain a neural network decision or how to evaluate an explanation. Our contributions in this paper are twofold. First, we investigate schemes to combine explanation methods and reduce model uncertainty to obtain a single aggregated explanation. We provide evidence that the aggregation is better at identifying important features, than on individual methods. Adversarial attacks on explanations is a recent active research topic. As our second contribution, we present evidence that aggregate explanations are much more robust to attacks than individual explanation methods.
Introducing Super Pseudo Panels: Application to Transport Preference Dynamics
Authors: Stanislav S. Borysov, Jeppe Rich
Date: 2019-03-01 00:00:00

We propose a new approach for constructing synthetic pseudo-panel data from
cross-sectional data. The pseudo panel and the preferences it intends to
describe is constructed at the individual level and is not affected by
aggregation bias across cohorts. This is accomplished by creating a
high-dimensional probabilistic model representation of the entire data set,
which allows sampling from the probabilistic model in such a way that all of
the intrinsic correlation properties of the original data are preserved. The
key to this is the use of deep learning algorithms based on the Conditional
Variational Autoencoder (CVAE) framework. From a modelling perspective, the
concept of a model-based resampling creates a number of opportunities in that
data can be organized and constructed to serve very specific needs of which the
forming of heterogeneous pseudo panels represents one. The advantage, in that
respect, is the ability to trade a serious aggregation bias (when aggregating
into cohorts) for an unsystematic noise disturbance. Moreover, the approach
makes it possible to explore high-dimensional sparse preference distributions
and their linkage to individual specific characteristics, which is not possible
if applying traditional pseudo-panel methods. We use the presented approach to
reveal the dynamics of transport preferences for a fixed pseudo panel of
individuals based on a large Danish cross-sectional data set covering the
period from 2006 to 2016. The model is also utilized to classify individuals
into 'slow' and 'fast' movers with respect to the speed at which their
preferences change over time. It is found that the prototypical fast mover is a
young woman who lives as a single in a large city whereas the typical slow
mover is a middle-aged man with high income from a nuclear family who lives in
a detached house outside a city.
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning
Authors: Yizhe Zhu, Jianwen Xie, Zhiqiang Tang, Xi Peng, Ahmed Elgammal
Date: 2019-03-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Triplet, Zero-Shot Learning

Zero-shot learning extends the conventional object classification to the unseen class recognition by introducing semantic representations of classes. Existing approaches predominantly focus on learning the proper mapping function for visual-semantic embedding, while neglecting the effect of learning discriminative visual features. In this paper, we study the significance of the discriminative region localization. We propose a semantic-guided multi-attention localization model, which automatically discovers the most discriminative parts of objects for zero-shot learning without any human annotations. Our model jointly learns cooperative global and local features from the whole object as well as the detected parts to categorize objects based on semantic descriptions. Moreover, with the joint supervision of embedding softmax loss and class-center triplet loss, the model is encouraged to learn features with high inter-class dispersion and intra-class compactness. Through comprehensive experiments on three widely used zero-shot learning benchmarks, we show the efficacy of the multi-attention localization and our proposed approach improves the state-of-the-art results by a considerable margin.
Asymptotics of MAP Inference in Deep Networks
Authors: Parthe Pandit, Mojtaba Sahraee, Sundeep Rangan, Alyson K. Fletcher
Date: 2019-03-01 00:00:00

Deep generative priors are a powerful tool for reconstruction problems with
complex data such as images and text. Inverse problems using such models
require solving an inference problem of estimating the input and hidden units
of the multi-layer network from its output. Maximum a priori (MAP) estimation
is a widely-used inference method as it is straightforward to implement, and
has been successful in practice. However, rigorous analysis of MAP inference in
multi-layer networks is difficult. This work considers a recently-developed
method, multi-layer vector approximate message passing (ML-VAMP), to study MAP
inference in deep networks. It is shown that the mean squared error of the
ML-VAMP estimate can be exactly and rigorously characterized in a certain
high-dimensional random limit. The proposed method thus provides a tractable
method for MAP inference with exact performance guarantees.
PEA265: Perceptual Assessment of Video Compression Artifacts
Authors: Liqun Lin, Shiqi Yu, Tiesong Zhao, Member, Zhou Wang, Fellow, IEEE
Date: 2019-03-01 00:00:00
Tasks: Blocking, Motion Estimation, Video Compression, Video Quality Assessment

The most widely used video encoders share a common hybrid coding framework
that includes block-based motion estimation/compensation and block-based
transform coding. Despite their high coding efficiency, the encoded videos
often exhibit visually annoying artifacts, denoted as Perceivable Encoding
Artifacts (PEAs), which significantly degrade the visual Qualityof- Experience
(QoE) of end users. To monitor and improve visual QoE, it is crucial to develop
subjective and objective measures that can identify and quantify various types
of PEAs. In this work, we make the first attempt to build a large-scale
subjectlabelled database composed of H.265/HEVC compressed videos containing
various PEAs. The database, namely the PEA265 database, includes 4 types of
spatial PEAs (i.e. blurring, blocking, ringing and color bleeding) and 2 types
of temporal PEAs (i.e. flickering and floating). Each containing at least
60,000 image or video patches with positive and negative labels. To objectively
identify these PEAs, we train Convolutional Neural Networks (CNNs) using the
PEA265 database. It appears that state-of-theart ResNeXt is capable of
identifying each type of PEAs with high accuracy. Furthermore, we define PEA
pattern and PEA intensity measures to quantify PEA levels of compressed video
sequence. We believe that the PEA265 database and our findings will benefit the
future development of video quality assessment methods and perceptually
motivated video encoders.
Quantitative Robustness of Localized Support Vector Machines
Authors: Florian Dumpert
Date: 2019-03-01 00:00:00

The huge amount of available data nowadays is a challenge for kernel-based
machine learning algorithms like SVMs with respect to runtime and storage
capacities. Local approaches might help to relieve these issues and to improve
statistical accuracy. It has already been shown that these local approaches are
consistent and robust in a basic sense. This article refines the analysis of
robustness properties towards the so-called influence function which expresses
the differentiability of the learning method: We show that there is a
differentiable dependency of our locally learned predictor on the underlying
distribution. The assumptions of the proven theorems can be verified without
knowing anything about this distribution. This makes the results interesting
also from an applied point of view.
Integrating Temporal Information to Spatial Information in a Neural Circuit
Authors: Nancy Lynch, Mien Brabeeba Wang
Date: 2019-03-01 00:00:00

In this paper, we consider networks of deterministic spiking neurons, firing synchronously at discrete times; such spiking neural networks are inspired by networks of neurons and synapses that occur in brains. We consider the problem of translating temporal information into spatial information in such networks, an important task that is carried out by actual brains. Specifically, we define two problems: "First Consecutive Spikes Counting (FCSC)" and "Total Spikes Counting (TSC)", which model spike and rate coding aspects of translating temporal information into spatial information respectively. Assuming an upper bound of $T$ on the length of the temporal input signal, we design two networks that solve these two problems, each using $O(\log T)$ neurons and terminating in time $1$. We also prove that there is no network with less than $T$ neurons that solves either question in time $0$.
Extended Bit-Plane Compression for Convolutional Neural Network Accelerators
Authors: Lukas Cavigelli, Luca Benini
Date: 2018-10-01 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection, speech-recognition, Speech Recognition

After the tremendous success of convolutional neural networks in image
classification, object detection, speech recognition, etc., there is now rising
demand for deployment of these compute-intensive ML models on tightly power
constrained embedded and mobile systems at low cost as well as for pushing the
throughput in data centers. This has triggered a wave of research towards
specialized hardware accelerators. Their performance is often constrained by
I/O bandwidth and the energy consumption is dominated by I/O transfers to
off-chip memory. We introduce and evaluate a novel, hardware-friendly
compression scheme for the feature maps present within convolutional neural
networks. We show that an average compression ratio of 4.4x relative to
uncompressed data and a gain of 60% over existing method can be achieved for
ResNet-34 with a compression block requiring <300 bit of sequential cells and
minimal combinational logic.
Using stigmergy as a computational memory in the design of recurrent neural networks
Authors: Federico A. Galatolo, Mario G. C. A. Cimino, Gigliola Vaglini
Date: 2019-01-09 00:00:00
Tasks: General Classification, Temporal Sequences

In this paper, a novel architecture of Recurrent Neural Network (RNN) is
designed and experimented. The proposed RNN adopts a computational memory based
on the concept of stigmergy. The basic principle of a Stigmergic Memory (SM) is
that the activity of deposit/removal of a quantity in the SM stimulates the
next activities of deposit/removal. Accordingly, subsequent SM activities tend
to reinforce/weaken each other, generating a coherent coordination between the
SM activities and the input temporal stimulus. We show that, in a problem of
supervised classification, the SM encodes the temporal input in an emergent
representational model, by coordinating the deposit, removal and classification
activities. This study lays down a basic framework for the derivation of a
SM-RNN. A formal ontology of SM is discussed, and the SM-RNN architecture is
detailed. To appreciate the computational power of an SM-RNN, comparative NNs
have been selected and trained to solve the MNIST handwritten digits
recognition benchmark in its two variants: spatial (sequences of bitmap rows)
and temporal (sequences of pen strokes).
A complete formalized knowledge representation model for advanced digital forensics timeline analysis
Authors: Yoan Chabot, Aurélie Bertaux, Christophe Nicollea, Tahar Kechadi
Date: 2019-02-21 00:00:00

Having a clear view of events that occurred over time is a difficult
objective to achieve in digital investigations (DI). Event reconstruction,
which allows investigators to understand the timeline of a crime, is one of the
most important step of a DI process. This complex task requires exploration of
a large amount of events due to the pervasiveness of new technologies nowadays.
Any evidence produced at the end of the investigative process must also meet
the requirements of the courts, such as reproducibility, verifiability,
validation, etc. For this purpose, we propose a new methodology, supported by
theoretical concepts, that can assist investigators through the whole process
including the construction and the interpretation of the events describing the
case. The proposed approach is based on a model which integrates knowledge of
experts from the fields of digital forensics and software development to allow
a semantically rich representation of events related to the incident. The main
purpose of this model is to allow the analysis of these events in an automatic
and efficient way. This paper describes the approach and then focuses on the
main conceptual and formal aspects: a formal incident modelization and
operators for timeline reconstruction and analysis.
Sparse Depth Enhanced Direct Thermal-infrared SLAM Beyond the Visible Spectrum
Authors: Young-Sik Shin, Ayoung Kim
Date: 2019-02-28 00:00:00
Tasks: Motion Estimation, Simultaneous Localization and Mapping

In this paper, we propose a thermal-infrared simultaneous localization and
mapping (SLAM) system enhanced by sparse depth measurements from Light
Detection and Ranging (LiDAR). Thermal-infrared cameras are relatively robust
against fog, smoke, and dynamic lighting conditions compared to RGB cameras
operating under the visible spectrum. Due to the advantages of thermal-infrared
cameras, exploiting them for motion estimation and mapping is highly appealing.
However, operating a thermal-infrared camera directly in existing vision-based
methods is difficult because of the modality difference. This paper proposes a
method to use sparse depth measurement for 6-DOF motion estimation by directly
tracking under 14- bit raw measurement of the thermal camera. In addition, we
perform a refinement to improve the local accuracy and include a loop closure
to maintain global consistency. The experimental results demonstrate that the
system is not only robust under various lighting conditions such as day and
night, but also overcomes the scale problem of monocular cameras. The video is
available at https://youtu.be/oO7lT3uAzLc.
Variational Quantum Circuit Model for Knowledge Graphs Embedding
Authors: Yunpu Ma, Volker Tresp, Liming Zhao, Yuyi Wang
Date: 2019-02-19 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, model, Relational Reasoning, Representation Learning

In this work, we propose the first quantum Ans\"atze for the statistical
relational learning on knowledge graphs using parametric quantum circuits. We
introduce two types of variational quantum circuits for knowledge graph
embedding. Inspired by the classical representation learning, we first consider
latent features for entities as coefficients of quantum states, while
predicates are characterized by parametric gates acting on the quantum states.
For the first model, the quantum advantages disappear when it comes to the
optimization of this model. Therefore, we introduce a second quantum circuit
model where embeddings of entities are generated from parameterized quantum
gates acting on the pure quantum state. The benefit of the second method is
that the quantum embeddings can be trained efficiently meanwhile preserving the
quantum advantages. We show the proposed methods can achieve comparable results
to the state-of-the-art classical models, e.g., RESCAL, DistMult. Furthermore,
after optimizing the models, the complexity of inductive inference on the
knowledge graphs might be reduced with respect to the number of entities.
The Regretful Navigation Agent for Vision-and-Language Navigation
Authors: Chih-Yao Ma, Zuxuan Wu, Ghassan AlRegib, Caiming Xiong, Zsolt Kira
Date: 2019-03-05 00:00:00
Proceeding: CVPR 2019 (Oral) 2019 3
Tasks: Decision Making, Vision and Language Navigation, Vision-Language Navigation, Visual Navigation

As deep learning continues to make progress for challenging perception tasks, there is increased interest in combining vision, language, and decision-making. Specifically, the Vision and Language Navigation (VLN) task involves navigating to a goal purely from language instructions and visual information without explicit knowledge of the goal. Recent successful approaches have made in-roads in achieving good success rates for this task but rely on beam search, which thoroughly explores a large number of trajectories and is unrealistic for applications such as robotics. In this paper, inspired by the intuition of viewing the problem as search on a navigation graph, we propose to use a progress monitor developed in prior work as a learnable heuristic for search. We then propose two modules incorporated into an end-to-end architecture: 1) A learned mechanism to perform backtracking, which decides whether to continue moving forward or roll back to a previous state (Regret Module) and 2) A mechanism to help the agent decide which direction to go next by showing directions that are visited and their associated progress estimate (Progress Marker). Combined, the proposed approach significantly outperforms current state-of-the-art methods using greedy action selection, with 5% absolute improvement on the test server in success rates, and more importantly 8% on success rates normalized by the path length.
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM
Authors: Yukun Ma, Haiyun Peng, Erik Cambria
Date: 2018-04-01 00:00:00
Proceeding: The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) 2018 4
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Natural Language Understanding, Sentence, Sentiment Analysis, Sentiment Classification

Analyzing people’s opinions and sentiments towards certain aspects is an important task of natural language understanding.
In this paper, we propose a novel solution to targeted aspect-based sentiment analysis, which tackles the challenges
of both aspect-based sentiment analysis and targeted sentiment analysis by exploiting commonsense knowledge. We
augment the long short-term memory (LSTM) network with a hierarchical attention mechanism consisting of a target level
attention and a sentence-level attention. Commonsense knowledge of sentiment-related concepts is incorporated into
the end-to-end training of a deep neural network for sentiment classification. In order to tightly integrate the commonsense
knowledge into the recurrent encoder, we propose an extension of LSTM, termed Sentic LSTM. We conduct experiments
on two publicly released datasets, which show that the combination of the proposed attention architecture and Sentic
LSTM can outperform state-of-the-art methods in targeted aspect sentiment tasks.
Learning Latent Opinions for Aspect-Level Sentiment Classification
Authors: Bailin Wang, Wei Lu
Date: 2018-04-01 00:00:00
Proceeding: AAAI-18 2018 4
Tasks: Aspect-Based Sentiment Analysis (ABSA), Classification, General Classification, Sentence, Sentiment Analysis, Sentiment Classification

Aspect-level sentiment classification aims at detecting the sentiment expressed towards a particular target in a sentence. Based on the observation that the sentiment polarity is often related to specific spans in the given sentence, it is possible to make use of such information for better classification. On the other hand, such information can also serve as justifications associated with the predictions.We propose a segmentation attention based LSTM model which can effectively capture the structural dependencies between the target and the sentiment expressions with a linear-chain conditional random field (CRF) layer. The model simulates human's process of inferring sentiment information when reading: when given a target, humans tend to search for surrounding relevant text spans in the sentence before making an informed decision on the underlying sentiment information.We perform sentiment classification tasks on publicly available datasets on online reviews across different languages from SemEval tasks and social comments from Twitter. Extensive experiments show that our model achieves the state-of-the-art performance while extracting interpretable sentiment expressions.
Aspect-Based Sentiment Analysis Using Bitmask Bidirectional Long Short Term Memory Networks
Authors: Binh Thanh Do
Date: 2018-05-01 00:00:00
Proceeding: FLAIRS-31 2018 5
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Word Embeddings

This paper introduces a new method to classify sentiment polarity for aspects in product reviews. We call it bitmask bidirectional long short term memory networks. It is based on long short term memory (LSTM) networks, which is a frequently mentioned model in natural language processing. Our proposed method uses a bitmask layer to keep attention on aspects. We evaluate it on reviews of restaurant and laptop domains from three popular contests: SemEval-2014 task 4, SemEval-2015 task 12, and SemEval-2016 task 5. It obtains competitive results with state-of-the-art methods based on LSTM networks. Furthermore, we demonstrate the benefit of using sentiment lexicons and word embeddings of a particular domain in aspect-based sentiment analysis.