pwc-archive/papers-with-abstracts huggingface dataset:
On the Minimax Misclassification Ratio of Hypergraph Community Detection
Authors: I Chien, Chung-Yi Lin, I-Hsiang Wang
Date: 2018-02-03 00:00:00
Tasks: Clustering, Community Detection, parameter estimation, Stochastic Block Model

Community detection in hypergraphs is explored. Under a generative hypergraph
model called "d-wise hypergraph stochastic block model" (d-hSBM) which
naturally extends the Stochastic Block Model from graphs to d-uniform
hypergraphs, the asymptotic minimax mismatch ratio is characterized. For
proving the achievability, we propose a two-step polynomial time algorithm that
achieves the fundamental limit. The first step of the algorithm is a hypergraph
spectral clustering method which achieves partial recovery to a certain
precision level. The second step is a local refinement method which leverages
the underlying probabilistic model along with parameter estimation from the
outcome of the first step. To characterize the asymptotic performance of the
proposed algorithm, we first derive a sufficient condition for attaining weak
consistency in the hypergraph spectral clustering step. Then, under the
guarantee of weak consistency in the first step, we upper bound the worst-case
risk attained in the local refinement step by an exponentially decaying
function of the size of the hypergraph and characterize the decaying rate. For
proving the converse, the lower bound of the minimax mismatch ratio is set by
finding a smaller parameter space which contains the most dominant error
events, inspired by the analysis in the achievability part. It turns out that
the minimax mismatch ratio decays exponentially fast to zero as the number of
nodes tends to infinity, and the rate function is a weighted combination of
several divergence terms, each of which is the Renyi divergence of order 1/2
between two Bernoulli's. The Bernoulli's involved in the characterization of
the rate function are those governing the random instantiation of hyperedges in
d-hSBM. Experimental results on synthetic data validate our theoretical finding
that the refinement step is critical in achieving the optimal statistical
limit.
Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement Learning
Authors: Minghai Chen, Sen Wang, Paul Pu Liang, Tadas Baltrušaitis, Amir Zadeh, Louis-Philippe Morency
Date: 2018-02-03 00:00:00
Tasks: Multimodal Sentiment Analysis, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentiment Analysis, Sentiment Classification, Subjectivity Analysis

With the increasing popularity of video sharing websites such as YouTube and
Facebook, multimodal sentiment analysis has received increasing attention from
the scientific community. Contrary to previous works in multimodal sentiment
analysis which focus on holistic information in speech segments such as bag of
words representations and average facial expression intensity, we develop a
novel deep architecture for multimodal sentiment analysis that performs
modality fusion at the word level. In this paper, we propose the Gated
Multimodal Embedding LSTM with Temporal Attention (GME-LSTM(A)) model that is
composed of 2 modules. The Gated Multimodal Embedding alleviates the
difficulties of fusion when there are noisy modalities. The LSTM with Temporal
Attention performs word level fusion at a finer fusion resolution between input
modalities and attends to the most important time steps. As a result, the
GME-LSTM(A) is able to better model the multimodal structure of speech through
time and perform better sentiment comprehension. We demonstrate the
effectiveness of this approach on the publicly-available Multimodal Corpus of
Sentiment Intensity and Subjectivity Analysis (CMU-MOSI) dataset by achieving
state-of-the-art sentiment classification and regression results. Qualitative
analysis on our model emphasizes the importance of the Temporal Attention Layer
in sentiment prediction because the additional acoustic and visual modalities
are noisy. We also demonstrate the effectiveness of the Gated Multimodal
Embedding in selectively filtering these noisy modalities out. Our results and
analysis open new areas in the study of sentiment analysis in human
communication and provide new models for multimodal fusion.
Multi-attention Recurrent Network for Human Communication Comprehension
Authors: Amir Zadeh, Paul Pu Liang, Soujanya Poria, Prateek Vij, Erik Cambria, Louis-Philippe Morency
Date: 2018-02-03 00:00:00
Tasks: Emotion Recognition, Multimodal Sentiment Analysis, Sentiment Analysis

Human face-to-face communication is a complex multimodal signal. We use words
(language modality), gestures (vision modality) and changes in tone (acoustic
modality) to convey our intentions. Humans easily process and understand
face-to-face communication, however, comprehending this form of communication
remains a significant challenge for Artificial Intelligence (AI). AI must
understand each modality and the interactions between them that shape human
communication. In this paper, we present a novel neural architecture for
understanding human communication called the Multi-attention Recurrent Network
(MARN). The main strength of our model comes from discovering interactions
between modalities through time using a neural component called the
Multi-attention Block (MAB) and storing them in the hybrid memory of a
recurrent component called the Long-short Term Hybrid Memory (LSTHM). We
perform extensive comparisons on six publicly available datasets for multimodal
sentiment analysis, speaker trait recognition and emotion recognition. MARN
shows state-of-the-art performance on all the datasets.
A survey of embedding models of entities and relationships for knowledge graph completion
Authors: Dat Quoc Nguyen
Date: 2017-03-23 00:00:00
Proceeding: COLING (TextGraphs) 2020 12
Tasks: Knowledge Base Completion, Knowledge Graph Completion, Knowledge Graphs, Link Prediction

Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge graphs are typically incomplete, it is useful to perform knowledge graph completion or link prediction, i.e. predict whether a relationship not in the knowledge graph is likely to be true. This paper serves as a comprehensive survey of embedding models of entities and relationships for knowledge graph completion, summarizing up-to-date experimental results on standard benchmark datasets and pointing out potential future research directions.
Build a Compact Binary Neural Network through Bit-level Sensitivity and Data Pruning
Authors: Yixing Li, Fengbo Ren
Date: 2018-02-03 00:00:00
Tasks: Neural Network Compression, Sensitivity

Convolutional neural network (CNN) has been widely used for vision-based
tasks. Due to the high computational complexity and memory storage requirement,
it is hard to directly deploy a full-precision CNN on embedded devices. The
hardware-friendly designs are needed for re-source-limited and
energy-constrained embed-ded devices. Emerging solutions are adopted for the
neural network compression, e.g., bina-ry/ternary weight network, pruned
network and quantized network. Among them, Binarized Neural Network (BNN) is
believed to be the most hardware-friendly framework due to its small network
size and low computational com-plexity. No existing work has further shrunk the
size of BNN. In this work, we explore the redun-dancy in BNN and build a
compact BNN (CBNN) based on the bit-level sensitivity analy-sis and bit-level
data pruning. The input data is converted to a high dimensional bit-sliced
for-mat. In post-training stage, we analyze the im-pact of different bit slices
to the accuracy. By pruning the redundant input bit slices and shrinking the
network size, we are able to build a more compact BNN. Our result shows that we
can further scale down the network size of the BNN up to 3.9x with no more than
1% accuracy drop. The actual runtime can be reduced up to 2x and 9.9x compared
with the baseline BNN and its full-precision counterpart, respectively.
Privacy with Estimation Guarantees
Authors: Hao Wang, Lisa Vo, Flavio P. Calmon, Muriel Médard, Ken R. Duffy, Mayank Varia
Date: 2017-10-02 00:00:00

We study the central problem in data privacy: how to share data with an analyst while providing both privacy and utility guarantees to the user that owns the data. In this setting, we present an estimation-theoretic analysis of the privacy-utility trade-off (PUT). Here, an analyst is allowed to reconstruct (in a mean-squared error sense) certain functions of the data (utility), while other private functions should not be reconstructed with distortion below a certain threshold (privacy). We demonstrate how chi-square information captures the fundamental PUT in this case and provide bounds for the best PUT. We propose a convex program to compute privacy-assuring mappings when the functions to be disclosed and hidden are known a priori and the data distribution is known. We derive lower bounds on the minimum mean-squared error of estimating a target function from the disclosed data and evaluate the robustness of our approach when an empirical distribution is used to compute the privacy-assuring mappings instead of the true data distribution. We illustrate the proposed approach through two numerical experiments.
Large-Scale Image Retrieval with Attentive Deep Local Features
Authors: Hyeonwoo Noh, Andre Araujo, Jack Sim, Tobias Weyand, Bohyung Han
Date: 2016-12-19 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Retrieval, Retrieval

We propose an attentive local feature descriptor suitable for large-scale
image retrieval, referred to as DELF (DEep Local Feature). The new feature is
based on convolutional neural networks, which are trained only with image-level
annotations on a landmark image dataset. To identify semantically useful local
features for image retrieval, we also propose an attention mechanism for
keypoint selection, which shares most network layers with the descriptor. This
framework can be used for image retrieval as a drop-in replacement for other
keypoint detectors and descriptors, enabling more accurate feature matching and
geometric verification. Our system produces reliable confidence scores to
reject false positives---in particular, it is robust against queries that have
no correct match in the database. To evaluate the proposed descriptor, we
introduce a new large-scale dataset, referred to as Google-Landmarks dataset,
which involves challenges in both database and query such as background
clutter, partial occlusion, multiple landmarks, objects in variable scales,
etc. We show that DELF outperforms the state-of-the-art global and local
descriptors in the large-scale setting by significant margins. Code and dataset
can be found at the project webpage:
https://github.com/tensorflow/models/tree/master/research/delf .
Left-Center-Right Separated Neural Network for Aspect-based Sentiment Analysis with Rotatory Attention
Authors: Shiliang Zheng, Rui Xia
Date: 2018-02-03 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis

Deep learning techniques have achieved success in aspect-based sentiment
analysis in recent years. However, there are two important issues that still
remain to be further studied, i.e., 1) how to efficiently represent the target
especially when the target contains multiple words; 2) how to utilize the
interaction between target and left/right contexts to capture the most
important words in them. In this paper, we propose an approach, called
left-center-right separated neural network with rotatory attention (LCR-Rot),
to better address the two problems. Our approach has two characteristics: 1) it
has three separated LSTMs, i.e., left, center and right LSTMs, corresponding to
three parts of a review (left context, target phrase and right context); 2) it
has a rotatory attention mechanism which models the relation between target and
left/right contexts. The target2context attention is used to capture the most
indicative sentiment words in left/right contexts. Subsequently, the
context2target attention is used to capture the most important word in the
target. This leads to a two-side representation of the target: left-aware
target and right-aware target. We compare our approach on three benchmark
datasets with ten related methods proposed recently. The results show that our
approach significantly outperforms the state-of-the-art techniques.
Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification
Authors: Huihui He, Rui Xia
Date: 2018-02-03 00:00:00
Tasks: Classification, Computational Efficiency, Emotion Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Learning, Representation Learning

Recently the deep learning techniques have achieved success in multi-label
classification due to its automatic representation learning ability and the
end-to-end learning framework. Existing deep neural networks in multi-label
classification can be divided into two kinds: binary relevance neural network
(BRNN) and threshold dependent neural network (TDNN). However, the former needs
to train a set of isolate binary networks which ignore dependencies between
labels and have heavy computational load, while the latter needs an additional
threshold function mechanism to transform the multi-class probabilities to
multi-label outputs. In this paper, we propose a joint binary neural network
(JBNN), to address these shortcomings. In JBNN, the representation of the text
is fed to a set of logistic functions instead of a softmax function, and the
multiple binary classifications are carried out synchronously in one neural
network framework. Moreover, the relations between labels are captured via
training on a joint binary cross entropy (JBCE) loss. To better meet
multi-label emotion classification, we further proposed to incorporate the
prior label relations into the JBCE loss. The experimental results on the
benchmark dataset show that our model performs significantly better than the
state-of-the-art multi-label emotion classification methods, in both
classification performance and computational efficiency.
Densely Connected Bidirectional LSTM with Applications to Sentence Classification
Authors: Zixiang Ding, Rui Xia, Jianfei Yu, Xiang Li, Jian Yang
Date: 2018-02-03 00:00:00
Tasks: Classification, General Classification, Sentence, Sentence Classification

Deep neural networks have recently been shown to achieve highly competitive
performance in many computer vision tasks due to their abilities of exploring
in a much larger hypothesis space. However, since most deep architectures like
stacked RNNs tend to suffer from the vanishing-gradient and overfitting
problems, their effects are still understudied in many NLP tasks. Inspired by
this, we propose a novel multi-layer RNN model called densely connected
bidirectional long short-term memory (DC-Bi-LSTM) in this paper, which
essentially represents each layer by the concatenation of its hidden state and
all preceding layers' hidden states, followed by recursively passing each
layer's representation to all subsequent layers. We evaluate our proposed model
on five benchmark datasets of sentence classification. DC-Bi-LSTM with depth up
to 20 can be successfully trained and obtain significant improvements over the
traditional Bi-LSTM with the same or even less parameters. Moreover, our model
has promising performance compared with the state-of-the-art approaches.
Boundary-based Image Forgery Detection by Fast Shallow CNN
Authors: Zhongping Zhang, Yixuan Zhang, Zheng Zhou, Jiebo Luo
Date: 2018-01-20 00:00:00
Tasks: Demosaicking, Image Forgery Detection

Image forgery detection is the task of detecting and localizing forged parts
in tampered images. Previous works mostly focus on high resolution images using
traces of resampling features, demosaicing features or sharpness of edges.
However, a good detection method should also be applicable to low resolution
images because compressed or resized images are common these days. To this end,
we propose a Shallow Convolutional Neural Network(SCNN), capable of
distinguishing the boundaries of forged regions from original edges in low
resolution images. SCNN is designed to utilize the information of chroma and
saturation. Based on SCNN, two approaches that are named Sliding Windows
Detection (SWD) and Fast SCNN, respectively, are developed to detect and
localize image forgery region. In this paper, we substantiate that Fast SCNN
can detect drastic change of chroma and saturation. In image forgery detection
experiments Our model is evaluated on the CASIA 2.0 dataset. The results show
that Fast SCNN performs well on low resolution images and achieves significant
improvements over the state-of-the-art.
Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization
Authors: Nicolas Y. Masse, Gregory D. Grant, David J. Freedman
Date: 2018-02-02 00:00:00

Humans and most animals can learn new tasks without forgetting old ones.
However, training artificial neural networks (ANNs) on new tasks typically
cause it to forget previously learned tasks. This phenomenon is the result of
"catastrophic forgetting", in which training an ANN disrupts connection weights
that were important for solving previous tasks, degrading task performance.
Several recent studies have proposed methods to stabilize connection weights of
ANNs that are deemed most important for solving a task, which helps alleviate
catastrophic forgetting. Here, drawing inspiration from algorithms that are
believed to be implemented in vivo, we propose a complementary method: adding a
context-dependent gating signal, such that only sparse, mostly non-overlapping
patterns of units are active for any one task. This method is easy to
implement, requires little computational overhead, and allows ANNs to maintain
high performance across large numbers of sequentially presented tasks when
combined with weight stabilization. This work provides another example of how
neuroscience-inspired algorithms can benefit ANN design and capability.
Selective Sampling and Mixture Models in Generative Adversarial Networks
Authors: Karim Said Barsim, Lirong Yang, Bin Yang
Date: 2018-02-02 00:00:00

In this paper, we propose a multi-generator extension to the adversarial
training framework, in which the objective of each generator is to represent a
unique component of a target mixture distribution. In the training phase, the
generators cooperate to represent, as a mixture, the target distribution while
maintaining distinct manifolds. As opposed to traditional generative models,
inference from a particular generator after training resembles selective
sampling from a unique component in the target distribution. We demonstrate the
feasibility of the proposed architecture both analytically and with basic
Multi-Layer Perceptron (MLP) models trained on the MNIST dataset.
Bayesian Renewables Scenario Generation via Deep Generative Networks
Authors: Yize Chen, Pan Li, Baosen Zhang
Date: 2018-02-02 00:00:00
Tasks: Diversity, Generative Adversarial Network

We present a method to generate renewable scenarios using Bayesian
probabilities by implementing the Bayesian generative adversarial
network~(Bayesian GAN), which is a variant of generative adversarial networks
based on two interconnected deep neural networks. By using a Bayesian
formulation, generators can be constructed and trained to produce scenarios
that capture different salient modes in the data, allowing for better diversity
and more accurate representation of the underlying physical process. Compared
to conventional statistical models that are often hard to scale or sample from,
this method is model-free and can generate samples extremely efficiently. For
validation, we use wind and solar times-series data from NREL integration data
sets to train the Bayesian GAN. We demonstrate that proposed method is able to
generate clusters of wind scenarios with different variance and mean value, and
is able to distinguish and generate wind and solar scenarios simultaneously
even if the historical data are intentionally mixed.
End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception
Authors: Zhengyuan Yang, Yixuan Zhang, Jerry Yu, Junjie Cai, Jiebo Luo
Date: 2018-01-20 00:00:00
Tasks: Autonomous Driving, Multi-Task Learning, Self-Driving Cars, Steering Control

Convolutional Neural Networks (CNN) have been successfully applied to
autonomous driving tasks, many in an end-to-end manner. Previous end-to-end
steering control methods take an image or an image sequence as the input and
directly predict the steering angle with CNN. Although single task learning on
steering angles has reported good performances, the steering angle alone is not
sufficient for vehicle control. In this work, we propose a multi-task learning
framework to predict the steering angle and speed control simultaneously in an
end-to-end manner. Since it is nontrivial to predict accurate speed values with
only visual inputs, we first propose a network to predict discrete speed
commands and steering angles with image sequences. Moreover, we propose a
multi-modal multi-task network to predict speed values and steering angles by
taking previous feedback speeds and visual recordings as inputs. Experiments
are conducted on the public Udacity dataset and a newly collected SAIC dataset.
Results show that the proposed model predicts steering angles and speed values
accurately. Furthermore, we improve the failure data synthesis methods to solve
the problem of error accumulation in real road tests.
Query2Vec: An Evaluation of NLP Techniques for Generalized Workload Analytics
Authors: Shrainik Jain, Bill Howe, Jiaqi Yan, Thierry Cruanes
Date: 2018-01-17 00:00:00
Tasks: Feature Engineering, Transfer Learning

We consider methods for learning vector representations of SQL queries to
support generalized workload analytics tasks, including workload summarization
for index selection and predicting queries that will trigger memory errors. We
consider vector representations of both raw SQL text and optimized query plans,
and evaluate these methods on synthetic and real SQL workloads. We find that
general algorithms based on vector representations can outperform existing
approaches that rely on specialized features. For index recommendation, we
cluster the vector representations to compress large workloads with no loss in
performance from the recommended index. For error prediction, we train a
classifier over learned vectors that can automatically relate subtle syntactic
patterns with specific errors raised during query execution. Surprisingly, we
also find that these methods enable transfer learning, where a model trained on
one SQL corpus can be applied to an unrelated corpus and still enable good
performance. We find that these general approaches, when trained on a large
corpus of SQL queries, provides a robust foundation for a variety of workload
analysis tasks and database features, without requiring application-specific
feature engineering.