pwc-archive/papers-with-abstracts huggingface dataset:
Balanced Sparsity for Efficient DNN Inference on GPU
Authors: Zhuliang Yao, Shijie Cao, Wencong Xiao, Chen Zhang, Lanshun Nie
Date: 2018-11-01 00:00:00
Tasks: GPU

In trained deep neural networks, unstructured pruning can reduce redundant
weights to lower storage cost. However, it requires the customization of
hardwares to speed up practical inference. Another trend accelerates sparse
model inference on general-purpose hardwares by adopting coarse-grained
sparsity to prune or regularize consecutive weights for efficient computation.
But this method often sacrifices model accuracy. In this paper, we propose a
novel fine-grained sparsity approach, balanced sparsity, to achieve high model
accuracy with commercial hardwares efficiently. Our approach adapts to high
parallelism property of GPU, showing incredible potential for sparsity in the
widely deployment of deep learning services. Experiment results show that
balanced sparsity achieves up to 3.1x practical speedup for model inference on
GPU, while retains the same high model accuracy as fine-grained sparsity.
Attention-Aware Generalized Mean Pooling for Image Retrieval
Authors: Yinzheng Gu, Chuanpeng Li, Jinbin Xie
Date: 2018-11-01 00:00:00
Tasks: Image Retrieval, Retrieval

It has been shown that image descriptors extracted by convolutional neural
networks (CNNs) achieve remarkable results for retrieval problems. In this
paper, we apply attention mechanism to CNN, which aims at enhancing more
relevant features that correspond to important keypoints in the input image.
The generated attention-aware features are then aggregated by the previous
state-of-the-art generalized mean (GeM) pooling followed by normalization to
produce a compact global descriptor, which can be efficiently compared to other
image descriptors by the dot product. An extensive comparison of our proposed
approach with state-of-the-art methods is performed on the new challenging
ROxford5k and RParis6k retrieval benchmarks. Results indicate significant
improvement over previous work. In particular, our attention-aware GeM (AGeM)
descriptor outperforms state-of-the-art method on ROxford5k under the `Hard'
evaluation protocal.
Cogni-Net: Cognitive Feature Learning through Deep Visual Perception
Authors: Pranay Mukherjee, Abhirup Das, Ayan Kumar Bhunia, Partha Pratim Roy
Date: 2018-11-01 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Knowledge Distillation, Transfer Learning

Can we ask computers to recognize what we see from brain signals alone? Our
paper seeks to utilize the knowledge learnt in the visual domain by popular
pre-trained vision models and use it to teach a recurrent model being trained
on brain signals to learn a discriminative manifold of the human brain's
cognition of different visual object categories in response to perceived visual
cues. For this we make use of brain EEG signals triggered from visual stimuli
like images and leverage the natural synchronization between images and their
corresponding brain signals to learn a novel representation of the cognitive
feature space. The concept of knowledge distillation has been used here for
training the deep cognition model, CogniNet\footnote{The source code of the
proposed system is publicly available at
{https://www.github.com/53X/CogniNET}}, by employing a student-teacher learning
technique in order to bridge the process of inter-modal knowledge transfer. The
proposed novel architecture obtains state-of-the-art results, significantly
surpassing other existing models. The experiments performed by us also suggest
that if visual stimuli information like brain EEG signals can be gathered on a
large scale, then that would help to obtain a better understanding of the
largely unexplored domain of human brain cognition.
Online Learning Algorithms for Statistical Arbitrage
Authors: Christopher Mohri
Date: 2018-11-01 00:00:00

Statistical arbitrage is a class of financial trading strategies using mean
reversion models. The corresponding techniques rely on a number of assumptions
which may not hold for general non-stationary stochastic processes. This paper
presents an alternative technique for statistical arbitrage based on online
learning which does not require such assumptions and which benefits from strong
learning guarantees.
MOHONE: Modeling Higher Order Network Effects in KnowledgeGraphs via Network Infused Embeddings
Authors: Hao Yu, Vivek Kulkarni, William Wang
Date: 2018-11-01 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graph Embeddings, Knowledge Graphs, Link Prediction

Many knowledge graph embedding methods operate on triples and are therefore
implicitly limited by a very local view of the entire knowledge graph. We
present a new framework MOHONE to effectively model higher order network
effects in knowledge-graphs, thus enabling one to capture varying degrees of
network connectivity (from the local to the global). Our framework is generic,
explicitly models the network scale, and captures two different aspects of
similarity in networks: (a) shared local neighborhood and (b) structural
role-based similarity. First, we introduce methods that learn network
representations of entities in the knowledge graph capturing these varied
aspects of similarity. We then propose a fast, efficient method to incorporate
the information captured by these network representations into existing
knowledge graph embeddings. We show that our method consistently and
significantly improves the performance on link prediction of several different
knowledge-graph embedding methods including TRANSE, TRANSD, DISTMULT, and
COMPLEX(by at least 4 points or 17% in some cases).
Towards Explainable NLP: A Generative Explanation Framework for Text Classification
Authors: Hui Liu, Qingyu Yin, William Yang Wang
Date: 2018-11-01 00:00:00
Proceeding: ACL 2019 7
Tasks: BIG-bench Machine Learning, Classification, General Classification, text-classification, Text Classification

Building explainable systems is a critical problem in the field of Natural Language Processing (NLP), since most machine learning models provide no explanations for the predictions. Existing approaches for explainable machine learning systems tend to focus on interpreting the outputs or the connections between inputs and outputs. However, the fine-grained information is often ignored, and the systems do not explicitly generate the human-readable explanations. To better alleviate this problem, we propose a novel generative explanation framework that learns to make classification decisions and generate fine-grained explanations at the same time. More specifically, we introduce the explainable factor and the minimum risk training approach that learn to generate more reasonable explanations. We construct two new datasets that contain summaries, rating scores, and fine-grained reasons. We conduct experiments on both datasets, comparing with several strong neural network baseline systems. Experimental results show that our method surpasses all baselines on both datasets, and is able to generate concise explanations at the same time.
Unauthorized AI cannot Recognize Me: Reversible Adversarial Example
Authors: Jiayang Liu, Weiming Zhang, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma
Date: 2018-11-01 00:00:00
Tasks: Adversarial Attack, BIG-bench Machine Learning, General Classification, Image Classification, Speech Recognition

In this study, we propose a new methodology to control how user's data is recognized and used by AI via exploiting the properties of adversarial examples. For this purpose, we propose reversible adversarial example (RAE), a new type of adversarial example. A remarkable feature of RAE is that the image can be correctly recognized and used by the AI model specified by the user because the authorized AI can recover the original image from the RAE exactly by eliminating adversarial perturbation. On the other hand, other unauthorized AI models cannot recognize it correctly because it functions as an adversarial example. Moreover, RAE can be considered as one type of encryption to computer vision since reversibility guarantees the decryption. To realize RAE, we combine three technologies, adversarial example, reversible data hiding for exact recovery of adversarial perturbation, and encryption for selective control of AIs who can remove adversarial perturbation. Experimental results show that the proposed method can achieve comparable attack ability with the corresponding adversarial attack method and similar visual quality with the original image, including white-box attacks and black-box attacks.
Dial2Desc: End-to-end Dialogue Description Generation
Authors: Haojie Pan, Junpei Zhou, Zhou Zhao, Yan Liu, Deng Cai, Min Yang
Date: 2018-11-01 00:00:00
Tasks: Descriptive, Meeting Summarization

We first propose a new task named Dialogue Description (Dial2Desc). Unlike
other existing dialogue summarization tasks such as meeting summarization, we
do not maintain the natural flow of a conversation but describe an object or an
action of what people are talking about. The Dial2Desc system takes a dialogue
text as input, then outputs a concise description of the object or the action
involved in this conversation. After reading this short description, one can
quickly extract the main topic of a conversation and build a clear picture in
his mind, without reading or listening to the whole conversation. Based on the
existing dialogue dataset, we build a new dataset, which has more than one
hundred thousand dialogue-description pairs. As a step forward, we demonstrate
that one can get more accurate and descriptive results using a new neural
attentive model that exploits the interaction between utterances from different
speakers, compared with other baselines.
Designing an Effective Metric Learning Pipeline for Speaker Diarization
Authors: Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Huan Song, Andreas Spanias
Date: 2018-11-01 00:00:00
Tasks: Metric Learning, speaker-diarization, Speaker Diarization

State-of-the-art speaker diarization systems utilize knowledge from external
data, in the form of a pre-trained distance metric, to effectively determine
relative speaker identities to unseen data. However, much of recent focus has
been on choosing the appropriate feature extractor, ranging from pre-trained
$i-$vectors to representations learned via different sequence modeling
architectures (e.g. 1D-CNNs, LSTMs, attention models), while adopting
off-the-shelf metric learning solutions. In this paper, we argue that,
regardless of the feature extractor, it is crucial to carefully design a metric
learning pipeline, namely the loss function, the sampling strategy and the
discrimnative margin parameter, for building robust diarization systems.
Furthermore, we propose to adopt a fine-grained validation process to obtain a
comprehensive evaluation of the generalization power of metric learning
pipelines. To this end, we measure diarization performance across different
language speakers, and variations in the number of speakers in a recording.
Using empirical studies, we provide interesting insights into the effectiveness
of different design choices and make recommendations.
A Regularized Attention Mechanism for Graph Attention Networks
Authors: Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Andreas Spanias
Date: 2018-11-01 00:00:00
Tasks: Graph Attention

Machine learning models that can exploit the inherent structure in data have gained prominence. In particular, there is a surge in deep learning solutions for graph-structured data, due to its wide-spread applicability in several fields. Graph attention networks (GAT), a recent addition to the broad class of feature learning models in graphs, utilizes the attention mechanism to efficiently learn continuous vector representations for semi-supervised learning problems. In this paper, we perform a detailed analysis of GAT models, and present interesting insights into their behavior. In particular, we show that the models are vulnerable to heterogeneous rogue nodes and hence propose novel regularization strategies to improve the robustness of GAT models. Using benchmark datasets, we demonstrate performance improvements on semi-supervised learning, using the proposed robust variant of GAT.
Pixel Level Data Augmentation for Semantic Image Segmentation using Generative Adversarial Networks
Authors: Shuangting Liu, Jia-Qi Zhang, Yuxin Chen, Yifan Liu, Zengchang Qin, Tao Wan
Date: 2018-11-01 00:00:00
Tasks: Data Augmentation, Image Segmentation, Segmentation, Semantic Segmentation

Semantic segmentation is one of the basic topics in computer vision, it aims to assign semantic labels to every pixel of an image. Unbalanced semantic label distribution could have a negative influence on segmentation accuracy. In this paper, we investigate using data augmentation approach to balance the semantic label distribution in order to improve segmentation performance. We propose using generative adversarial networks (GANs) to generate realistic images for improving the performance of semantic segmentation networks. Experimental results show that the proposed method can not only improve segmentation performance on those classes with low accuracy, but also obtain 1.3% to 2.1% increase in average segmentation accuracy. It shows that this augmentation method can boost accuracy and be easily applicable to any other segmentation models.
PerceptionNet: A Deep Convolutional Neural Network for Late Sensor Fusion
Authors: Panagiotis Kasnesis, Charalampos Z. Patrikakis, Iakovos S. Venieris
Date: 2018-11-01 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Sensor Fusion, Time Series, Time Series Analysis

Human Activity Recognition (HAR) based on motion sensors has drawn a lot of
attention over the last few years, since perceiving the human status enables
context-aware applications to adapt their services on users' needs. However,
motion sensor fusion and feature extraction have not reached their full
potentials, remaining still an open issue. In this paper, we introduce
PerceptionNet, a deep Convolutional Neural Network (CNN) that applies a late 2D
convolution to multimodal time-series sensor data, in order to extract
automatically efficient features for HAR. We evaluate our approach on two
public available HAR datasets to demonstrate that the proposed model fuses
effectively multimodal sensors and improves the performance of HAR. In
particular, PerceptionNet surpasses the performance of state-of-the-art HAR
methods based on: (i) features extracted from humans, (ii) deep CNNs exploiting
early fusion approaches, and (iii) Long Short-Term Memory (LSTM), by an average
accuracy of more than 3%.
Deep Counterfactual Regret Minimization
Authors: Noam Brown, Adam Lerer, Sam Gross, Tuomas Sandholm
Date: 2018-11-01 00:00:00
Tasks: counterfactual

Counterfactual Regret Minimization (CFR) is the leading framework for solving large imperfect-information games. It converges to an equilibrium by iteratively traversing the game tree. In order to deal with extremely large games, abstraction is typically applied before running CFR. The abstracted game is solved with tabular CFR, and its solution is mapped back to the full game. This process can be problematic because aspects of abstraction are often manual and domain specific, abstraction algorithms may miss important strategic nuances of the game, and there is a chicken-and-egg problem because determining a good abstraction requires knowledge of the equilibrium of the game. This paper introduces Deep Counterfactual Regret Minimization, a form of CFR that obviates the need for abstraction by instead using deep neural networks to approximate the behavior of CFR in the full game. We show that Deep CFR is principled and achieves strong performance in large poker games. This is the first non-tabular variant of CFR to be successful in large games.
Modeling Melodic Feature Dependency with Modularized Variational Auto-Encoder
Authors: Yu-An Wang, Yu-Kai Huang, Tzu-Chuan Lin, Shang-Yu Su, Yun-Nung Chen
Date: 2018-10-31 00:00:00

Automatic melody generation has been a long-time aspiration for both AI
researchers and musicians. However, learning to generate euphonious melodies
has turned out to be highly challenging. This paper introduces 1) a new variant
of variational autoencoder (VAE), where the model structure is designed in a
modularized manner in order to model polyphonic and dynamic music with domain
knowledge, and 2) a hierarchical encoding/decoding strategy, which explicitly
models the dependency between melodic features. The proposed framework is
capable of generating distinct melodies that sounds natural, and the
experiments for evaluating generated music clips show that the proposed model
outperforms the baselines in human evaluation.
Conceptual Content in Deep Convolutional Neural Networks: An analysis into multi-faceted properties of neurons
Authors: Zahra Sadeghi
Date: 2018-10-31 00:00:00
Tasks: Diversity

In this paper, convolutional layers of pre-trained VGG16 model are analyzed. The analysis is based on the responses of neurons to the images of classes in ImageNet database. First, a visualization method is proposed in order to illustrate the learned content of each neuron. Next, single and multi-faceted neurons are investigated based on the diversity of neurons responses to different category of objects. Finally, neuronal similarities at each layer are computed and compared. The results demonstrate that the neurons in lower layers exhibit a multi-faceted behavior, whereas the majority of neurons in higher layers com-prise single-faceted property and tend to respond to a smaller number of concepts.
Clustered Monotone Transforms for Rating Factorization
Authors: Gaurush Hiranandani, Raghav Somani, Oluwasanmi Koyejo, Sreangsu Acharyya
Date: 2018-10-31 00:00:00
Tasks: Recommendation Systems, regression

Exploiting low-rank structure of the user-item rating matrix has been the
crux of many recommendation engines. However, existing recommendation engines
force raters with heterogeneous behavior profiles to map their intrinsic rating
scales to a common rating scale (e.g. 1-5). This non-linear transformation of
the rating scale shatters the low-rank structure of the rating matrix,
therefore resulting in a poor fit and consequentially, poor recommendations. In
this paper, we propose Clustered Monotone Transforms for Rating Factorization
(CMTRF), a novel approach to perform regression up to unknown monotonic
transforms over unknown population segments. Essentially, for recommendation
systems, the technique searches for monotonic transformations of the rating
scales resulting in a better fit. This is combined with an underlying matrix
factorization regression model that couples the user-wise ratings to exploit
shared low dimensional structure. The rating scale transformations can be
generated for each user, for a cluster of users, or for all the users at once,
forming the basis of three simple and efficient algorithms proposed in this
paper, all of which alternate between transformation of the rating scales and
matrix factorization regression. Despite the non-convexity, CMTRF is
theoretically shown to recover a unique solution under mild conditions.
Experimental results on two synthetic and seven real-world datasets show that
CMTRF outperforms other state-of-the-art baselines.
Low-Precision Random Fourier Features for Memory-Constrained Kernel Approximation
Authors: Jian Zhang, Avner May, Tri Dao, Christopher Ré
Date: 2018-10-31 00:00:00
Tasks: Quantization

We investigate how to train kernel approximation methods that generalize well
under a memory budget. Building on recent theoretical work, we define a measure
of kernel approximation error which we find to be more predictive of the
empirical generalization performance of kernel approximation methods than
conventional metrics. An important consequence of this definition is that a
kernel approximation matrix must be high rank to attain close approximation.
Because storing a high-rank approximation is memory intensive, we propose using
a low-precision quantization of random Fourier features (LP-RFFs) to build a
high-rank approximation under a memory budget. Theoretically, we show
quantization has a negligible effect on generalization performance in important
settings. Empirically, we demonstrate across four benchmark datasets that
LP-RFFs can match the performance of full-precision RFFs and the Nystr\"{o}m
method, with 3x-10x and 50x-460x less memory, respectively.
Mixture Density Generative Adversarial Networks
Authors: Hamid Eghbal-zadeh, Werner Zellinger, Gerhard Widmer
Date: 2018-10-31 00:00:00
Proceeding: CVPR 2019 6

Generative Adversarial Networks have surprising ability for generating sharp
and realistic images, though they are known to suffer from the so-called mode
collapse problem. In this paper, we propose a new GAN variant called Mixture
Density GAN that while being capable of generating high-quality images,
overcomes this problem by encouraging the Discriminator to form clusters in its
embedding space, which in turn leads the Generator to exploit these and
discover different modes in the data. This is achieved by positioning Gaussian
density functions in the corners of a simplex, using the resulting Gaussian
mixture as a likelihood function over discriminator embeddings, and formulating
an objective function for GAN training that is based on these likelihoods. We
demonstrate empirically (1) the quality of the generated images in Mixture
Density GAN and their strong similarity to real images, as measured by the
Fr\'echet Inception Distance (FID), which compares very favourably with
state-of-the-art methods, and (2) the ability to avoid mode collapse and
discover all data modes.
Recovery Guarantees for Quadratic Tensors with Sparse Observations
Authors: Hongyang R. Zhang, Vatsal Sharan, Moses Charikar, YIngyu Liang
Date: 2018-10-31 00:00:00
Tasks: Recommendation Systems

We consider the tensor completion problem of predicting the missing entries of a tensor. The commonly used CP model has a triple product form, but an alternate family of quadratic models, which are the sum of pairwise products instead of a triple product, have emerged from applications such as recommendation systems. Non-convex methods are the method of choice for learning quadratic models, and this work examines their sample complexity and error guarantee. Our main result is that with the number of samples being only linear in the dimension, all local minima of the mean squared error objective are global minima and recover the original tensor. We substantiate our theoretical results with experiments on synthetic and real-world data, showing that quadratic models have better performance than CP models where there are a limited amount of observations available.
DOLORES: Deep Contextualized Knowledge Graph Embeddings
Authors: Haoyu Wang, Vivek Kulkarni, William Yang Wang
Date: 2018-10-31 00:00:00
Proceeding: AKBC 2020 6
Tasks: Knowledge Graph Embeddings, Knowledge Graphs, Link Prediction, Prediction, Relation, Triple Classification, Type prediction

We introduce a new method DOLORES for learning knowledge graph embeddings
that effectively captures contextual cues and dependencies among entities and
relations. First, we note that short paths on knowledge graphs comprising of
chains of entities and relations can encode valuable information regarding
their contextual usage. We operationalize this notion by representing knowledge
graphs not as a collection of triples but as a collection of entity-relation
chains, and learn embeddings for entities and relations using deep neural
models that capture such contextual usage. In particular, our model is based on
Bi-Directional LSTMs and learn deep representations of entities and relations
from constructed entity-relation chains. We show that these representations can
very easily be incorporated into existing models to significantly advance the
state of the art on several knowledge graph prediction tasks like link
prediction, triple classification, and missing relation type prediction (in
some cases by at least 9.5%).