pwc-archive/papers-with-abstracts huggingface dataset:
Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms
Authors: Jianyu Wang, Gauri Joshi
Date: 2018-08-22 00:00:00

Communication-efficient SGD algorithms, which allow nodes to perform local
updates and periodically synchronize local models, are highly effective in
improving the speed and scalability of distributed SGD. However, a rigorous
convergence analysis and comparative study of different communication-reduction
strategies remains a largely open problem. This paper presents a unified
framework called Cooperative SGD that subsumes existing communication-efficient
SGD algorithms such as periodic-averaging, elastic-averaging and decentralized
SGD. By analyzing Cooperative SGD, we provide novel convergence guarantees for
existing algorithms. Moreover, this framework enables us to design new
communication-efficient SGD algorithms that strike the best balance between
reducing communication overhead and achieving fast error convergence with low
error floor.
Approximation Trees: Statistical Stability in Model Distillation
Authors: Yichen Zhou, Zhengze Zhou, Giles Hooker
Date: 2018-08-22 00:00:00
Tasks: model

This paper examines the stability of learned explanations for black-box
predictions via model distillation with decision trees. One approach to
intelligibility in machine learning is to use an understandable `student' model
to mimic the output of an accurate `teacher'. Here, we consider the use of
regression trees as a student model, in which nodes of the tree can be used as
`explanations' for particular predictions, and the whole structure of the tree
can be used as a global representation of the resulting function. However,
individual trees are sensitive to the particular data sets used to train them,
and an interpretation of a student model may be suspect if small changes in the
training data have a large effect on it. In this context, access to outcomes
from a teacher helps to stabilize the greedy splitting strategy by generating a
much larger corpus of training examples than was originally available. We
develop tests to ensure that enough examples are generated at each split so
that the same splitting rule would be chosen with high probability were the
tree to be re trained. Further, we develop a stopping rule to indicate how deep
the tree should be built based on recent results on the variability of Random
Forests when these are used as the teacher. We provide concrete examples of
these procedures on the CAD-MDD and COMPAS data sets.
Robust Counterfactual Inferences using Feature Learning and their Applications
Authors: Abhimanyu Mitra, Kannan Achan, Sushant Kumar
Date: 2018-08-22 00:00:00
Tasks: counterfactual, Counterfactual Inference

In a wide variety of applications, including personalization, we want to
measure the difference in outcome due to an intervention and thus have to deal
with counterfactual inference. The feedback from a customer in any of these
situations is only 'bandit feedback' - that is, a partial feedback based on
whether we chose to intervene or not. Typically randomized experiments are
carried out to understand whether an intervention is overall better than no
intervention. Here we present a feature learning algorithm to learn from a
randomized experiment where the intervention in consideration is most effective
and where it is least effective rather than only focusing on the overall
impact, thus adding a context to our learning mechanism and extract more
information. From the randomized experiment, we learn the feature
representations which divide the population into subpopulations where we
observe statistically significant difference in average customer feedback
between those who were subjected to the intervention and those who were not,
with a level of significance l, where l is a configurable parameter in our
model. We use this information to derive the value of the intervention in
consideration for each instance in the population. With experiments, we show
that using this additional learning, in future interventions, the context for
each instance could be leveraged to decide whether to intervene or not.
Training Deeper Neural Machine Translation Models with Transparent Attention
Authors: Ankur Bapna, Mia Xu Chen, Orhan Firat, Yuan Cao, Yonghui Wu
Date: 2018-08-22 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, NMT, Translation

While current state-of-the-art NMT models, such as RNN seq2seq and
Transformers, possess a large number of parameters, they are still shallow in
comparison to convolutional models used for both text and vision applications.
In this work we attempt to train significantly (2-3x) deeper Transformer and
Bi-RNN encoders for machine translation. We propose a simple modification to
the attention mechanism that eases the optimization of deeper models, and
results in consistent gains of 0.7-1.1 BLEU on the benchmark WMT'14
English-German and WMT'15 Czech-English tasks for both architectures.
Learning Hierarchical Semantic Image Manipulation through Structured Representations
Authors: Seunghoon Hong, Xinchen Yan, Thomas Huang, Honglak Lee
Date: 2018-08-22 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Image Generation, Image Manipulation, Object

Understanding, reasoning, and manipulating semantic concepts of images have
been a fundamental research problem for decades. Previous work mainly focused
on direct manipulation on natural image manifold through color strokes,
key-points, textures, and holes-to-fill. In this work, we present a novel
hierarchical framework for semantic image manipulation. Key to our hierarchical
framework is that we employ a structured semantic layout as our intermediate
representation for manipulation. Initialized with coarse-level bounding boxes,
our structure generator first creates pixel-wise semantic layout capturing the
object shape, object-object interactions, and object-scene relations. Then our
image generator fills in the pixel-level textures guided by the semantic
layout. Such framework allows a user to manipulate images at object-level by
adding, removing, and moving one bounding box at a time. Experimental
evaluations demonstrate the advantages of the hierarchical manipulation
framework over existing image generation and context hole-filing models, both
qualitatively and quantitatively. Benefits of the hierarchical framework are
further demonstrated in applications such as semantic object manipulation,
interactive image editing, and data-driven image manipulation.
Sarcasm Analysis using Conversation Context
Authors: Debanjan Ghosh, Alexander R. Fabbri, Smaranda Muresan
Date: 2018-08-22 00:00:00
Proceeding: CL 2018 12
Tasks: Sarcasm Detection, Sentence

Computational models for sarcasm detection have often relied on the content
of utterances in isolation. However, the speaker's sarcastic intent is not
always apparent without additional context. Focusing on social media
discussions, we investigate three issues: (1) does modeling conversation
context help in sarcasm detection; (2) can we identify what part of
conversation context triggered the sarcastic reply; and (3) given a sarcastic
post that contains multiple sentences, can we identify the specific sentence
that is sarcastic. To address the first issue, we investigate several types of
Long Short-Term Memory (LSTM) networks that can model both the conversation
context and the current turn. We show that LSTM networks with sentence-level
attention on context and current turn, as well as the conditional LSTM network
(Rocktaschel et al. 2016), outperform the LSTM model that reads only the
current turn. As conversation context, we consider the prior turn, the
succeeding turn or both. Our computational models are tested on two types of
social media platforms: Twitter and discussion forums. We discuss several
differences between these datasets ranging from their size to the nature of the
gold-label annotations. To address the last two issues, we present a
qualitative analysis of attention weights produced by the LSTM models (with
attention) and discuss the results compared with human performance on the two
tasks.
An Overview of Datatype Quantization Techniques for Convolutional Neural Networks
Authors: Ali Athar
Date: 2018-08-22 00:00:00
Tasks: Quantization

Convolutional Neural Networks (CNNs) are becoming increasingly popular due to
their superior performance in the domain of computer vision, in applications
such as objection detection and recognition. However, they demand complex,
power-consuming hardware which makes them unsuitable for implementation on
low-power mobile and embedded devices. In this paper, a description and
comparison of various techniques is presented which aim to mitigate this
problem. This is primarily achieved by quantizing the floating-point weights
and activations to reduce the hardware requirements, and adapting the training
and inference algorithms to maintain the network's performance.
Rethinking Monocular Depth Estimation with Adversarial Training
Authors: Richard Chen, Faisal Mahmood, Alan Yuille, Nicholas J. Durr
Date: 2018-08-22 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation

Monocular depth estimation is an extensively studied computer vision problem with a vast variety of applications. Deep learning-based methods have demonstrated promise for both supervised and unsupervised depth estimation from monocular images. Most existing approaches treat depth estimation as a regression problem with a local pixel-wise loss function. In this work, we innovate beyond existing approaches by using adversarial training to learn a context-aware, non-local loss function. Such an approach penalizes the joint configuration of predicted depth values at the patch-level instead of the pixel-level, which allows networks to incorporate more global information. In this framework, the generator learns a mapping between RGB images and its corresponding depth map, while the discriminator learns to distinguish depth map and RGB pairs from ground truth. This conditional GAN depth estimation framework is stabilized using spectral normalization to prevent mode collapse when learning from diverse datasets. We test this approach using a diverse set of generators that include U-Net and joint CNN-CRF. We benchmark this approach on the NYUv2, Make3D and KITTI datasets, and observe that adversarial training reduces relative error by several fold, achieving state-of-the-art performance.
Vehicles Lane-changing Behavior Detection
Authors: Iljoo Baek, Mengwen He
Date: 2018-08-22 00:00:00
Tasks: Autonomous Vehicles, Dimensionality Reduction

The lane-level localization accuracy is very important for autonomous
vehicles. The Global Navigation Satellite System (GNSS), e.g. GPS, is a generic
localization method for vehicles, but is vulnerable to the multi-path
interference in the urban environment. Integrating the vision-based relative
localization result and a digital map with the GNSS is a common and cheap way
to increase the global localization accuracy and thus to realize the lane-level
localization. This project is to develop a mono-camera based lane-changing
behavior detection module for the correction of lateral GPS localization. We
implemented a Support Vector Machine (SVM) based framework to directly classify
the driving behavior, including the lane keeping, left and right lane changing,
from a sampled data of the raw image captured by the mono-camera installed
behind the window shield. The training data was collected from the driving
around Carnegie Mellon University, and we compared the trained SVM models w/
and w/o the Principle Component Analysis (PCA) dimension reduction technique.
The performance of the SVM based classification method was compared with the
CNN method.
SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation
Authors: Xinyi Wang, Hieu Pham, Zihang Dai, Graham Neubig
Date: 2018-08-22 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Data Augmentation, Machine Translation, NMT, Sentence, Translation

In this work, we examine methods for data augmentation for text-based tasks
such as neural machine translation (NMT). We formulate the design of a data
augmentation policy with desirable properties as an optimization problem, and
derive a generic analytic solution. This solution not only subsumes some
existing augmentation schemes, but also leads to an extremely simple data
augmentation strategy for NMT: randomly replacing words in both the source
sentence and the target sentence with other random words from their
corresponding vocabularies. We name this method SwitchOut. Experiments on three
translation datasets of different scales show that SwitchOut yields consistent
improvements of about 0.5 BLEU, achieving better or comparable performances to
strong alternatives such as word dropout (Sennrich et al., 2016a). Code to
implement this method is included in the appendix.
XPCA: Extending PCA for a Combination of Discrete and Continuous Variables
Authors: Clifford Anderson-Bergman, Tamara G. Kolda, Kina Kincher-Winoto
Date: 2018-08-22 00:00:00
Tasks: Dimensionality Reduction

Principal component analysis (PCA) is arguably the most popular tool in
multivariate exploratory data analysis. In this paper, we consider the question
of how to handle heterogeneous variables that include continuous, binary, and
ordinal. In the probabilistic interpretation of low-rank PCA, the data has a
normal multivariate distribution and, therefore, normal marginal distributions
for each column. If some marginals are continuous but not normal, the
semiparametric copula-based principal component analysis (COCA) method is an
alternative to PCA that combines a Gaussian copula with nonparametric
marginals. If some marginals are discrete or semi-continuous, we propose a new
extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric
marginals and accounts for discrete variables in the likelihood calculation by
integrating over appropriate intervals. Like PCA, the factors produced by XPCA
can be used to find latent structure in data, build predictive models, and
perform dimensionality reduction. We present the new model, its induced
likelihood function, and a fitting algorithm which can be applied in the
presence of missing data. We demonstrate how to use XPCA to produce an
estimated full conditional distribution for each data point, and use this to
produce to provide estimates for missing data that are automatically range
respecting. We compare the methods as applied to simulated and real-world data
sets that have a mixture of discrete and continuous variables.
Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition
Authors: Unaiza Ahsan, Rishi Madhok, Irfan Essa
Date: 2018-08-22 00:00:00
Tasks: Action Recognition, Activity Recognition, Optical Flow Estimation, Position, Self-Supervised Learning, Temporal Action Localization, Transfer Learning, Video Recognition

We propose a self-supervised learning method to jointly reason about spatial
and temporal context for video recognition. Recent self-supervised approaches
have used spatial context [9, 34] as well as temporal coherency [32] but a
combination of the two requires extensive preprocessing such as tracking
objects through millions of video frames [59] or computing optical flow to
determine frame regions with high motion [30]. We propose to combine spatial
and temporal context in one self-supervised framework without any heavy
preprocessing. We divide multiple video frames into grids of patches and train
a network to solve jigsaw puzzles on these patches from multiple frames. So the
network is trained to correctly identify the position of a patch within a video
frame as well as the position of a patch over time. We also propose a novel
permutation strategy that outperforms random permutations while significantly
reducing computational and memory constraints. We use our trained network for
transfer learning tasks such as video activity recognition and demonstrate the
strength of our approach on two benchmark video action recognition datasets
without using a single frame from these datasets for unsupervised pretraining
of our proposed video jigsaw network.
Second-order Democratic Aggregation
Authors: Tsung-Yu Lin, Subhransu Maji, Piotr Koniusz
Date: 2018-08-22 00:00:00
Proceeding: ECCV 2018 9
Tasks: General Classification, Material Classification, Scene Understanding, Texture Synthesis

Aggregated second-order features extracted from deep convolutional networks
have been shown to be effective for texture generation, fine-grained
recognition, material classification, and scene understanding. In this paper,
we study a class of orderless aggregation functions designed to minimize
interference or equalize contributions in the context of second-order features
and we show that they can be computed just as efficiently as their first-order
counterparts and they have favorable properties over aggregation by summation.
Another line of work has shown that matrix power normalization after
aggregation can significantly improve the generalization of second-order
representations. We show that matrix power normalization implicitly equalizes
contributions during aggregation thus establishing a connection between matrix
normalization techniques and prior work on minimizing interference. Based on
the analysis we present {\gamma}-democratic aggregators that interpolate
between sum ({\gamma}=1) and democratic pooling ({\gamma}=0) outperforming both
on several classification tasks. Moreover, unlike power normalization, the
{\gamma}-democratic aggregations can be computed in a low dimensional space by
sketching that allows the use of very high-dimensional second-order features.
This results in a state-of-the-art performance on several datasets.
Everybody Dance Now
Authors: Caroline Chan, Shiry Ginosar, Tinghui Zhou, Alexei A. Efros
Date: 2018-08-22 00:00:00
Proceeding: ICCV 2019 10
Tasks: Face Generation, Image-to-Image Translation, Video Generation

This paper presents a simple method for "do as I do" motion transfer: given a source video of a person dancing, we can transfer that performance to a novel (amateur) target after only a few minutes of the target subject performing standard moves. We approach this problem as video-to-video translation using pose as an intermediate representation. To transfer the motion, we extract poses from the source subject and apply the learned pose-to-appearance mapping to generate the target subject. We predict two consecutive frames for temporally coherent video results and introduce a separate pipeline for realistic face synthesis. Although our method is quite simple, it produces surprisingly compelling results (see video). This motivates us to also provide a forensics tool for reliable synthetic content detection, which is able to distinguish videos synthesized by our system from real data. In addition, we release a first-of-its-kind open-source dataset of videos that can be legally used for training and motion transfer.
Capsule Networks for Protein Structure Classification and Prediction
Authors: Dan Rosa de Jesus, Julian Cuevas, Wilson Rivera, Silvia Crivelli
Date: 2018-08-22 00:00:00
Tasks: Classification, General Classification, GPU, Prediction

Capsule Networks have great potential to tackle problems in structural
biology because of their attention to hierarchical relationships. This paper
describes the implementation and application of a Capsule Network architecture
to the classification of RAS protein family structures on GPU-based
computational resources. The proposed Capsule Network trained on 2D and 3D
structural encodings can successfully classify HRAS and KRAS structures. The
Capsule Network can also classify a protein-based dataset derived from a
PSI-BLAST search on sequences of KRAS and HRAS mutations. Our results show an
accuracy improvement compared to traditional convolutional networks, while
improving interpretability through visualization of activation vectors.
Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks
Authors: Yang He, Xuanyi Dong, Guoliang Kang, Yanwei Fu, Chenggang Yan, Yi Yang
Date: 2018-08-22 00:00:00
Tasks: image-classification, Image Classification

Deeper and wider Convolutional Neural Networks (CNNs) achieve superior performance but bring expensive computation cost. Accelerating such over-parameterized neural network has received increased attention. A typical pruning algorithm is a three-stage pipeline, i.e., training, pruning, and retraining. Prevailing approaches fix the pruned filters to zero during retraining, and thus significantly reduce the optimization space. Besides, they directly prune a large number of filters at first, which would cause unrecoverable information loss. To solve these problems, we propose an Asymptotic Soft Filter Pruning (ASFP) method to accelerate the inference procedure of the deep neural networks. First, we update the pruned filters during the retraining stage. As a result, the optimization space of the pruned model would not be reduced but be the same as that of the original model. In this way, the model has enough capacity to learn from the training data. Second, we prune the network asymptotically. We prune few filters at first and asymptotically prune more filters during the training procedure. With asymptotic pruning, the information of the training set would be gradually concentrated in the remaining filters, so the subsequent training and pruning process would be stable. Experiments show the effectiveness of our ASFP on image classification benchmarks. Notably, on ILSVRC-2012, our ASFP reduces more than 40% FLOPs on ResNet-50 with only 0.14% top-5 accuracy degradation, which is higher than the soft filter pruning (SFP) by 8%.
GraphLab: A New Framework for Parallel Machine Learning
Authors: Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin, Joseph M. Hellerstein
Date: 2010-06-25 00:00:00
Tasks: BIG-bench Machine Learning, compressed sensing

Designing and implementing efficient, provably correct parallel machine
learning (ML) algorithms is challenging. Existing high-level parallel
abstractions like MapReduce are insufficiently expressive while low-level tools
like MPI and Pthreads leave ML experts repeatedly solving the same design
challenges. By targeting common patterns in ML, we developed GraphLab, which
improves upon abstractions like MapReduce by compactly expressing asynchronous
iterative algorithms with sparse computational dependencies while ensuring data
consistency and achieving a high degree of parallel performance. We demonstrate
the expressiveness of the GraphLab framework by designing and implementing
parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and
Compressed Sensing. We show that using GraphLab we can achieve excellent
parallel performance on large scale real-world problems.
Recommender Systems
Authors: Linyuan Lü, Matus Medo, Chi Ho Yeung, Yi-Cheng Zhang, Zi-Ke Zhang, Tao Zhou
Date: 2012-02-06 00:00:00
Tasks: Recommendation Systems

The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.