pwc-archive/papers-with-abstracts huggingface dataset:
A Compromise Principle in Deep Monocular Depth Estimation
Authors: Huan Fu, Mingming Gong, Chaohui Wang, DaCheng Tao
Date: 2017-08-28 00:00:00
Tasks: Classification, Data Augmentation, Depth Estimation, General Classification, Monocular Depth Estimation, regression

Monocular depth estimation, which plays a key role in understanding 3D scene
geometry, is fundamentally an ill-posed problem. Existing methods based on deep
convolutional neural networks (DCNNs) have examined this problem by learning
convolutional networks to estimate continuous depth maps from monocular images.
However, we find that training a network to predict a high spatial resolution
continuous depth map often suffers from poor local solutions. In this paper, we
hypothesize that achieving a compromise between spatial and depth resolutions
can improve network training. Based on this "compromise principle", we propose
a regression-classification cascaded network (RCCN), which consists of a
regression branch predicting a low spatial resolution continuous depth map and
a classification branch predicting a high spatial resolution discrete depth
map. The two branches form a cascaded structure allowing the classification and
regression branches to benefit from each other. By leveraging large-scale raw
training datasets and some data augmentation strategies, our network achieves
top or state-of-the-art results on the NYU Depth V2, KITTI, and Make3D
benchmarks.
Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains
Authors: Ching-Yun Ko, Kim Batselier, Wenjian Yu, Ngai Wong
Date: 2018-04-17 00:00:00
Tasks: Video Inpainting

We propose a new tensor completion method based on tensor trains. The
to-be-completed tensor is modeled as a low-rank tensor train, where we use the
known tensor entries and their coordinates to update the tensor train. A novel
tensor train initialization procedure is proposed specifically for image and
video completion, which is demonstrated to ensure fast convergence of the
completion algorithm. The tensor train framework is also shown to easily
accommodate Total Variation and Tikhonov regularization due to their low-rank
tensor train representations. Image and video inpainting experiments verify the
superiority of the proposed scheme in terms of both speed and scalability,
where a speedup of up to 155X is observed compared to state-of-the-art tensor
completion methods at a similar accuracy. Moreover, we demonstrate the proposed
scheme is especially advantageous over existing algorithms when only tiny
portions (say, 1%) of the to-be-completed images/videos are known.
Small-loss bounds for online learning with partial information
Authors: Thodoris Lykouris, Karthik Sridharan, Eva Tardos
Date: 2017-11-09 00:00:00
Tasks: Multi-Armed Bandits

We consider the problem of adversarial (non-stochastic) online learning with partial information feedback, where at each round, a decision maker selects an action from a finite set of alternatives. We develop a black-box approach for such problems where the learner observes as feedback only losses of a subset of the actions that includes the selected action. When losses of actions are non-negative, under the graph-based feedback model introduced by Mannor and Shamir, we offer algorithms that attain the so called "small-loss" $o(\alpha L^{\star})$ regret bounds with high probability, where $\alpha$ is the independence number of the graph, and $L^{\star}$ is the loss of the best action. Prior to our work, there was no data-dependent guarantee for general feedback graphs even for pseudo-regret (without dependence on the number of actions, i.e. utilizing the increased information feedback). Taking advantage of the black-box nature of our technique, we extend our results to many other applications such as semi-bandits (including routing in networks), contextual bandits (even with an infinite comparator class), as well as learning with slowly changing (shifting) comparators. In the special case of classical bandit and semi-bandit problems, we provide optimal small-loss, high-probability guarantees of $\tilde{O}(\sqrt{dL^{\star}})$ for actual regret, where $d$ is the number of actions, answering open questions of Neu. Previous bounds for bandits and semi-bandits were known only for pseudo-regret and only in expectation. We also offer an optimal $\tilde{O}(\sqrt{\kappa L^{\star}})$ regret guarantee for fixed feedback graphs with clique-partition number at most $\kappa$.
Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks
Authors: Xue Yang, Hao Sun, Kun fu, Jirui Yang, Xian Sun, Menglong Yan, Zhi Guo
Date: 2018-06-12 00:00:00
Tasks: object-detection, Object Detection

Ship detection has been playing a significant role in the field of remote
sensing for a long time but it is still full of challenges. The main
limitations of traditional ship detection methods usually lie in the complexity
of application scenarios, the difficulty of intensive object detection and the
redundancy of detection region. In order to solve such problems above, we
propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN)
which can effectively detect ship in different scenes including ocean and port.
Specifically, we put forward the Dense Feature Pyramid Network (DFPN), which is
aimed at solving the problem resulted from the narrow width of the ship.
Compared with previous multi-scale detectors such as Feature Pyramid Network
(FPN), DFPN builds the high-level semantic feature-maps for all scales by means
of dense connections, through which enhances the feature propagation and
encourages the feature reuse. Additionally, in the case of ship rotation and
dense arrangement, we design a rotation anchor strategy to predict the minimum
circumscribed rectangle of the object so as to reduce the redundant detection
region and improve the recall. Furthermore, we also propose multi-scale ROI
Align for the purpose of maintaining the completeness of semantic and spatial
information. Experiments based on remote sensing images from Google Earth for
ship detection show that our detection method based on R-DFPN representation
has a state-of-the-art performance.
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering
Authors: Wuwei Lan, Wei Xu
Date: 2018-06-12 00:00:00
Proceeding: COLING 2018 8
Tasks: Natural Language Inference, Paraphrase Identification, Question Answering, Semantic Textual Similarity, Sentence, Sentence Pair Modeling

In this paper, we analyze several neural network designs (and their
variations) for sentence pair modeling and compare their performance
extensively across eight datasets, including paraphrase identification,
semantic textual similarity, natural language inference, and question answering
tasks. Although most of these models have claimed state-of-the-art performance,
the original papers often reported on only one or two selected datasets. We
provide a systematic study and show that (i) encoding contextual information by
LSTM and inter-sentence interactions are critical, (ii) Tree-LSTM does not help
as much as previously claimed but surprisingly improves performance on Twitter
datasets, (iii) the Enhanced Sequential Inference Model is the best so far for
larger datasets, while the Pairwise Word Interaction Model achieves the best
performance when less data is available. We release our implementations as an
open-source toolkit.
Sparse, Collaborative, or Nonnegative Representation: Which Helps Pattern Classification?
Authors: Jun Xu, Wangpeng An, Lei Zhang, David Zhang
Date: 2018-06-12 00:00:00
Tasks: Classification, Face Recognition, General Classification, Object Categorization

The use of sparse representation (SR) and collaborative representation (CR)
for pattern classification has been widely studied in tasks such as face
recognition and object categorization. Despite the success of SR/CR based
classifiers, it is still arguable whether it is the $\ell_{1}$-norm sparsity or
the $\ell_{2}$-norm collaborative property that brings the success of SR/CR
based classification. In this paper, we investigate the use of nonnegative
representation (NR) for pattern classification, which is largely ignored by
previous work. Our analyses reveal that NR can boost the representation power
of homogeneous samples while limiting the representation power of heterogeneous
samples, making the representation sparse and discriminative simultaneously and
thus providing a more effective solution to representation based classification
than SR/CR. Our experiments demonstrate that the proposed NR based classifier
(NRC) outperforms previous representation based classifiers. With deep features
as inputs, it also achieves state-of-the-art performance on various visual
classification tasks.
Learning Representations and Generative Models for 3D Point Clouds
Authors: Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas Guibas
Date: 2017-07-08 00:00:00
Proceeding: ICML 2018 7
Tasks: Diversity, Representation Learning

Three-dimensional geometric data offer an excellent domain for studying
representation learning and generative modeling. In this paper, we look at
geometric data represented as point clouds. We introduce a deep AutoEncoder
(AE) network with state-of-the-art reconstruction quality and generalization
ability. The learned representations outperform existing methods on 3D
recognition tasks and enable shape editing via simple algebraic manipulations,
such as semantic part editing, shape analogies and shape interpolation, as well
as shape completion. We perform a thorough study of different generative models
including GANs operating on the raw point clouds, significantly improved GANs
trained in the fixed latent space of our AEs, and Gaussian Mixture Models
(GMMs). To quantitatively evaluate generative models we introduce measures of
sample fidelity and diversity based on matchings between sets of point clouds.
Interestingly, our evaluation of generalization, fidelity and diversity reveals
that GMMs trained in the latent space of our AEs yield the best results
overall.
ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents
Authors: Stefano Mezza, Alessandra Cervone, Giuliano Tortoreto, Evgeny A. Stepanov, Giuseppe Riccardi
Date: 2018-06-12 00:00:00
Proceeding: COLING 2018 8
Tasks: General Classification, Spoken Language Understanding

Dialogue Act (DA) tagging is crucial for spoken language understanding
systems, as it provides a general representation of speakers' intents, not
bound to a particular dialogue system. Unfortunately, publicly available data
sets with DA annotation are all based on different annotation schemes and thus
incompatible with each other. Moreover, their schemes often do not cover all
aspects necessary for open-domain human-machine interaction. In this paper, we
propose a methodology to map several publicly available corpora to a subset of
the ISO standard, in order to create a large task-independent training corpus
for DA classification. We show the feasibility of using this corpus to train a
domain-independent DA tagger testing it on out-of-domain conversational data,
and argue the importance of training on multiple corpora to achieve robustness
across different DA categories.
Differentiable Compositional Kernel Learning for Gaussian Processes
Authors: Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger Grosse
Date: 2018-06-12 00:00:00
Proceeding: ICML 2018 7
Tasks: Bayesian Optimization, Gaussian Processes, Time Series, Time Series Analysis, valid

The generalization properties of Gaussian processes depend heavily on the
choice of kernel, and this choice remains a dark art. We present the Neural
Kernel Network (NKN), a flexible family of kernels represented by a neural
network. The NKN architecture is based on the composition rules for kernels, so
that each unit of the network corresponds to a valid kernel. It can compactly
approximate compositional kernel structures such as those used by the Automatic
Statistician (Lloyd et al., 2014), but because the architecture is
differentiable, it is end-to-end trainable with gradient-based optimization. We
show that the NKN is universal for the class of stationary kernels. Empirically
we demonstrate pattern discovery and extrapolation abilities of NKN on several
tasks that depend crucially on identifying the underlying structure, including
time series and texture extrapolation, as well as Bayesian optimization.
Augmenting Stream Constraint Programming with Eventuality Conditions
Authors: Jasper C. H. Lee, Jimmy H. M. Lee, Allen Z. Zhong
Date: 2018-06-12 00:00:00

Stream constraint programming is a recent addition to the family of
constraint programming frameworks, where variable domains are sets of infinite
streams over finite alphabets. Previous works showed promising results for its
applicability to real-world planning and control problems. In this paper,
motivated by the modelling of planning applications, we improve the
expressiveness of the framework by introducing 1) the "until" constraint, a new
construct that is adapted from Linear Temporal Logic and 2) the @ operator on
streams, a syntactic sugar for which we provide a more efficient solving
algorithm over simple desugaring. For both constructs, we propose corresponding
novel solving algorithms and prove their correctness. We present competitive
experimental results on the Missionaries and Cannibals logic puzzle and a
standard path planning application on the grid, by comparing with Apt and
Brand's method for verifying eventuality conditions using a CP approach.
Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking
Authors: Haichuan Yang, Yuhao Zhu, Ji Liu
Date: 2018-06-12 00:00:00
Proceeding: ICLR 2019 5

Deep Neural Networks (DNNs) are increasingly deployed in highly energy-constrained environments such as autonomous drones and wearable devices while at the same time must operate in real-time. Therefore, reducing the energy consumption has become a major design consideration in DNN training. This paper proposes the first end-to-end DNN training framework that provides quantitative energy consumption guarantees via weighted sparse projection and input masking. The key idea is to formulate the DNN training as an optimization problem in which the energy budget imposes a previously unconsidered optimization constraint. We integrate the quantitative DNN energy estimation into the DNN training process to assist the constrained optimization. We prove that an approximate algorithm can be used to efficiently solve the optimization problem. Compared to the best prior energy-saving methods, our framework trains DNNs that provide higher accuracies under same or lower energy budgets. Code is publicly available.
Support Vector Machine Application for Multiphase Flow Pattern Prediction
Authors: Pablo Guillen-Rondon, Melvin D. Robinson, Carlos Torres, Eduardo Pereya
Date: 2018-06-12 00:00:00
Tasks: General Classification, Prediction

In this paper a data analytical approach featuring support vector machines
(SVM) is employed to train a predictive model over an experimentaldataset,
which consists of the most relevant studies for two-phase flow pattern
prediction. The database for this study consists of flow patterns or flow
regimes in gas-liquid two-phase flow. The term flow pattern refers to the
geometrical configuration of the gas and liquid phases in the pipe. When gas
and liquid flow simultaneously in a pipe, the two phases can distribute
themselves in a variety of flow configurations. Gas-liquid two-phase flow
occurs ubiquitously in various major industrial fields: petroleum, chemical,
nuclear, and geothermal industries. The flow configurations differ from each
other in the spatial distribution of the interface, resulting in different flow
characteristics. Experimental results obtained by applying the presented
methodology to different combinations of flow patterns demonstrate that the
proposed approach is state-of-the-art alternatives by achieving 97% correct
classification. The results suggest machine learning could be used as an
effective tool for automatic detection and classification of gas-liquid flow
patterns.
Embedding Text in Hyperbolic Spaces
Authors: Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, George E. Dahl
Date: 2018-06-12 00:00:00
Proceeding: WS 2018 6
Tasks: Sentence, Sentence Embeddings

Natural language text exhibits hierarchical structure in a variety of
respects. Ideally, we could incorporate our prior knowledge of this
hierarchical structure into unsupervised learning algorithms that work on text
data. Recent work by Nickel & Kiela (2017) proposed using hyperbolic instead of
Euclidean embedding spaces to represent hierarchical data and demonstrated
encouraging results when embedding graphs. In this work, we extend their method
with a re-parameterization technique that allows us to learn hyperbolic
embeddings of arbitrarily parameterized objects. We apply this framework to
learn word and sentence embeddings in hyperbolic space in an unsupervised
manner from text corpora. The resulting embeddings seem to encode certain
intuitive notions of hierarchy, such as word-context frequency and phrase
constituency. However, the implicit continuous hierarchy in the learned
hyperbolic space makes interrogating the model's learned hierarchies more
difficult than for models that learn explicit edges between items. The learned
hyperbolic embeddings show improvements over Euclidean embeddings in some --
but not all -- downstream tasks, suggesting that hierarchical organization is
more useful for some tasks than others.
Pixels, voxels, and views: A study of shape representations for single view 3D object shape prediction
Authors: Daeyun Shin, Charless C. Fowlkes, Derek Hoiem
Date: 2018-04-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object

The goal of this paper is to compare surface-based and volumetric 3D object
shape representations, as well as viewer-centered and object-centered reference
frames for single-view 3D shape prediction. We propose a new algorithm for
predicting depth maps from multiple viewpoints, with a single depth or RGB
image as input. By modifying the network and the way models are evaluated, we
can directly compare the merits of voxels vs. surfaces and viewer-centered vs.
object-centered for familiar vs. unfamiliar objects, as predicted from RGB or
depth images. Among our findings, we show that surface-based methods outperform
voxel representations for objects from novel classes and produce higher
resolution outputs. We also find that using viewer-centered coordinates is
advantageous for novel objects, while object-centered representations are
better for more familiar objects. Interestingly, the coordinate frame
significantly affects the shape representation learned, with object-centered
placing more importance on implicitly recognizing the object category and
viewer-centered producing shape representations with less dependence on
category recognition.
Efficient end-to-end learning for quantizable representations
Authors: Yeonwoo Jeong, Hyun Oh Song
Date: 2018-05-15 00:00:00
Proceeding: ICML 2018 7
Tasks: Binarization, Metric Learning, Representation Learning

Embedding representation learning via neural networks is at the core
foundation of modern similarity based search. While much effort has been put in
developing algorithms for learning binary hamming code representations for
search efficiency, this still requires a linear scan of the entire dataset per
each query and trades off the search accuracy through binarization. To this
end, we consider the problem of directly learning a quantizable embedding
representation and the sparse binary hash code end-to-end which can be used to
construct an efficient hash table not only providing significant search
reduction in the number of data but also achieving the state of the art search
accuracy outperforming previous state of the art deep metric learning methods.
We also show that finding the optimal sparse binary hash code in a mini-batch
can be computed exactly in polynomial time by solving a minimum cost flow
problem. Our results on Cifar-100 and on ImageNet datasets show the state of
the art search accuracy in precision@k and NMI metrics while providing up to
98X and 478X search speedup respectively over exhaustive linear search. The
source code is available at
https://github.com/maestrojeong/Deep-Hash-Table-ICML18
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches
Authors: Amirali Aghazadeh, Ryan Spring, Daniel Lejeune, Gautam Dasarathy, Anshumali Shrivastava, Richard G. Baraniuk
Date: 2018-06-12 00:00:00
Tasks: BIG-bench Machine Learning, feature selection

Feature selection is an important challenge in machine learning. It plays a
crucial role in the explainability of machine-driven decisions that are rapidly
permeating throughout modern society. Unfortunately, the explosion in the size
and dimensionality of real-world datasets poses a severe challenge to standard
feature selection algorithms. Today, it is not uncommon for datasets to have
billions of dimensions. At such scale, even storing the feature vector is
impossible, causing most existing feature selection methods to fail.
Workarounds like feature hashing, a standard approach to large-scale machine
learning, helps with the computational feasibility, but at the cost of losing
the interpretability of features. In this paper, we present MISSION, a novel
framework for ultra large-scale feature selection that performs stochastic
gradient descent while maintaining an efficient representation of the features
in memory using a Count-Sketch data structure. MISSION retains the simplicity
of feature hashing without sacrificing the interpretability of the features
while using only O(log^2(p)) working memory. We demonstrate that MISSION
accurately and efficiently performs feature selection on real-world,
large-scale datasets with billions of dimensions.
Differentially Private Matrix Completion Revisited
Authors: Prateek Jain, Om Thakkar, Abhradeep Thakurta
Date: 2017-12-28 00:00:00
Proceeding: ICML 2018 7
Tasks: Collaborative Filtering, Matrix Completion, Privacy Preserving

We provide the first provably joint differentially private algorithm with
formal utility guarantees for the problem of user-level privacy-preserving
collaborative filtering. Our algorithm is based on the Frank-Wolfe method, and
it consistently estimates the underlying preference matrix as long as the
number of users $m$ is $\omega(n^{5/4})$, where $n$ is the number of items, and
each user provides her preference for at least $\sqrt{n}$ randomly selected
items. Along the way, we provide an optimal differentially private algorithm
for singular vector computation, based on the celebrated Oja's method, that
provides significant savings in terms of space and time while operating on
sparse matrices. We also empirically evaluate our algorithm on a suite of
datasets, and show that it consistently outperforms the state-of-the-art
private algorithms.