pwc-archive/papers-with-abstracts huggingface dataset:
CRRN: Multi-Scale Guided Concurrent Reflection Removal Network
Authors: Renjie Wan, Boxin Shi, Ling-Yu Duan, Ah-Hwee Tan, Alex C. Kot
Date: 2018-05-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: Reflection Removal

Removing the undesired reflections from images taken through the glass is of
broad application to various computer vision tasks. Non-learning based methods
utilize different handcrafted priors such as the separable sparse gradients
caused by different levels of blurs, which often fail due to their limited
description capability to the properties of real-world reflections. In this
paper, we propose the Concurrent Reflection Removal Network (CRRN) to tackle
this problem in a unified framework. Our proposed network integrates image
appearance information and multi-scale gradient information with human
perception inspired loss function, and is trained on a new dataset with 3250
reflection images taken under diverse real-world scenes. Extensive experiments
on a public benchmark dataset show that the proposed method performs favorably
against state-of-the-art methods.
Automated proof synthesis for propositional logic with deep neural networks
Authors: Taro Sekiyama, Kohei Suenaga
Date: 2018-05-30 00:00:00
Tasks: Automated Theorem Proving

This work explores the application of deep learning, a machine learning
technique that uses deep neural networks (DNN) in its core, to an automated
theorem proving (ATP) problem. To this end, we construct a statistical model
which quantifies the likelihood that a proof is indeed a correct one of a given
proposition. Based on this model, we give a proof-synthesis procedure that
searches for a proof in the order of the likelihood. This procedure uses an
estimator of the likelihood of an inference rule being applied at each step of
a proof. As an implementation of the estimator, we propose a
proposition-to-proof architecture, which is a DNN tailored to the automated
proof synthesis problem. To empirically demonstrate its usefulness, we apply
our model to synthesize proofs of propositional logic. We train the
proposition-to-proof model using a training dataset of proposition-proof pairs.
The evaluation against a benchmark set shows the very high accuracy and an
improvement to the recent work of neural proof synthesis.
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach
Authors: Sahil Garg, Irina Rish, Guillermo Cecchi, Palash Goyal, Sarik Ghazarian, Shuyang Gao, Greg Ver Steeg, Aram Galstyan
Date: 2018-04-26 00:00:00
Tasks: Computational Efficiency, Dialogue Generation, Model Selection

We propose a novel dialogue modeling framework, the first-ever nonparametric kernel functions based approach for dialogue modeling, which learns kernelized hashcodes as compressed text representations; unlike traditional deep learning models, it handles well relatively small datasets, while also scaling to large ones. We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses. As demonstrated on three real-life datasets, including prominently psychotherapy sessions, the proposed approach significantly outperforms several state-of-art neural network based dialogue systems, both in terms of computational efficiency, reducing training time from days or weeks to hours, and the response quality, achieving an order of magnitude improvement over competitors in frequency of being chosen as the best model by human evaluators.
Infinite Arms Bandit: Optimality via Confidence Bounds
Authors: Hock Peng Chan, Shouri Hu
Date: 2018-05-30 00:00:00

Berry et al. (1997) initiated the development of the infinite arms bandit problem. They derived a regret lower bound of all allocation strategies for Bernoulli rewards with uniform priors, and proposed strategies based on success runs. Bonald and Prouti\`{e}re (2013) proposed a two-target algorithm that achieves the regret lower bound, and extended optimality to Bernoulli rewards with general priors. We present here a confidence bound target (CBT) algorithm that achieves optimality for rewards that are bounded above. For each arm we construct a confidence bound and compare it against each other and a target value to determine if the arm should be sampled further. The target value depends on the assumed priors of the arm means. In the absence of information on the prior, the target value is determined empirically. Numerical studies here show that CBT is versatile and outperforms its competitors.
Tight Regret Bounds for Bayesian Optimization in One Dimension
Authors: Jonathan Scarlett
Date: 2018-05-30 00:00:00
Proceeding: ICML 2018 7
Tasks: Bayesian Optimization

We consider the problem of Bayesian optimization (BO) in one dimension, under a Gaussian process prior and Gaussian sampling noise. We provide a theoretical analysis showing that, under fairly mild technical assumptions on the kernel, the best possible cumulative regret up to time $T$ behaves as $\Omega(\sqrt{T})$ and $O(\sqrt{T\log T})$. This gives a tight characterization up to a $\sqrt{\log T}$ factor, and includes the first non-trivial lower bound for noisy BO. Our assumptions are satisfied, for example, by the squared exponential and Mat\'ern-$\nu$ kernels, with the latter requiring $\nu > 2$. Our results certify the near-optimality of existing bounds (Srinivas {\em et al.}, 2009) for the SE kernel, while proving them to be strictly suboptimal for the Mat\'ern kernel with $\nu > 2$.
A Hierarchical End-to-End Model for Jointly Improving Text Summarization and Sentiment Classification
Authors: Shuming Ma, Xu sun, Junyang Lin, Xuancheng Ren
Date: 2018-05-03 00:00:00
Tasks: Abstractive Text Summarization, Classification, General Classification, Sentiment Analysis, Sentiment Classification, Text Summarization

Text summarization and sentiment classification both aim to capture the main
ideas of the text but at different levels. Text summarization is to describe
the text within a few sentences, while sentiment classification can be regarded
as a special type of summarization which "summarizes" the text into a even more
abstract fashion, i.e., a sentiment class. Based on this idea, we propose a
hierarchical end-to-end model for joint learning of text summarization and
sentiment classification, where the sentiment classification label is treated
as the further "summarization" of the text summarization output. Hence, the
sentiment classification layer is put upon the text summarization layer, and a
hierarchical structure is derived. Experimental results on Amazon online
reviews datasets show that our model achieves better performance than the
strong baseline systems on both abstractive summarization and sentiment
classification.
A Fine-to-Coarse Convolutional Neural Network for 3D Human Action Recognition
Authors: Thao Minh Le, Nakamasa Inoue, Koichi Shinoda
Date: 2018-05-30 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

This paper presents a new framework for human action recognition from a 3D
skeleton sequence. Previous studies do not fully utilize the temporal
relationships between video segments in a human action. Some studies
successfully used very deep Convolutional Neural Network (CNN) models but often
suffer from the data insufficiency problem. In this study, we first segment a
skeleton sequence into distinct temporal segments in order to exploit the
correlations between them. The temporal and spatial features of a skeleton
sequence are then extracted simultaneously by utilizing a fine-to-coarse (F2C)
CNN architecture optimized for human skeleton sequences. We evaluate our
proposed method on NTU RGB+D and SBU Kinect Interaction dataset. It achieves
79.6% and 84.6% of accuracies on NTU RGB+D with cross-object and cross-view
protocol, respectively, which are almost identical with the state-of-the-art
performance. In addition, our method significantly improves the accuracy of the
actions in two-person interactions.
Multi-function Convolutional Neural Networks for Improving Image Classification Performance
Authors: Luna M. Zhang
Date: 2018-05-30 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Multi-class Classification

Traditional Convolutional Neural Networks (CNNs) typically use the same
activation function (usually ReLU) for all neurons with non-linear mapping
operations. For example, the deep convolutional architecture Inception-v4 uses
ReLU. To improve the classification performance of traditional CNNs, a new
"Multi-function Convolutional Neural Network" (MCNN) is created by using
different activation functions for different neurons. For $n$ neurons and $m$
different activation functions, there are a total of $m^n-m$ MCNNs and only $m$
traditional CNNs. Therefore, the best model is very likely to be chosen from
MCNNs because there are $m^n-2m$ more MCNNs than traditional CNNs. For
performance analysis, two different datasets for two applications (classifying
handwritten digits from the MNIST database and classifying brain MRI images
into one of the four stages of Alzheimer's disease (AD)) are used. For both
applications, an activation function is randomly selected for each layer of a
MCNN. For the AD diagnosis application, MCNNs using a newly created
multi-function Inception-v4 architecture are constructed. Overall, simulations
show that MCNNs can outperform traditional CNNs in terms of multi-class
classification accuracy for both applications. An important future research
work will be to efficiently select the best MCNN from $m^n-m$ candidate MCNNs.
Current CNN software only provides users with partial functionality of MCNNs
since different layers can use different activation functions but not
individual neurons in the same layer. Thus, modifying current CNN software
systems such as ResNets, DenseNets, and Dual Path Networks by using multiple
activation functions and developing more effective and faster MCNN software
systems and tools would be very useful to solve difficult practical image
classification problems.
Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors
Authors: Rico Jonschkowski, Divyam Rastogi, Oliver Brock
Date: 2018-05-28 00:00:00
Tasks: State Estimation

We present differentiable particle filters (DPFs): a differentiable
implementation of the particle filter algorithm with learnable motion and
measurement models. Since DPFs are end-to-end differentiable, we can
efficiently train their models by optimizing end-to-end state estimation
performance, rather than proxy objectives such as model accuracy. DPFs encode
the structure of recursive state estimation with prediction and measurement
update that operate on a probability distribution over states. This structure
represents an algorithmic prior that improves learning performance in state
estimation problems while enabling explainability of the learned model. Our
experiments on simulated and real data show substantial benefits from end-to-
end learning with algorithmic priors, e.g. reducing error rates by ~80%. Our
experiments also show that, unlike long short-term memory networks, DPFs learn
localization in a policy-agnostic way and thus greatly improve generalization.
Source code is available at
https://github.com/tu-rbo/differentiable-particle-filters .
Hyperspectral Imaging Technology and Transfer Learning Utilized in Identification Haploid Maize Seeds
Authors: Wen-Xuan Liao, Xuan-Yu Wang, Dong An, Yao-Guang Wei
Date: 2018-05-30 00:00:00
Tasks: Transfer Learning

It is extremely important to correctly identify the cultivars of maize seeds
in the breeding process of maize. In this paper, the transfer learning as a
method of deep learning is adopted to establish a model by combining with the
hyperspectral imaging technology. The haploid seeds can be recognized from
large amount of diploid maize ones with great accuracy through the model.
First, the information of maize seeds on each wave band is collected using the
hyperspectral imaging technology, and then the recognition model is built on
VGG-19 network, which is pre-trained by large-scale computer vision database
(Image-Net). The correct identification rate of model utilizing seed spectral
images containing 256 wave bands (862.5-1704.2nm) reaches 96.32%, and the
correct identification rate of the model utilizing the seed spectral images
containing single-band reaches 95.75%. The experimental results show that, CNN
model which is pre-trained by visible light image database can be applied to
the near-infrared hyperspectral imaging-based identification of maize seeds,
and high accurate identification rate can be achieved. Meanwhile, when there is
small amount of data samples, it can still realize high recognition by using
transfer learning. The model not only meets the requirements of breeding
recognition, but also greatly reduce the cost occurred in sample collection.
Rice Classification Using Spatio-Spectral Deep Convolutional Neural Network
Authors: Itthi Chatnuntawech, Kittipong Tantisantisom, Paisan Khanchaitit, Thitikorn Boonkoom, Berkin Bilgic, Ekapol Chuangsuwanich
Date: 2018-05-29 00:00:00
Tasks: Classification, General Classification

Rice has been one of the staple foods that contribute significantly to human food supplies. Numerous rice varieties have been cultivated, imported, and exported worldwide. Different rice varieties could be mixed during rice production and trading. Rice impurities could damage the trust between rice importers and exporters, calling for the need to develop a rice variety inspection system. In this work, we develop a non-destructive rice variety classification system that benefits from the synergy between hyperspectral imaging and deep convolutional neural network (CNN). The proposed method uses a hyperspectral imaging system to simultaneously acquire complementary spatial and spectral information of rice seeds. The rice varieties are then determined from the acquired spatio-spectral data using a deep CNN. As opposed to several existing rice variety classification methods that require hand-engineered features, the proposed method automatically extracts spatio-spectral features from the raw sensor data. As demonstrated using two types of rice datasets, the proposed method achieved up to 11.9% absolute improvement in the mean classification accuracy, compared to the commonly used classification methods based on support vector machines.
To Trust Or Not To Trust A Classifier
Authors: Heinrich Jiang, Been Kim, Melody Y. Guan, Maya Gupta
Date: 2018-05-30 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Topological Data Analysis

Knowing when a classifier's prediction can be trusted is useful in many
applications and critical for safely using AI. While the bulk of the effort in
machine learning research has been towards improving classifier performance,
understanding when a classifier's predictions should and should not be trusted
has received far less attention. The standard approach is to use the
classifier's discriminant or confidence score; however, we show there exists an
alternative that is more effective in many situations. We propose a new score,
called the trust score, which measures the agreement between the classifier and
a modified nearest-neighbor classifier on the testing example. We show
empirically that high (low) trust scores produce surprisingly high precision at
identifying correctly (incorrectly) classified examples, consistently
outperforming the classifier's confidence score as well as many other
baselines. Further, under some mild distributional assumptions, we show that if
the trust score for an example is high (low), the classifier will likely agree
(disagree) with the Bayes-optimal classifier. Our guarantees consist of
non-asymptotic rates of statistical consistency under various nonparametric
settings and build on recent developments in topological data analysis.
A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception
Authors: William Lotter, Gabriel Kreiman, David Cox
Date: 2018-05-28 00:00:00
Tasks: Open-Ended Question Answering, Predict Future Video Frames

While deep neural networks take loose inspiration from neuroscience, it is an
open question how seriously to take the analogies between artificial deep
networks and biological neuronal systems. Interestingly, recent work has shown
that deep convolutional neural networks (CNNs) trained on large-scale image
recognition tasks can serve as strikingly good models for predicting the
responses of neurons in visual cortex to visual stimuli, suggesting that
analogies between artificial and biological neural networks may be more than
superficial. However, while CNNs capture key properties of the average
responses of cortical neurons, they fail to explain other properties of these
neurons. For one, CNNs typically require large quantities of labeled input data
for training. Our own brains, in contrast, rarely have access to this kind of
supervision, so to the extent that representations are similar between CNNs and
brains, this similarity must arise via different training paths. In addition,
neurons in visual cortex produce complex time-varying responses even to static
inputs, and they dynamically tune themselves to temporal regularities in the
visual environment. We argue that these differences are clues to fundamental
differences between the computations performed in the brain and in deep
networks. To begin to close the gap, here we study the emergent properties of a
previously-described recurrent generative network that is trained to predict
future video frames in a self-supervised manner. Remarkably, the model is able
to capture a wide variety of seemingly disparate phenomena observed in visual
cortex, ranging from single unit response dynamics to complex perceptual motion
illusions. These results suggest potentially deep connections between recurrent
predictive neural network models and the brain, providing new leads that can
enrich both fields.
Non-rigid Reconstruction with a Single Moving RGB-D Camera
Authors: Shafeeq Elanattil, Peyman Moghadam, Sridha Sridharan, Clinton Fookes, Mark Cox
Date: 2018-05-29 00:00:00

We present a novel non-rigid reconstruction method using a moving RGB-D
camera. Current approaches use only non-rigid part of the scene and completely
ignore the rigid background. Non-rigid parts often lack sufficient geometric
and photometric information for tracking large frame-to-frame motion. Our
approach uses camera pose estimated from the rigid background for foreground
tracking. This enables robust foreground tracking in situations where large
frame-to-frame motion occurs. Moreover, we are proposing a multi-scale
deformation graph which improves non-rigid tracking without compromising the
quality of the reconstruction. We are also contributing a synthetic dataset
which is made publically available for evaluating non-rigid reconstruction
methods. The dataset provides frame-by-frame ground truth geometry of the
scene, the camera trajectory, and masks for background foreground. Experimental
results show that our approach is more robust in handling larger frame-to-frame
motions and provides better reconstruction compared to state-of-the-art
approaches.
Planning, Inference and Pragmatics in Sequential Language Games
Authors: Fereshte Khani, Noah D. Goodman, Percy Liang
Date: 2018-05-30 00:00:00
Proceeding: TACL 2018 1

We study sequential language games in which two players, each with private
information, communicate to achieve a common goal. In such games, a successful
player must (i) infer the partner's private information from the partner's
messages, (ii) generate messages that are most likely to help with the goal,
and (iii) reason pragmatically about the partner's strategy. We propose a model
that captures all three characteristics and demonstrate their importance in
capturing human behavior on a new goal-oriented dataset we collected using
crowdsourcing.
Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice
Authors: Amir Rasouli, John K. Tsotsos
Date: 2018-05-30 00:00:00
Tasks: Autonomous Vehicles

One of the major challenges that autonomous cars are facing today is driving
in urban environments. To make it a reality, autonomous vehicles require the
ability to communicate with other road users and understand their intentions.
Such interactions are essential between the vehicles and pedestrians as the
most vulnerable road users. Understanding pedestrian behavior, however, is not
intuitive and depends on various factors such as demographics of the
pedestrians, traffic dynamics, environmental conditions, etc. In this paper, we
identify these factors by surveying pedestrian behavior studies, both the
classical works on pedestrian-driver interaction and the modern ones that
involve autonomous vehicles. To this end, we will discuss various methods of
studying pedestrian behavior, and analyze how the factors identified in the
literature are interrelated. We will also review the practical applications
aimed at solving the interaction problem including design approaches for
autonomous vehicles that communicate with pedestrians and visual perception and
reasoning algorithms tailored to understanding pedestrian intention. Based on
our findings, we will discuss the open problems and propose future research
directions.
Deep Defense: Training DNNs with Improved Adversarial Robustness
Authors: Ziang Yan, Yiwen Guo, Chang-Shui Zhang
Date: 2018-02-23 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Adversarial Robustness

Despite the efficacy on a variety of computer vision tasks, deep neural
networks (DNNs) are vulnerable to adversarial attacks, limiting their
applications in security-critical systems. Recent works have shown the
possibility of generating imperceptibly perturbed image inputs (a.k.a.,
adversarial examples) to fool well-trained DNN classifiers into making
arbitrary predictions. To address this problem, we propose a training recipe
named "deep defense". Our core idea is to integrate an adversarial
perturbation-based regularizer into the classification objective, such that the
obtained models learn to resist potential attacks, directly and precisely. The
whole optimization problem is solved just like training a recursive network.
Experimental results demonstrate that our method outperforms training with
adversarial/Parseval regularizations by large margins on various datasets
(including MNIST, CIFAR-10 and ImageNet) and different DNN architectures. Code
and models for reproducing our results are available at
https://github.com/ZiangYan/deepdefense.pytorch