pwc-archive/papers-with-abstracts huggingface dataset:
GamePad: A Learning Environment for Theorem Proving
Authors: Daniel Huang, Prafulla Dhariwal, Dawn Song, Ilya Sutskever
Date: 2018-06-02 00:00:00
Proceeding: ICLR 2019 5
Tasks: Automated Theorem Proving, Position

In this paper, we introduce a system called GamePad that can be used to
explore the application of machine learning methods to theorem proving in the
Coq proof assistant. Interactive theorem provers such as Coq enable users to
construct machine-checkable proofs in a step-by-step manner. Hence, they
provide an opportunity to explore theorem proving with human supervision. We
use GamePad to synthesize proofs for a simple algebraic rewrite problem and
train baseline models for a formalization of the Feit-Thompson theorem. We
address position evaluation (i.e., predict the number of proof steps left) and
tactic prediction (i.e., predict the next proof step) tasks, which arise
naturally in tactic-based theorem proving.
Maximum Principle Based Algorithms for Deep Learning
Authors: Qianxiao Li, Long Chen, Cheng Tai, Weinan E
Date: 2017-10-26 00:00:00
Tasks: Deep Learning

The continuous dynamical system approach to deep learning is explored in
order to devise alternative frameworks for training algorithms. Training is
recast as a control problem and this allows us to formulate necessary
optimality conditions in continuous time using the Pontryagin's maximum
principle (PMP). A modification of the method of successive approximations is
then used to solve the PMP, giving rise to an alternative training algorithm
for deep learning. This approach has the advantage that rigorous error
estimates and convergence results can be established. We also show that it may
avoid some pitfalls of gradient-based methods, such as slow convergence on flat
landscapes near saddle points. Furthermore, we demonstrate that it obtains
favorable initial convergence rate per-iteration, provided Hamiltonian
maximization can be efficiently carried out - a step which is still in need of
improvement. Overall, the approach opens up new avenues to attack problems
associated with deep learning, such as trapping in slow manifolds and
inapplicability of gradient-based methods for discrete trainable variables.
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks
Authors: Qianxiao Li, Shuji Hao
Date: 2018-03-04 00:00:00
Proceeding: ICML 2018 7

Deep learning is formulated as a discrete-time optimal control problem. This
allows one to characterize necessary conditions for optimality and develop
training algorithms that do not rely on gradients with respect to the trainable
parameters. In particular, we introduce the discrete-time method of successive
approximations (MSA), which is based on the Pontryagin's maximum principle, for
training neural networks. A rigorous error estimate for the discrete MSA is
obtained, which sheds light on its dynamics and the means to stabilize the
algorithm. The developed methods are applied to train, in a rather principled
way, neural networks with weights that are constrained to take values in a
discrete set. We obtain competitive performance and interestingly, very sparse
weights in the case of ternary networks, which may be useful in model
deployment in low-memory devices.
Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data
Authors: N. Olspert, J. Lehtinen, M. J. Käpylä, J. Pelt, A. Grigorievskiy
Date: 2017-12-21 00:00:00
Tasks: Gaussian Processes, Time Series, Time Series Analysis

Debate over the existence of branches in the stellar activity-rotation
diagrams continues. Application of modern time series analysis tools to study
the mean cycle periods in chromospheric activity index is lacking. We develop
such models, based on Gaussian processes, for one-dimensional time series and
apply it to the extended Mount Wilson Ca H&K sample. Our main aim is to study
how the previously commonly used assumption of strict harmonicity of the
stellar cycles as well as handling of the linear trends affects the results. We
introduce three methods of different complexity, starting with the simple
Bayesian harmonic model and followed by Gaussian Process models with periodic
and quasi-periodic covariance functions. We confirm the existence of two
populations in the activity-period diagram. We find only one significant trend
in the inactive population, namely that the cycle periods get shorter with
increasing rotation. This is in contrast with earlier studies, that postulate
the existence of trends in both of the populations. In terms of rotation to
cycle period ratio, our data is consistent with only two activity branches such
that the active branch merges together with the transitional one. The retrieved
stellar cycles are uniformly distributed over the R'HK activity index,
indicating that the operation of stellar large-scale dynamos carries smoothly
over the Vaughan-Preston gap. At around the solar activity index, however,
indications of a disruption in the cyclic dynamo action are seen. Our study
shows that stellar cycle estimates depend significantly on the model applied.
Such model-dependent aspects include the improper treatment of linear trends,
while the assumption of strict harmonicity can result in the appearance of
double cyclicities that seem more likely to be explained by the
quasi-periodicity of the cycles.
Internal Model from Observations for Reward Shaping
Authors: Daiki Kimura, Subhajit Chaudhury, Ryuki Tachibana, Sakyasingha Dasgupta
Date: 2018-06-02 00:00:00
Tasks: model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning methods require careful design involving a reward
function to obtain the desired action policy for a given task. In the absence
of hand-crafted reward functions, prior work on the topic has proposed several
methods for reward estimation by using expert state trajectories and action
pairs. However, there are cases where complete or good action information
cannot be obtained from expert demonstrations. We propose a novel reinforcement
learning method in which the agent learns an internal model of observation on
the basis of expert-demonstrated state trajectories to estimate rewards without
completely learning the dynamics of the external environment from state-action
pairs. The internal model is obtained in the form of a predictive model for the
given expert state distribution. During reinforcement learning, the agent
predicts the reward as a function of the difference between the actual state
and the state predicted by the internal model. We conducted multiple
experiments in environments of varying complexity, including the Super Mario
Bros and Flappy Bird games. We show our method successfully trains good
policies directly from expert game-play videos.
BoxNet: Deep Learning Based Biomedical Image Segmentation Using Boxes Only Annotation
Authors: Lin Yang, Yizhe Zhang, Zhuo Zhao, Hao Zheng, Peixian Liang, Michael T. C. Ying, Anil T. Ahuja, Danny Z. Chen
Date: 2018-06-02 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

In recent years, deep learning (DL) methods have become powerful tools for
biomedical image segmentation. However, high annotation efforts and costs are
commonly needed to acquire sufficient biomedical training data for DL models.
To alleviate the burden of manual annotation, in this paper, we propose a new
weakly supervised DL approach for biomedical image segmentation using boxes
only annotation. First, we develop a method to combine graph search (GS) and DL
to generate fine object masks from box annotation, in which DL uses box
annotation to compute a rough segmentation for GS and then GS is applied to
locate the optimal object boundaries. During the mask generation process, we
carefully utilize information from box annotation to filter out potential
errors, and then use the generated masks to train an accurate DL segmentation
network. Extensive experiments on gland segmentation in histology images, lymph
node segmentation in ultrasound images, and fungus segmentation in electron
microscopy images show that our approach attains superior performance over the
best known state-of-the-art weakly supervised DL method and is able to achieve
(1) nearly the same accuracy compared to fully supervised DL methods with far
less annotation effort, (2) significantly better results with similar
annotation time, and (3) robust performance in various applications.
Does the brain represent words? An evaluation of brain decoding studies of language understanding
Authors: Jon Gauthier, Anna Ivanova
Date: 2018-06-02 00:00:00
Tasks: Brain Decoding, Sentence

Language decoding studies have identified word representations which can be
used to predict brain activity in response to novel words and sentences
(Anderson et al., 2016; Pereira et al., 2018). The unspoken assumption of these
studies is that, during processing, linguistic information is transformed into
some shared semantic space, and those semantic representations are then used
for a variety of linguistic and non-linguistic tasks. We claim that current
studies vastly underdetermine the content of these representations, the
algorithms which the brain deploys to produce and consume them, and the
computational tasks which they are designed to solve. We illustrate this
indeterminacy with an extension of the sentence-decoding experiment of Pereira
et al. (2018), showing how standard evaluations fail to distinguish between
language processing models which deploy different mechanisms and which are
optimized to solve very different tasks. We conclude by suggesting changes to
the brain decoding paradigm which can support stronger claims of neural
representation.
Efficient Entropy for Policy Gradient with Multidimensional Action Space
Authors: Yiming Zhang, Quan Ho Vuong, Kenny Song, Xiao-Yue Gong, Keith W. Ross
Date: 2018-06-02 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years, deep reinforcement learning has been shown to be adept at
solving sequential decision processes with high-dimensional state spaces such
as in the Atari games. Many reinforcement learning problems, however, involve
high-dimensional discrete action spaces as well as high-dimensional state
spaces. This paper considers entropy bonus, which is used to encourage
exploration in policy gradient. In the case of high-dimensional action spaces,
calculating the entropy and its gradient requires enumerating all the actions
in the action space and running forward and backpropagation for each action,
which may be computationally infeasible. We develop several novel unbiased
estimators for the entropy bonus and its gradient. We apply these estimators to
several models for the parameterized policies, including Independent Sampling,
CommNet, Autoregressive with Modified MDP, and Autoregressive with LSTM.
Finally, we test our algorithms on two environments: a multi-hunter
multi-rabbit grid game and a multi-agent multi-arm bandit problem. The results
show that our entropy estimators substantially improve performance with
marginal additional computational cost.
Fast Locality Sensitive Hashing for Beam Search on GPU
Authors: Xing Shi, Shizhen Xu, Kevin Knight
Date: 2018-06-02 00:00:00
Tasks: GPU, Machine Translation, Translation

We present a GPU-based Locality Sensitive Hashing (LSH) algorithm to speed up
beam search for sequence models. We utilize the winner-take-all (WTA) hash,
which is based on relative ranking order of hidden dimensions and thus
resilient to perturbations in numerical values. Our algorithm is designed by
fully considering the underling architecture of CUDA-enabled GPUs
(Algorithm/Architecture Co-design): 1) A parallel Cuckoo hash table is applied
for LSH code lookup (guaranteed O(1) lookup time); 2) Candidate lists are
shared across beams to maximize the parallelism; 3) Top frequent words are
merged into candidate lists to improve performance. Experiments on 4
large-scale neural machine translation models demonstrate that our algorithm
can achieve up to 4x speedup on softmax module, and 2x overall speedup without
hurting BLEU on GPU.
CytonMT: an Efficient Neural Machine Translation Open-source Toolkit Implemented in C++
Authors: Xiaolin Wang, Masao Utiyama, Eiichiro Sumita
Date: 2018-02-17 00:00:00
Proceeding: EMNLP 2018 11
Tasks: GPU, Machine Translation, Translation

This paper presents an open-source neural machine translation toolkit named
CytonMT (https://github.com/arthurxlw/cytonMt). The toolkit is built from
scratch only using C++ and NVIDIA's GPU-accelerated libraries. The toolkit
features training efficiency, code simplicity and translation quality.
Benchmarks show that CytonMT accelerates the training speed by 64.5% to 110.8%
on neural networks of various sizes, and achieves competitive translation
quality.
Monocular Depth Estimation with Augmented Ordinal Depth Relationships
Authors: Yuanzhouhan Cao, Tianqi Zhao, Ke Xian, Chunhua Shen, Zhiguo Cao, Shugong Xu
Date: 2018-06-02 00:00:00
Tasks: Depth Estimation, Depth Prediction, Monocular Depth Estimation, Stereo Matching, Stereo Matching Hand

Most existing algorithms for depth estimation from single monocular images need large quantities of metric groundtruth depths for supervised learning. We show that relative depth can be an informative cue for metric depth estimation and can be easily obtained from vast stereo videos. Acquiring metric depths from stereo videos is sometimes impracticable due to the absence of camera parameters. In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo matching algorithm. We introduce a new "Relative Depth in Stereo" (RDIS) dataset densely labelled with relative depths. We first pretrain a ResNet model on our RDIS dataset. Then we finetune the model on RGB-D datasets with metric ground-truth depths. During our finetuning, we formulate depth estimation as a classification task. This re-formulation scheme enables us to obtain the confidence of a depth prediction in the form of probability distribution. With this confidence, we propose an information gain loss to make use of the predictions that are close to ground-truth. We evaluate our approach on both indoor and outdoor benchmark RGB-D datasets and achieve state-of-the-art performance.
Improving Sparse Representation-Based Classification Using Local Principal Component Analysis
Authors: Chelsea Weaver, Naoki Saito
Date: 2016-07-04 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification, Sparse Representation-based Classification

Sparse representation-based classification (SRC), proposed by Wright et al.,
seeks the sparsest decomposition of a test sample over the dictionary of
training samples, with classification to the most-contributing class. Because
it assumes test samples can be written as linear combinations of their
same-class training samples, the success of SRC depends on the size and
representativeness of the training set. Our proposed classification algorithm
enlarges the training set by using local principal component analysis to
approximate the basis vectors of the tangent hyperplane of the class manifold
at each training sample. The dictionary in SRC is replaced by a local
dictionary that adapts to the test sample and includes training samples and
their corresponding tangent basis vectors. We use a synthetic data set and
three face databases to demonstrate that this method can achieve higher
classification accuracy than SRC in cases of sparse sampling, nonlinear class
manifolds, and stringent dimension reduction.
Federated Learning with Non-IID Data
Authors: Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vikas Chandra
Date: 2018-06-02 00:00:00
Tasks: Federated Learning

Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local. This decentralized approach to train models provides privacy, security, regulatory and economic benefits. In this work, we focus on the statistical challenge of federated learning when local data is non-IID. We first show that the accuracy of federated learning reduces significantly, by up to 55% for neural networks trained for highly skewed non-IID data, where each client device trains only on a single class of data. We further show that this accuracy reduction can be explained by the weight divergence, which can be quantified by the earth mover's distance (EMD) between the distribution over classes on each device and the population distribution. As a solution, we propose a strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge devices. Experiments show that accuracy can be increased by 30% for the CIFAR-10 dataset with only 5% globally shared data.
Detecting Adversarial Examples via Key-based Network
Authors: Pinlong Zhao, Zhouyu Fu, Ou wu, QinGhua Hu, Jun Wang
Date: 2018-06-02 00:00:00

Though deep neural networks have achieved state-of-the-art performance in
visual classification, recent studies have shown that they are all vulnerable
to the attack of adversarial examples. Small and often imperceptible
perturbations to the input images are sufficient to fool the most powerful deep
neural networks. Various defense methods have been proposed to address this
issue. However, they either require knowledge on the process of generating
adversarial examples, or are not robust against new attacks specifically
designed to penetrate the existing defense. In this work, we introduce
key-based network, a new detection-based defense mechanism to distinguish
adversarial examples from normal ones based on error correcting output codes,
using the binary code vectors produced by multiple binary classifiers applied
to randomly chosen label-sets as signatures to match normal images and reject
adversarial examples. In contrast to existing defense methods, the proposed
method does not require knowledge of the process for generating adversarial
examples and can be applied to defend against different types of attacks. For
the practical black-box and gray-box scenarios, where the attacker does not
know the encoding scheme, we show empirically that key-based network can
effectively detect adversarial examples generated by several state-of-the-art
attacks.
SCAN: Sliding Convolutional Attention Network for Scene Text Recognition
Authors: Yi-Chao Wu, Fei Yin, Xu-Yao Zhang, Li Liu, Cheng-Lin Liu
Date: 2018-06-02 00:00:00
Tasks: Scene Text Recognition

Scene text recognition has drawn great attentions in the community of
computer vision and artificial intelligence due to its challenges and wide
applications. State-of-the-art recurrent neural networks (RNN) based models map
an input sequence to a variable length output sequence, but are usually applied
in a black box manner and lack of transparency for further improvement, and the
maintaining of the entire past hidden states prevents parallel computation in a
sequence. In this paper, we investigate the intrinsic characteristics of text
recognition, and inspired by human cognition mechanisms in reading texts, we
propose a scene text recognition method with sliding convolutional attention
network (SCAN). Similar to the eye movement during reading, the process of SCAN
can be viewed as an alternation between saccades and visual fixations. Compared
to the previous recurrent models, computations over all elements of SCAN can be
fully parallelized during training. Experimental results on several challenging
benchmarks, including the IIIT5k, SVT and ICDAR 2003/2013 datasets, demonstrate
the superiority of SCAN over state-of-the-art methods in terms of both the
model interpretability and performance.
Coarse-to-Fine Salient Object Detection with Low-Rank Matrix Recovery
Authors: Qi Zheng, Shujian Yu, Xinge You, Qinmu Peng
Date: 2018-05-21 00:00:00
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

Low-Rank Matrix Recovery (LRMR) has recently been applied to saliency detection by decomposing image features into a low-rank component associated with background and a sparse component associated with visual salient regions. Despite its great potential, existing LRMR-based saliency detection methods seldom consider the inter-relationship among elements within these two components, thus are prone to generating scattered or incomplete saliency maps. In this paper, we introduce a novel and efficient LRMR-based saliency detection model under a coarse-to-fine framework to circumvent this limitation. First, we roughly measure the saliency of image regions with a baseline LRMR model that integrates a $\ell_1$-norm sparsity constraint and a Laplacian regularization smooth term. Given samples from the coarse saliency map, we then learn a projection that maps image features to refined saliency values, to significantly sharpen the object boundaries and to preserve the object entirety. We evaluate our framework against existing LRMR-based methods on three benchmark datasets. Experimental results validate the superiority of our method as well as the effectiveness of our suggested coarse-to-fine framework, especially for images containing multiple objects.
Autoencoders Learn Generative Linear Models
Authors: Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde
Date: 2018-06-02 00:00:00

We provide a series of results for unsupervised learning with autoencoders.
Specifically, we study shallow two-layer autoencoder architectures with shared
weights. We focus on three generative models for data that are common in
statistical machine learning: (i) the mixture-of-gaussians model, (ii) the
sparse coding model, and (iii) the sparsity model with non-negative
coefficients. For each of these models, we prove that under suitable choices of
hyperparameters, architectures, and initialization, autoencoders learned by
gradient descent can successfully recover the parameters of the corresponding
model. To our knowledge, this is the first result that rigorously studies the
dynamics of gradient descent for weight-sharing autoencoders. Our analysis can
be viewed as theoretical evidence that shallow autoencoder modules indeed can
be used as feature learning mechanisms for a variety of data models, and may
shed insight on how to train larger stacked architectures with autoencoders as
basic building blocks.
Variable Selection for Nonparametric Learning with Power Series Kernels
Authors: Kota Matsui, Wataru Kumagai, Kenta Kanamori, Mitsuaki Nishikimi, Takafumi Kanamori
Date: 2018-06-02 00:00:00
Tasks: Density Ratio Estimation, regression, Variable Selection

In this paper, we propose a variable selection method for general
nonparametric kernel-based estimation. The proposed method consists of
two-stage estimation: (1) construct a consistent estimator of the target
function, (2) approximate the estimator using a few variables by l1-type
penalized estimation. We see that the proposed method can be applied to various
kernel nonparametric estimation such as kernel ridge regression, kernel-based
density and density-ratio estimation. We prove that the proposed method has the
property of the variable selection consistency when the power series kernel is
used. This result is regarded as an extension of the variable selection
consistency for the non-negative garrote to the kernel-based estimators.
Several experiments including simulation studies and real data applications
show the effectiveness of the proposed method.