pwc-archive/papers-with-abstracts huggingface dataset:
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion
Authors: Richard Y. Zhang, Salar Fattahi, Somayeh Sojoudi
Date: 2018-02-14 00:00:00
Proceeding: ICML 2018 7
Tasks: Matrix Completion

The sparse inverse covariance estimation problem is commonly solved using an
$\ell_{1}$-regularized Gaussian maximum likelihood estimator known as
"graphical lasso", but its computational cost becomes prohibitive for large
data sets. A recent line of results showed--under mild assumptions--that the
graphical lasso estimator can be retrieved by soft-thresholding the sample
covariance matrix and solving a maximum determinant matrix completion (MDMC)
problem. This paper proves an extension of this result, and describes a
Newton-CG algorithm to efficiently solve the MDMC problem. Assuming that the
thresholded sample covariance matrix is sparse with a sparse Cholesky
factorization, we prove that the algorithm converges to an $\epsilon$-accurate
solution in $O(n\log(1/\epsilon))$ time and $O(n)$ memory. The algorithm is
highly efficient in practice: we solve the associated MDMC problems with as
many as 200,000 variables to 7-9 digits of accuracy in less than an hour on a
standard laptop computer running MATLAB.
Interlinked Convolutional Neural Networks for Face Parsing
Authors: Yisu Zhou, Xiaolin Hu, Bo Zhang
Date: 2018-06-07 00:00:00
Tasks: Face Parsing

Face parsing is a basic task in face image analysis. It amounts to labeling
each pixel with appropriate facial parts such as eyes and nose. In the paper,
we present a interlinked convolutional neural network (iCNN) for solving this
problem in an end-to-end fashion. It consists of multiple convolutional neural
networks (CNNs) taking input in different scales. A special interlinking layer
is designed to allow the CNNs to exchange information, enabling them to
integrate local and contextual information efficiently. The hallmark of iCNN is
the extensive use of downsampling and upsampling in the interlinking layers,
while traditional CNNs usually uses downsampling only. A two-stage pipeline is
proposed for face parsing and both stages use iCNN. The first stage localizes
facial parts in the size-reduced image and the second stage labels the pixels
in the identified facial parts in the original image. On a benchmark dataset we
have obtained better results than the state-of-the-art methods.
Towards Robust Training of Neural Networks by Regularizing Adversarial Gradients
Authors: Fuxun Yu, Zirui Xu, Yanzhi Wang, ChenChen Liu, Xiang Chen
Date: 2018-05-23 00:00:00

In recent years, neural networks have demonstrated outstanding effectiveness
in a large amount of applications.However, recent works have shown that neural
networks are susceptible to adversarial examples, indicating possible flaws
intrinsic to the network structures. To address this problem and improve the
robustness of neural networks, we investigate the fundamental mechanisms behind
adversarial examples and propose a novel robust training method via regulating
adversarial gradients. The regulation effectively squeezes the adversarial
gradients of neural networks and significantly increases the difficulty of
adversarial example generation.Without any adversarial example involved, the
robust training method could generate naturally robust networks, which are
near-immune to various types of adversarial examples. Experiments show the
naturally robust networks can achieve optimal accuracy against Fast Gradient
Sign Method (FGSM) and C\&W attacks on MNIST, Cifar10, and Google Speech
Command dataset. Moreover, our proposed method also provides neural networks
with consistent robustness against transferable attacks.
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Authors: Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec
Date: 2018-06-07 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Graph Generation, Molecular Graph Generation, Reinforcement Learning

Generating novel graph structures that optimize given objectives while
obeying some given underlying rules is fundamental for chemistry, biology and
social science research. This is especially important in the task of molecular
graph generation, whose goal is to discover novel molecules with desired
properties such as drug-likeness and synthetic accessibility, while obeying
physical laws such as chemical valency. However, designing models to find
molecules that optimize desired properties while incorporating highly complex
and non-differentiable rules remains to be a challenging task. Here we propose
Graph Convolutional Policy Network (GCPN), a general graph convolutional
network based model for goal-directed graph generation through reinforcement
learning. The model is trained to optimize domain-specific rewards and
adversarial loss through policy gradient, and acts in an environment that
incorporates domain-specific rules. Experimental results show that GCPN can
achieve 61% improvement on chemical property optimization over state-of-the-art
baselines while resembling known molecules, and achieve 184% improvement on the
constrained property optimization task.
Stochastic Zeroth-order Optimization via Variance Reduction method
Authors: Liu Liu, Minhao Cheng, Cho-Jui Hsieh, DaCheng Tao
Date: 2018-05-30 00:00:00

Derivative-free optimization has become an important technique used in
machine learning for optimizing black-box models. To conduct updates without
explicitly computing gradient, most current approaches iteratively sample a
random search direction from Gaussian distribution and compute the estimated
gradient along that direction. However, due to the variance in the search
direction, the convergence rates and query complexities of existing methods
suffer from a factor of $d$, where $d$ is the problem dimension. In this paper,
we introduce a novel Stochastic Zeroth-order method with Variance Reduction
under Gaussian smoothing (SZVR-G) and establish the complexity for optimizing
non-convex problems. With variance reduction on both sample space and search
space, the complexity of our algorithm is sublinear to $d$ and is strictly
better than current approaches, in both smooth and non-smooth cases. Moreover,
we extend the proposed method to the mini-batch version. Our experimental
results demonstrate the superior performance of the proposed method over
existing derivative-free optimization techniques. Furthermore, we successfully
apply our method to conduct a universal black-box attack to deep neural
networks and present some interesting results.
Quantitative Phase Imaging and Artificial Intelligence: A Review
Authors: YoungJu Jo, Hyungjoo Cho, Sang Yun Lee, Gunho Choi, Geon Kim, Hyun-seok Min, YongKeun Park
Date: 2018-06-06 00:00:00
Tasks: BIG-bench Machine Learning

Recent advances in quantitative phase imaging (QPI) and artificial
intelligence (AI) have opened up the possibility of an exciting frontier. The
fast and label-free nature of QPI enables the rapid generation of large-scale
and uniform-quality imaging data in two, three, and four dimensions.
Subsequently, the AI-assisted interrogation of QPI data using data-driven
machine learning techniques results in a variety of biomedical applications.
Also, machine learning enhances QPI itself. Herein, we review the synergy
between QPI and machine learning with a particular focus on deep learning.
Further, we provide practical guidelines and perspectives for further
development.
Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images
Authors: Jian Ren, Ilker Hacihaliloglu, Eric A. Singer, David J. Foran, Xin Qi
Date: 2018-06-04 00:00:00
Tasks: Domain Adaptation, General Classification, Prognosis, Unsupervised Domain Adaptation, whole slide images

Automatic and accurate Gleason grading of histopathology tissue slides is
crucial for prostate cancer diagnosis, treatment, and prognosis. Usually,
histopathology tissue slides from different institutions show heterogeneous
appearances because of different tissue preparation and staining procedures,
thus the predictable model learned from one domain may not be applicable to a
new domain directly. Here we propose to adopt unsupervised domain adaptation to
transfer the discriminative knowledge obtained from the source domain to the
target domain without requiring labeling of images at the target domain. The
adaptation is achieved through adversarial training to find an invariant
feature space along with the proposed Siamese architecture on the target domain
to add a regularization that is appropriate for the whole-slide images. We
validate the method on two prostate cancer datasets and obtain significant
classification improvement of Gleason scores as compared with the baseline
models.
The effect of the choice of neural network depth and breadth on the size of its hypothesis space
Authors: Lech Szymanski, Brendan McCane, Michael Albert
Date: 2018-06-06 00:00:00

We show that the number of unique function mappings in a neural network
hypothesis space is inversely proportional to $\prod_lU_l!$, where $U_{l}$ is
the number of neurons in the hidden layer $l$.
Learning Compact Neural Networks with Regularization
Authors: Samet Oymak
Date: 2018-02-05 00:00:00
Proceeding: ICML 2018 7
Tasks: Network Pruning

Proper regularization is critical for speeding up training, improving
generalization performance, and learning compact models that are cost
efficient. We propose and analyze regularized gradient descent algorithms for
learning shallow neural networks. Our framework is general and covers
weight-sharing (convolutional networks), sparsity (network pruning), and
low-rank constraints among others. We first introduce covering dimension to
quantify the complexity of the constraint set and provide insights on the
generalization properties. Then, we show that proposed algorithms become
well-behaved and local linear convergence occurs once the amount of data
exceeds the covering dimension. Overall, our results demonstrate that
near-optimal sample complexity is sufficient for efficient learning and
illustrate how regularization can be beneficial to learn over-parameterized
networks.
Diversity is All You Need: Learning Skills without a Reward Function
Authors: Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Levine
Date: 2018-02-16 00:00:00
Proceeding: ICLR 2019 5
Tasks: All, Diversity, Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Unsupervised Reinforcement Learning

Intelligent creatures can explore their environments and learn useful skills
without supervision. In this paper, we propose DIAYN ('Diversity is All You
Need'), a method for learning useful skills without a reward function. Our
proposed method learns skills by maximizing an information theoretic objective
using a maximum entropy policy. On a variety of simulated robotic tasks, we
show that this simple objective results in the unsupervised emergence of
diverse skills, such as walking and jumping. In a number of reinforcement
learning benchmark environments, our method is able to learn a skill that
solves the benchmark task despite never receiving the true task reward. We show
how pretrained skills can provide a good parameter initialization for
downstream tasks, and can be composed hierarchically to solve complex, sparse
reward tasks. Our results suggest that unsupervised discovery of skills can
serve as an effective pretraining mechanism for overcoming challenges of
exploration and data efficiency in reinforcement learning.
Visual Reasoning by Progressive Module Networks
Authors: Seung Wook Kim, Makarand Tapaswi, Sanja Fidler
Date: 2018-06-06 00:00:00
Proceeding: ICLR 2019 5
Tasks: Visual Reasoning

Humans learn to solve tasks of increasing complexity by building on top of
previously acquired knowledge. Typically, there exists a natural progression in
the tasks that we learn - most do not require completely independent solutions,
but can be broken down into simpler subtasks. We propose to represent a solver
for each task as a neural module that calls existing modules (solvers for
simpler tasks) in a functional program-like manner. Lower modules are a black
box to the calling module, and communicate only via a query and an output.
Thus, a module for a new task learns to query existing modules and composes
their outputs in order to produce its own output. Our model effectively
combines previous skill-sets, does not suffer from forgetting, and is fully
differentiable. We test our model in learning a set of visual reasoning tasks,
and demonstrate improved performances in all tasks by learning progressively.
By evaluating the reasoning process using human judges, we show that our model
is more interpretable than an attention-based baseline.
NumtaDB - Assembled Bengali Handwritten Digits
Authors: Samiul Alam, Tahsin Reasat, Rashed Mohammad Doha, Ahmed Imtiaz Humayun
Date: 2018-06-06 00:00:00

To benchmark Bengali digit recognition algorithms, a large publicly available
dataset is required which is free from biases originating from geographical
location, gender, and age. With this aim in mind, NumtaDB, a dataset consisting
of more than 85,000 images of hand-written Bengali digits, has been assembled.
This paper documents the collection and curation process of numerals along with
the salient statistics of the dataset.
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Authors: Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann
Date: 2018-04-03 00:00:00
Proceeding: ICML 2018 7
Tasks: Graph Embedding, Hypernym Discovery, Link Prediction, Representation Learning

Learning graph representations via low-dimensional embeddings that preserve
relevant network properties is an important class of problems in machine
learning. We here present a novel method to embed directed acyclic graphs.
Following prior work, we first advocate for using hyperbolic spaces which
provably model tree-like structures better than Euclidean geometry. Second, we
view hierarchical relations as partial orders defined using a family of nested
geodesically convex cones. We prove that these entailment cones admit an
optimal shape with a closed form expression both in the Euclidean and
hyperbolic spaces, and they canonically define the embedding learning process.
Experiments show significant improvements of our method over strong recent
baselines both in terms of representational capacity and generalization.
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation
Authors: Jalaj Bhandari, Daniel Russo, Raghav Singal
Date: 2018-06-06 00:00:00
Tasks: Q-Learning, Reinforcement Learning

Temporal difference learning (TD) is a simple iterative algorithm used to
estimate the value function corresponding to a given policy in a Markov
decision process. Although TD is one of the most widely used algorithms in
reinforcement learning, its theoretical analysis has proved challenging and few
guarantees on its statistical efficiency are available. In this work, we
provide a simple and explicit finite time analysis of temporal difference
learning with linear function approximation. Except for a few key insights, our
analysis mirrors standard techniques for analyzing stochastic gradient descent
algorithms, and therefore inherits the simplicity and elegance of that
literature. Final sections of the paper show how all of our main results extend
to the study of TD learning with eligibility traces, known as TD($\lambda$),
and to Q-learning applied in high-dimensional optimal stopping problems.
Deep Reinforcement Learning for General Video Game AI
Authors: Ruben Rodriguez Torrado, Philip Bontrager, Julian Togelius, Jialin Liu, Diego Perez-Liebana
Date: 2018-06-06 00:00:00
Tasks: Atari Games, Benchmarking, Deep Reinforcement Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The General Video Game AI (GVGAI) competition and its associated software
framework provides a way of benchmarking AI algorithms on a large number of
games written in a domain-specific description language. While the competition
has seen plenty of interest, it has so far focused on online planning,
providing a forward model that allows the use of algorithms such as Monte Carlo
Tree Search.
  In this paper, we describe how we interface GVGAI to the OpenAI Gym
environment, a widely used way of connecting agents to reinforcement learning
problems. Using this interface, we characterize how widely used implementations
of several deep reinforcement learning algorithms fare on a number of GVGAI
games. We further analyze the results to provide a first indication of the
relative difficulty of these games relative to each other, and relative to
those in the Arcade Learning Environment under similar conditions.
Deep Ordinal Regression Network for Monocular Depth Estimation
Authors: Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, DaCheng Tao
Date: 2018-06-06 00:00:00
Proceeding: CVPR 2018 6
Tasks: Depth Estimation, Monocular Depth Estimation, regression

Monocular depth estimation, which plays a crucial role in understanding 3D
scene geometry, is an ill-posed problem. Recent methods have gained significant
improvement by exploring image-level information and hierarchical features from
deep convolutional neural networks (DCNNs). These methods model depth
estimation as a regression problem and train the regression networks by
minimizing mean squared error, which suffers from slow convergence and
unsatisfactory local solutions. Besides, existing depth estimation networks
employ repeated spatial pooling operations, resulting in undesirable
low-resolution feature maps. To obtain high-resolution depth maps,
skip-connections or multi-layer deconvolution networks are required, which
complicates network training and consumes much more computations. To eliminate
or at least largely reduce these problems, we introduce a spacing-increasing
discretization (SID) strategy to discretize depth and recast depth network
learning as an ordinal regression problem. By training the network using an
ordinary regression loss, our method achieves much higher accuracy and
\dd{faster convergence in synch}. Furthermore, we adopt a multi-scale network
structure which avoids unnecessary spatial pooling and captures multi-scale
information in parallel.
  The method described in this paper achieves state-of-the-art results on four
challenging benchmarks, i.e., KITTI [17], ScanNet [9], Make3D [50], and NYU
Depth v2 [42], and win the 1st prize in Robust Vision Challenge 2018. Code has
been made available at: https://github.com/hufu6371/DORN.
Studying the Difference Between Natural and Programming Language Corpora
Authors: Casey Casalnuovo, Kenji Sagae, Prem Devanbu
Date: 2018-06-06 00:00:00

Code corpora, as observed in large software systems, are now known to be far
more repetitive and predictable than natural language corpora. But why? Does
the difference simply arise from the syntactic limitations of programming
languages? Or does it arise from the differences in authoring decisions made by
the writers of these natural and programming language texts? We conjecture that
the differences are not entirely due to syntax, but also from the fact that
reading and writing code is un-natural for humans, and requires substantial
mental effort; so, people prefer to write code in ways that are familiar to
both reader and writer. To support this argument, we present results from two
sets of studies: 1) a first set aimed at attenuating the effects of syntax, and
2) a second, aimed at measuring repetitiveness of text written in other
settings (e.g. second language, technical/specialized jargon), which are also
effortful to write. We find find that this repetition in source code is not
entirely the result of grammar constraints, and thus some repetition must
result from human choice. While the evidence we find of similar repetitive
behavior in technical and learner corpora does not conclusively show that such
language is used by humans to mitigate difficulty, it is consistent with that
theory.
Rethinking Radiology: An Analysis of Different Approaches to BraTS
Authors: William Bakst, Linus Meyer-Teruel, Jasdeep Singh
Date: 2018-06-06 00:00:00
Tasks: Deep Learning

This paper discusses the deep learning architectures currently used for
pixel-wise segmentation of primary and secondary glioblastomas and low-grade
gliomas. We implement various models such as the popular UNet architecture and
compare the performance of these implementations on the BRATS dataset. This
paper will explore the different approaches and combinations, offering an in
depth discussion of how they perform and how we may improve upon them using
more recent advancements in deep learning architectures.
Scalable k-Means Clustering via Lightweight Coresets
Authors: Olivier Bachem, Mario Lucic, Andreas Krause
Date: 2017-02-27 00:00:00
Tasks: Clustering, Data Summarization

Coresets are compact representations of data sets such that models trained on
a coreset are provably competitive with models trained on the full data set. As
such, they have been successfully used to scale up clustering models to massive
data sets. While existing approaches generally only allow for multiplicative
approximation errors, we propose a novel notion of lightweight coresets that
allows for both multiplicative and additive errors. We provide a single
algorithm to construct lightweight coresets for k-means clustering as well as
soft and hard Bregman clustering. The algorithm is substantially faster than
existing constructions, embarrassingly parallel, and the resulting coresets are
smaller. We further show that the proposed approach naturally generalizes to
statistical k-means clustering and that, compared to existing results, it can
be used to compute smaller summaries for empirical risk minimization. In
extensive experiments, we demonstrate that the proposed algorithm outperforms
existing data summarization strategies in practice.
Text-Independent Speaker Verification Using 3D Convolutional Neural Networks
Authors: Amirsina Torfi, Jeremy Dawson, Nasser M. Nasrabadi
Date: 2017-05-26 00:00:00
Tasks: Speaker Verification, Text-Independent Speaker Verification

In this paper, a novel method using 3D Convolutional Neural Network (3D-CNN)
architecture has been proposed for speaker verification in the text-independent
setting. One of the main challenges is the creation of the speaker models. Most
of the previously-reported approaches create speaker models based on averaging
the extracted features from utterances of the speaker, which is known as the
d-vector system. In our paper, we propose an adaptive feature learning by
utilizing the 3D-CNNs for direct speaker model creation in which, for both
development and enrollment phases, an identical number of spoken utterances per
speaker is fed to the network for representing the speakers' utterances and
creation of the speaker model. This leads to simultaneously capturing the
speaker-related information and building a more robust system to cope with
within-speaker variation. We demonstrate that the proposed method significantly
outperforms the traditional d-vector verification system. Moreover, the
proposed system can also be an alternative to the traditional d-vector system
which is a one-shot speaker modeling system by utilizing 3D-CNNs.