pwc-archive/papers-with-abstracts huggingface dataset:
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
Authors: Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, Pieter Abbeel
Date: 2019-02-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Computational Efficiency, Density Estimation, Image Generation

Flow-based generative models are powerful exact likelihood models with efficient sampling and inference. Despite their computational efficiency, flow-based models generally have much worse density modeling performance compared to state-of-the-art autoregressive models. In this paper, we investigate and improve upon three limiting design choices employed by flow-based models in prior work: the use of uniform noise for dequantization, the use of inexpressive affine flows, and the use of purely convolutional conditioning networks in coupling layers. Based on our findings, we propose Flow++, a new flow-based model that is now the state-of-the-art non-autoregressive model for unconditional density estimation on standard image benchmarks. Our work has begun to close the significant performance gap that has so far existed between autoregressive models and flow-based models. Our implementation is available at https://github.com/aravindsrinivas/flowpp
Deep Learning Solutions for TanDEM-X-based Forest Classification
Authors: Antonio Mazza, Francescopaolo Sica
Date: 2019-02-01 00:00:00
Tasks: Classification, Deep Learning, General Classification, image-classification, Image Classification, object-detection, Object Detection

In the last few years, deep learning (DL) has been successfully and massively
employed in computer vision for discriminative tasks, such as image
classification or object detection. This kind of problems are core to many
remote sensing (RS) applications as well, though with domain-specific
peculiarities. Therefore, there is a growing interest on the use of DL methods
for RS tasks. Here, we consider the forest/non-forest classification problem
with TanDEM-X data, and test two state-of-the-art DL models, suitably adapting
them to the specific task. Our experiments confirm the great potential of DL
methods for RS applications.
ColorNet: Investigating the importance of color spaces for image classification
Authors: Shreyank N Gowda, Chun Yuan
Date: 2019-02-01 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Image classification is a fundamental application in computer vision.
Recently, deeper networks and highly connected networks have shown state of the
art performance for image classification tasks. Most datasets these days
consist of a finite number of color images. These color images are taken as
input in the form of RGB images and classification is done without modifying
them. We explore the importance of color spaces and show that color spaces
(essentially transformations of original RGB images) can significantly affect
classification accuracy. Further, we show that certain classes of images are
better represented in particular color spaces and for a dataset with a highly
varying number of classes such as CIFAR and Imagenet, using a model that
considers multiple color spaces within the same model gives excellent levels of
accuracy. Also, we show that such a model, where the input is preprocessed into
multiple color spaces simultaneously, needs far fewer parameters to obtain high
accuracy for classification. For example, our model with 1.75M parameters
significantly outperforms DenseNet 100-12 that has 12M parameters and gives
results comparable to Densenet-BC-190-40 that has 25.6M parameters for
classification of four competitive image classification datasets namely:
CIFAR-10, CIFAR-100, SVHN and Imagenet. Our model essentially takes an RGB
image as input, simultaneously converts the image into 7 different color spaces
and uses these as inputs to individual densenets. We use small and wide
densenets to reduce computation overhead and number of hyperparameters
required. We obtain significant improvement on current state of the art results
on these datasets as well.
Policy Consolidation for Continual Reinforcement Learning
Authors: Christos Kaplanis, Murray Shanahan, Claudia Clopath
Date: 2019-02-01 00:00:00
Tasks: Continual Learning, continuous-control, Continuous Control, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a method for tackling catastrophic forgetting in deep reinforcement learning that is \textit{agnostic} to the timescale of changes in the distribution of experiences, does not require knowledge of task boundaries, and can adapt in \textit{continuously} changing environments. In our \textit{policy consolidation} model, the policy network interacts with a cascade of hidden networks that simultaneously remember the agent's policy at a range of timescales and regularise the current policy by its own history, thereby improving its ability to learn without forgetting. We find that the model improves continual learning relative to baselines on a number of continuous control tasks in single-task, alternating two-task, and multi-agent competitive self-play settings.
ProteinNet: a standardized data set for machine learning of protein structure
Authors: Mohammed AlQuraishi
Date: 2019-02-01 00:00:00
Tasks: BIG-bench Machine Learning, Protein Secondary Structure Prediction, Protein Structure Prediction

Rapid progress in deep learning has spurred its application to bioinformatics
problems including protein structure prediction and design. In classic machine
learning problems like computer vision, progress has been driven by
standardized data sets that facilitate fair assessment of new methods and lower
the barrier to entry for non-domain experts. While data sets of protein
sequence and structure exist, they lack certain components critical for machine
learning, including high-quality multiple sequence alignments and insulated
training / validation splits that account for deep but only weakly detectable
homology across protein space. We have created the ProteinNet series of data
sets to provide a standardized mechanism for training and assessing data-driven
models of protein sequence-structure relationships. ProteinNet integrates
sequence, structure, and evolutionary information in programmatically
accessible file formats tailored for machine learning frameworks. Multiple
sequence alignments of all structurally characterized proteins were created
using substantial high-performance computing resources. Standardized data
splits were also generated to emulate the difficulty of past CASP (Critical
Assessment of protein Structure Prediction) experiments by resetting protein
sequence and structure space to the historical states that preceded six prior
CASPs. Utilizing sensitive evolution-based distance metrics to segregate
distantly related proteins, we have additionally created validation sets
distinct from the official CASP sets that faithfully mimic their difficulty.
ProteinNet thus represents a comprehensive and accessible resource for training
and assessing machine-learned models of protein structure.
Sharp Analysis for Nonconvex SGD Escaping from Saddle Points
Authors: Cong Fang, Zhouchen Lin, Tong Zhang
Date: 2019-02-01 00:00:00
Tasks: Stochastic Optimization

In this paper, we give a sharp analysis for Stochastic Gradient Descent (SGD) and prove that SGD is able to efficiently escape from saddle points and find an $(\epsilon, O(\epsilon^{0.5}))$-approximate second-order stationary point in $\tilde{O}(\epsilon^{-3.5})$ stochastic gradient computations for generic nonconvex optimization problems, when the objective function satisfies gradient-Lipschitz, Hessian-Lipschitz, and dispersive noise assumptions. This result subverts the classical belief that SGD requires at least $O(\epsilon^{-4})$ stochastic gradient computations for obtaining an $(\epsilon,O(\epsilon^{0.5}))$-approximate second-order stationary point. Such SGD rate matches, up to a polylogarithmic factor of problem-dependent parameters, the rate of most accelerated nonconvex stochastic optimization algorithms that adopt additional techniques, such as Nesterov's momentum acceleration, negative curvature search, as well as quadratic and cubic regularization tricks. Our novel analysis gives new insights into nonconvex SGD and can be potentially generalized to a broad class of stochastic optimization algorithms.
Natural and Adversarial Error Detection using Invariance to Image Transformations
Authors: Yuval Bahat, Michal Irani, Gregory Shakhnarovich
Date: 2019-02-01 00:00:00
Tasks: Translation

We propose an approach to distinguish between correct and incorrect image
classifications. Our approach can detect misclassifications which either occur
$\it{unintentionally}$ ("natural errors"), or due to
$\it{intentional~adversarial~attacks}$ ("adversarial errors"), both in a single
$\it{unified~framework}$. Our approach is based on the observation that
correctly classified images tend to exhibit robust and consistent
classifications under certain image transformations (e.g., horizontal flip,
small image translation, etc.). In contrast, incorrectly classified images
(whether due to adversarial errors or natural errors) tend to exhibit large
variations in classification results under such transformations. Our approach
does not require any modifications or retraining of the classifier, hence can
be applied to any pre-trained classifier. We further use state of the art
targeted adversarial attacks to demonstrate that even when the adversary has
full knowledge of our method, the adversarial distortion needed for bypassing
our detector is $\it{no~longer~imperceptible~to~the~human~eye}$. Our approach
obtains state-of-the-art results compared to previous adversarial detection
methods, surpassing them by a large margin.
A Classification Supervised Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids
Authors: Qiuyu Zhu, Ruixin Zhang
Date: 2019-02-01 00:00:00
Tasks: Classification, Decoder, General Classification

Classic variational autoencoders are used to learn complex data distributions, that are built on standard function approximators. Especially, VAE has shown promise on a lot of complex task. In this paper, a new autoencoder model - classification supervised autoencoder (CSAE) based on predefined evenly-distributed class centroids (PEDCC) is proposed. Our method uses PEDCC of latent variables to train the network to ensure the maximization of inter-class distance and the minimization of inner-class distance. Instead of learning mean/variance of latent variables distribution and taking reparameterization of VAE, latent variables of CSAE are directly used to classify and as input of decoder. In addition, a new loss function is proposed to combine the loss function of classification. Based on the basic structure of the universal autoencoder, we realized the comprehensive optimal results of encoding, decoding, classification, and good model generalization performance at the same time. Theoretical advantages are reflected in experimental results.
Causally Driven Incremental Multi Touch Attribution Using a Recurrent Neural Network
Authors: Ruihuan Du, Yu Zhong, Harikesh Nair, Bo Cui, Ruyang Shou
Date: 2019-02-01 00:00:00
Tasks: Fairness

This paper describes a practical system for Multi Touch Attribution (MTA) for
use by a publisher of digital ads. We developed this system for JD.com, an
eCommerce company, which is also a publisher of digital ads in China. The
approach has two steps. The first step ('response modeling') fits a user-level
model for purchase of a product as a function of the user's exposure to ads.
The second ('credit allocation') uses the fitted model to allocate the
incremental part of the observed purchase due to advertising, to the ads the
user is exposed to over the previous T days. To implement step one, we train a
Recurrent Neural Network (RNN) on user-level conversion and exposure data. The
RNN has the advantage of flexibly handling the sequential dependence in the
data in a semi-parametric way. The specific RNN formulation we implement
captures the impact of advertising intensity, timing, competition, and
user-heterogeneity, which are known to be relevant to ad-response. To implement
step two, we compute Shapley Values, which have the advantage of having
axiomatic foundations and satisfying fairness considerations. The specific
formulation of the Shapley Value we implement respects incrementality by
allocating the overall incremental improvement in conversion to the exposed
ads, while handling the sequence-dependence of exposures on the observed
outcomes. The system is under production at JD.com, and scales to handle the
high dimensionality of the problem on the platform (attribution of the orders
of about 300M users, for roughly 160K brands, across 200+ ad-types, served
about 80B ad-impressions over a typical 15-day period).
Optimal Attack against Autoregressive Models by Manipulating the Environment
Authors: Yiding Chen, Xiaojin Zhu
Date: 2019-02-01 00:00:00
Tasks: Adversarial Attack, Model Predictive Control, Time Series, Time Series Analysis

We describe an optimal adversarial attack formulation against autoregressive time series forecast using Linear Quadratic Regulator (LQR). In this threat model, the environment evolves according to a dynamical system; an autoregressive model observes the current environment state and predicts its future values; an attacker has the ability to modify the environment state in order to manipulate future autoregressive forecasts. The attacker's goal is to force autoregressive forecasts into tracking a target trajectory while minimizing its attack expenditure. In the white-box setting where the attacker knows the environment and forecast models, we present the optimal attack using LQR for linear models, and Model Predictive Control (MPC) for nonlinear models. In the black-box setting, we combine system identification and MPC. Experiments demonstrate the effectiveness of our attacks.
Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models
Authors: Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael. I. Jordan, Bin Yu
Date: 2019-02-01 00:00:00

We study a class of weakly identifiable location-scale mixture models for which the maximum likelihood estimates based on $n$ i.i.d. samples are known to have lower accuracy than the classical $n^{- \frac{1}{2}}$ error. We investigate whether the Expectation-Maximization (EM) algorithm also converges slowly for these models. We provide a rigorous characterization of EM for fitting a weakly identifiable Gaussian mixture in a univariate setting where we prove that the EM algorithm converges in order $n^{\frac{3}{4}}$ steps and returns estimates that are at a Euclidean distance of order ${ n^{- \frac{1}{8}}}$ and ${ n^{-\frac{1} {4}}}$ from the true location and scale parameter respectively. Establishing the slow rates in the univariate setting requires a novel localization argument with two stages, with each stage involving an epoch-based argument applied to a different surrogate EM operator at the population level. We demonstrate several multivariate ($d \geq 2$) examples that exhibit the same slow rates as the univariate case. We also prove slow statistical rates in higher dimensions in a special case, when the fitted covariance is constrained to be a multiple of the identity.
Massively Multilingual Transfer for NER
Authors: Afshin Rahimi, Yuan Li, Trevor Cohn
Date: 2019-02-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Cross-Lingual Transfer, Few-Shot Learning, Low Resource Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

In cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language. While most prior work has used a single source model or a few carefully selected models, here we consider a `massive' setting with many such models. This setting raises the problem of poor transfer, particularly from distant languages. We propose two techniques for modulating the transfer, suitable for zero-shot or few-shot learning, respectively. Evaluating on named entity recognition, we show that our techniques are much more effective than strong baselines, including standard ensembling, and our unsupervised method rivals oracle selection of the single best individual model.
A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings
Authors: Wei Yang, Wei Lu, Vincent W. Zheng
Date: 2019-02-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Learning Word Embeddings, Word Embeddings

Learning word embeddings has received a significant amount of attention
recently. Often, word embeddings are learned in an unsupervised manner from a
large collection of text. The genre of the text typically plays an important
role in the effectiveness of the resulting embeddings. How to effectively train
word embedding models using data from different domains remains a problem that
is underexplored. In this paper, we present a simple yet effective method for
learning word embeddings based on text from different domains. We demonstrate
the effectiveness of our approach through extensive experiments on various
down-stream NLP tasks.
Learning Action Representations for Reinforcement Learning
Authors: Yash Chandak, Georgios Theocharous, James Kostas, Scott Jordan, Philip S. Thomas
Date: 2019-02-01 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Most model-free reinforcement learning methods leverage state representations (embeddings) for generalization, but either ignore structure in the space of actions or assume the structure is provided a priori. We show how a policy can be decomposed into a component that acts in a low-dimensional space of action representations and a component that transforms these representations into actual actions. These representations improve generalization over large, finite action sets by allowing the agent to infer the outcomes of actions similar to actions already taken. We provide an algorithm to both learn and use action representations and provide conditions for its convergence. The efficacy of the proposed method is demonstrated on large-scale real-world problems.
Compressing Gradient Optimizers via Count-Sketches
Authors: Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava
Date: 2019-02-01 00:00:00

Many popular first-order optimization methods (e.g., Momentum, AdaGrad, Adam)
accelerate the convergence rate of deep learning models. However, these
algorithms require auxiliary parameters, which cost additional memory
proportional to the number of parameters in the model. The problem is becoming
more severe as deep learning models continue to grow larger in order to learn
from complex, large-scale datasets. Our proposed solution is to maintain a
linear sketch to compress the auxiliary variables. We demonstrate that our
technique has the same performance as the full-sized baseline, while using
significantly less space for the auxiliary variables. Theoretically, we prove
that count-sketch optimization maintains the SGD convergence rate, while
gracefully reducing memory usage for large-models. On the large-scale 1-Billion
Word dataset, we save 25% of the memory used during training (8.6 GB instead of
11.7 GB) by compressing the Adam optimizer in the Embedding and Softmax layers
with negligible accuracy and performance loss. For an Amazon extreme
classification task with over 49.5 million classes, we also reduce the training
time by 38%, by increasing the mini-batch size 3.5x using our count-sketch
optimizer.
Critical initialisation in continuous approximations of binary neural networks
Authors: George Stamatescu, Federica Gerace, Carlo Lucibello, Ian Fuss, Langford B. White
Date: 2019-02-01 00:00:00
Proceeding: ICLR 2020 1

The training of stochastic neural network models with binary ($\pm1$) weights and activations via continuous surrogate networks is investigated. We derive new surrogates using a novel derivation based on writing the stochastic neural network as a Markov chain. This derivation also encompasses existing variants of the surrogates presented in the literature. Following this, we theoretically study the surrogates at initialisation. We derive, using mean field theory, a set of scalar equations describing how input signals propagate through the randomly initialised networks. The equations reveal whether so-called critical initialisations exist for each surrogate network, where the network can be trained to arbitrary depth. Moreover, we predict theoretically and confirm numerically, that common weight initialisation schemes used in standard continuous networks, when applied to the mean values of the stochastic binary weights, yield poor training performance. This study shows that, contrary to common intuition, the means of the stochastic binary weights should be initialised close to $\pm 1$, for deeper networks to be trainable.
Fast and Optimal Laplacian Solver for Gradient-Domain Image Editing using Green Function Convolution
Authors: Dominique Beaini, Sofiane Achiche, Fabrice Nonez, Olivier Brochu Dufour, Cédric Leblond-Ménard, Mahdis Asaadi, Maxime Raison
Date: 2019-02-01 00:00:00
Tasks: Edge Detection, GPU

In computer vision, the gradient and Laplacian of an image are used in different applications, such as edge detection, feature extraction, and seamless image cloning. Computing the gradient of an image is straightforward since numerical derivatives are available in most computer vision toolboxes. However, the reverse problem is more difficult, since computing an image from its gradient requires to solve the Laplacian equation, also called Poisson equation. Current discrete methods are either slow or require heavy parallel computing. The objective of this paper is to present a novel fast and robust method of solving the image gradient or Laplacian with minimal error, which can be used for gradient domain editing. By using a single convolution based on a numerical Green's function, the whole process is faster and straightforward to implement with different computer vision libraries. It can also be optimized on a GPU using fast Fourier transforms and can easily be generalized for an n dimension image. The tests show that, for images of resolution 801x1200, the proposed GFC can solve 100 Laplacian in parallel in around 1.0 milliseconds ms. This is orders of magnitude faster than our nearest competitor which requires 294ms for a single image. Furthermore, we prove mathematically and demonstrate empirically that the proposed method is the least error solver for gradient domain editing. The developed method is also validated with examples of Poisson blending, gradient removal, and the proposed gradient domain merging GDM. Finally, we present how the GDM can be leveraged in future works for convolutional neural networks CNN.
Dating Documents using Graph Convolution Networks
Authors: Shikhar Vashishth, Shib Sankar Dasgupta, Swayambhu Nath Ray, Partha Talukdar
Date: 2019-02-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Document Dating, Event Detection, Retrieval

Document date is essential for many important tasks, such as document
retrieval, summarization, event detection, etc. While existing approaches for
these tasks assume accurate knowledge of the document date, this is not always
available, especially for arbitrary documents from the Web. Document Dating is
a challenging problem which requires inference over the temporal structure of
the document. Prior document dating systems have largely relied on handcrafted
features while ignoring such document internal structures. In this paper, we
propose NeuralDater, a Graph Convolutional Network (GCN) based document dating
approach which jointly exploits syntactic and temporal graph structures of
document in a principled way. To the best of our knowledge, this is the first
application of deep learning for the problem of document dating. Through
extensive experiments on real-world datasets, we find that NeuralDater
significantly outperforms state-of-the-art baseline by 19% absolute (45%
relative) accuracy points.