pwc-archive/papers-with-abstracts huggingface dataset:
Confidence Region of Singular Subspaces for Low-rank Matrix Regression
Authors: Dong Xia
Date: 2018-05-24 00:00:00
Tasks: regression

Low-rank matrix regression refers to the instances of recovering a low-rank
matrix based on specially designed measurements and the corresponding noisy
outcomes. In the last decade, numerous statistical methodologies have been
developed for efficiently recovering the unknown low-rank matrices. However, in
some applications, the unknown singular subspace is scientifically more
important than the low-rank matrix itself. In this article, we revisit the
low-rank matrix regression model and introduce a two-step procedure to
construct confidence regions of the singular subspace. The procedure involves
the de-biasing for the typical low-rank estimators after which we calculate the
empirical singular vectors. We investigate the distribution of the joint
projection distance between the empirical singular subspace and the unknown
true singular subspace. We specifically prove the asymptotical normality of the
joint projection distance with data-dependent centering and normalization when
$r^{3/2}(m_1+m_2)^{3/2}=o(n/\log n)$ where $m_1, m_2$ denote the matrix row and
column sizes, $r$ is the rank and $n$ is the number of independent random
measurements. Consequently, we propose data-dependent confidence regions of the
true singular subspace which attains any pre-determined confidence level
asymptotically. In addition, non-asymptotical convergence rates are also
established. Numerical results are presented to demonstrate the merits of our
methods.
Pooling of Causal Models under Counterfactual Fairness via Causal Judgement Aggregation
Authors: Fabio Massimo Zennaro, Magdalena Ivanovska
Date: 2018-05-24 00:00:00
Tasks: BIG-bench Machine Learning, Causal Judgment, counterfactual, Fairness

In this paper we consider the problem of combining multiple probabilistic
causal models, provided by different experts, under the requirement that the
aggregated model satisfy the criterion of counterfactual fairness. We build
upon the work on causal models and fairness in machine learning, and we express
the problem of combining multiple models within the framework of opinion
pooling. We propose two simple algorithms, grounded in the theory of
counterfactual fairness and causal judgment aggregation, that are guaranteed to
generate aggregated probabilistic causal models respecting the criterion of
fairness, and we compare their behaviors on a toy case study.
Inverse Rational Control: Inferring What You Think from How You Forage
Authors: Zhengwei Wu, Paul Schrater, Xaq Pitkow
Date: 2018-05-24 00:00:00
Tasks: Imitation Learning

Complex behaviors are often driven by an internal model, which integrates sensory information over time and facilitates long-term planning. Inferring an agent's internal model is a crucial ingredient in social interactions (theory of mind), for imitation learning, and for interpreting neural activities of behaving agents. Here we describe a generic method to model an agent's behavior under an environment with uncertainty, and infer the agent's internal model, reward function, and dynamic beliefs. We apply our method to a simulated agent performing a naturalistic foraging task. We assume the agent behaves rationally --- that is, they take actions that optimize their subjective utility according to their understanding of the task and its relevant causal variables. We model this rational solution as a Partially Observable Markov Decision Process (POMDP) where the agent may make wrong assumptions about the task parameters. Given the agent's sensory observations and actions, we learn its internal model and reward function by maximum likelihood estimation over a set of task-relevant parameters. The Markov property of the POMDP enables us to characterize the transition probabilities between internal belief states and iteratively estimate the agent's policy using a constrained Expectation-Maximization (EM) algorithm. We validate our method on simulated agents performing suboptimally on a foraging task currently used in many neuroscience experiments, and successfully recover their internal model and reward function. Our work lays a critical foundation to discover how the brain represents and computes with dynamic beliefs.
Neural Network Quine
Authors: Oscar Chang, Hod Lipson
Date: 2018-03-15 00:00:00
Tasks: General Classification, image-classification, Image Classification

Self-replication is a key aspect of biological life that has been largely
overlooked in Artificial Intelligence systems. Here we describe how to build
and train self-replicating neural networks. The network replicates itself by
learning to output its own weights. The network is designed using a loss
function that can be optimized with either gradient-based or non-gradient-based
methods. We also describe a method we call regeneration to train the network
without explicit optimization, by injecting the network with predictions of its
own parameters. The best solution for a self-replicating network was found by
alternating between regeneration and optimization steps. Finally, we describe a
design for a self-replicating neural network that can solve an auxiliary task
such as MNIST image classification. We observe that there is a trade-off
between the network's ability to classify images and its ability to replicate,
but training is biased towards increasing its specialization at image
classification at the expense of replication. This is analogous to the
trade-off between reproduction and other tasks observed in nature. We suggest
that a self-replication mechanism for artificial intelligence is useful because
it introduces the possibility of continual improvement through natural
selection.
Scale-Robust Localization Using General Object Landmarks
Authors: Andrew Holliday, Gregory Dudek
Date: 2017-10-28 00:00:00
Tasks: Object, Visual Localization

Visual localization under large changes in scale is an important capability
in many robotic mapping applications, such as localizing at low altitudes in
maps built at high altitudes, or performing loop closure over long distances.
Existing approaches, however, are robust only up to about a 3x difference in
scale between map and query images.
  We propose a novel combination of deep-learning-based object features and
state-of-the-art SIFT point-features that yields improved robustness to scale
change. This technique is training-free and class-agnostic, and in principle
can be deployed in any environment out-of-the-box. We evaluate the proposed
technique on the KITTI Odometry benchmark and on a novel dataset of outdoor
images exhibiting changes in visual scale of $7\times$ and greater, which we
have released to the public. Our technique consistently outperforms
localization using either SIFT features or the proposed object features alone,
achieving both greater accuracy and much lower failure rates under large
changes in scale.
Gradient Regularization Improves Accuracy of Discriminative Models
Authors: Dániel Varga, Adrián Csiszárik, Zsolt Zombori
Date: 2017-12-28 00:00:00
Tasks: General Classification

Regularizing the gradient norm of the output of a neural network with respect
to its inputs is a powerful technique, rediscovered several times. This paper
presents evidence that gradient regularization can consistently improve
classification accuracy on vision tasks, using modern deep neural networks,
especially when the amount of training data is small. We introduce our
regularizers as members of a broader class of Jacobian-based regularizers. We
demonstrate empirically on real and synthetic data that the learning process
leads to gradients controlled beyond the training points, and results in
solutions that generalize well.
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
Authors: Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, Lawrence Carin
Date: 2018-05-24 00:00:00
Proceeding: ACL 2018 7
Tasks: Document Classification, General Classification, Named Entity Recognition (NER), Sentiment Analysis, Subjectivity Analysis, Text Classification, Word Embeddings

Many deep learning architectures have been proposed to model the
compositionality in text sequences, requiring a substantial number of
parameters and expensive computations. However, there has not been a rigorous
evaluation regarding the added value of sophisticated compositional functions.
In this paper, we conduct a point-by-point comparative study between Simple
Word-Embedding-based Models (SWEMs), consisting of parameter-free pooling
operations, relative to word-embedding-based RNN/CNN models. Surprisingly,
SWEMs exhibit comparable or even superior performance in the majority of cases
considered. Based upon this understanding, we propose two additional pooling
strategies over learned word embeddings: (i) a max-pooling operation for
improved interpretability; and (ii) a hierarchical pooling operation, which
preserves spatial (n-gram) information within text sequences. We present
experiments on 17 datasets encompassing three tasks: (i) (long) document
classification; (ii) text sequence matching; and (iii) short text tasks,
including classification and tagging. The source code and datasets can be
obtained from https:// github.com/dinghanshen/SWEM.
Stereo Magnification: Learning View Synthesis using Multiplane Images
Authors: Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, Noah Snavely
Date: 2018-05-24 00:00:00
Tasks: Novel View Synthesis

The view synthesis problem--generating novel views of a scene from known
imagery--has garnered recent attention due in part to compelling applications
in virtual and augmented reality. In this paper, we explore an intriguing
scenario for view synthesis: extrapolating views from imagery captured by
narrow-baseline stereo cameras, including VR cameras and now-widespread
dual-lens camera phones. We call this problem stereo magnification, and propose
a learning framework that leverages a new layered representation that we call
multiplane images (MPIs). Our method also uses a massive new data source for
learning view extrapolation: online videos on YouTube. Using data mined from
such videos, we train a deep network that predicts an MPI from an input stereo
image pair. This inferred MPI can then be used to synthesize a range of novel
views of the scene, including views that extrapolate significantly beyond the
input baseline. We show that our method compares favorably with several recent
view synthesis methods, and demonstrate applications in magnifying
narrow-baseline stereo images.
Model-Independent Online Learning for Influence Maximization
Authors: Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks Lakshmanan, Mark Schmidt
Date: 2017-03-01 00:00:00
Proceeding: ICML 2017 8
Tasks: model

We consider influence maximization (IM) in social networks, which is the
problem of maximizing the number of users that become aware of a product by
selecting a set of "seed" users to expose the product to. While prior work
assumes a known model of information diffusion, we propose a novel
parametrization that not only makes our framework agnostic to the underlying
diffusion model, but also statistically efficient to learn from data. We give a
corresponding monotone, submodular surrogate function, and show that it is a
good approximation to the original IM objective. We also consider the case of a
new marketer looking to exploit an existing social network, while
simultaneously learning the factors governing information propagation. For
this, we propose a pairwise-influence semi-bandit feedback model and develop a
LinUCB-based bandit algorithm. Our model-independent analysis shows that our
regret bound has a better (as compared to previous work) dependence on the size
of the network. Experimental evaluation suggests that our framework is robust
to the underlying diffusion model and can efficiently learn a near-optimal
solution.
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
Authors: Anurag Ranjan, Varun Jampani, Lukas Balles, Kihwan Kim, Deqing Sun, Jonas Wulff, Michael J. Black
Date: 2018-05-24 00:00:00
Proceeding: CVPR 2019 6
Tasks: Depth Estimation, Depth Prediction, Monocular Depth Estimation, Motion Estimation, Motion Segmentation, Optical Flow Estimation

We address the unsupervised learning of several interconnected problems in
low-level vision: single view depth prediction, camera motion estimation,
optical flow, and segmentation of a video into the static scene and moving
regions. Our key insight is that these four fundamental vision problems are
coupled through geometric constraints. Consequently, learning to solve them
together simplifies the problem because the solutions can reinforce each other.
We go beyond previous work by exploiting geometry more explicitly and
segmenting the scene into static and moving regions. To that end, we introduce
Competitive Collaboration, a framework that facilitates the coordinated
training of multiple specialized neural networks to solve complex problems.
Competitive Collaboration works much like expectation-maximization, but with
neural networks that act as both competitors to explain pixels that correspond
to static or moving regions, and as collaborators through a moderator that
assigns pixels to be either static or independently moving. Our novel method
integrates all these problems in a common framework and simultaneously reasons
about the segmentation of the scene into moving objects and the static
background, the camera motion, depth of the static scene structure, and the
optical flow of moving objects. Our model is trained without any supervision
and achieves state-of-the-art performance among joint unsupervised methods on
all sub-problems.
Implicit Autoencoders
Authors: Alireza Makhzani
Date: 2018-05-24 00:00:00
Proceeding: ICLR 2019 5
Tasks: Clustering, Image-to-Image Translation, Translation

In this paper, we describe the "implicit autoencoder" (IAE), a generative
autoencoder in which both the generative path and the recognition path are
parametrized by implicit distributions. We use two generative adversarial
networks to define the reconstruction and the regularization cost functions of
the implicit autoencoder, and derive the learning rules based on
maximum-likelihood learning. Using implicit distributions allows us to learn
more expressive posterior and conditional likelihood distributions for the
autoencoder. Learning an expressive conditional likelihood distribution enables
the latent code to only capture the abstract and high-level information of the
data, while the remaining low-level information is captured by the implicit
conditional likelihood distribution. We show the applications of implicit
autoencoders in disentangling content and style information, clustering,
semi-supervised classification, learning expressive variational distributions,
and multimodal image-to-image translation from unpaired data.
Meta-Gradient Reinforcement Learning
Authors: Zhongwen Xu, Hado van Hasselt, David Silver
Date: 2018-05-24 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Meta-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The goal of reinforcement learning algorithms is to estimate and/or optimise
the value function. However, unlike supervised learning, no teacher or oracle
is available to provide the true value function. Instead, the majority of
reinforcement learning algorithms estimate and/or optimise a proxy for the
value function. This proxy is typically based on a sampled and bootstrapped
approximation to the true value function, known as a return. The particular
choice of return is one of the chief components determining the nature of the
algorithm: the rate at which future rewards are discounted; when and how values
should be bootstrapped; or even the nature of the rewards themselves. It is
well-known that these decisions are crucial to the overall success of RL
algorithms. We discuss a gradient-based meta-learning algorithm that is able to
adapt the nature of the return, online, whilst interacting and learning from
the environment. When applied to 57 games on the Atari 2600 environment over
200 million frames, our algorithm achieved a new state-of-the-art performance.
Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging
Authors: Nicha C. Dvornek, Daniel Yang, Archana Venkataraman, Pamela Ventola, Lawrence H. Staib, Kevin A. Pelphrey, James S. Duncan
Date: 2018-05-24 00:00:00

Treating children with autism spectrum disorders (ASD) with behavioral
interventions, such as Pivotal Response Treatment (PRT), has shown promise in
recent studies. However, deciding which therapy is best for a given patient is
largely by trial and error, and choosing an ineffective intervention results in
loss of valuable treatment time. We propose predicting patient response to PRT
from baseline task-based fMRI by the novel application of a random forest and
tree bagging strategy. Our proposed learning pipeline uses random forest
regression to determine candidate brain voxels that may be informative in
predicting treatment response. The candidate voxels are then tested stepwise
for inclusion in a bagged tree ensemble. After the predictive model is
constructed, bias correction is performed to further increase prediction
accuracy. Using data from 19 ASD children who underwent a 16 week trial of PRT
and a leave-one-out cross-validation framework, the presented learning pipeline
was tested against several standard methods and variations of the pipeline and
resulted in the highest prediction accuracy.
New Insights into Bootstrapping for Bandits
Authors: Sharan Vaswani, Branislav Kveton, Zheng Wen, Anup Rao, Mark Schmidt, Yasin Abbasi-Yadkori
Date: 2018-05-24 00:00:00
Tasks: Thompson Sampling

We investigate the use of bootstrapping in the bandit setting. We first show
that the commonly used non-parametric bootstrapping (NPB) procedure can be
provably inefficient and establish a near-linear lower bound on the regret
incurred by it under the bandit model with Bernoulli rewards. We show that NPB
with an appropriate amount of forced exploration can result in sub-linear
albeit sub-optimal regret. As an alternative to NPB, we propose a weighted
bootstrapping (WB) procedure. For Bernoulli rewards, WB with multiplicative
exponential weights is mathematically equivalent to Thompson sampling (TS) and
results in near-optimal regret bounds. Similarly, in the bandit setting with
Gaussian rewards, we show that WB with additive Gaussian weights achieves
near-optimal regret. Beyond these special cases, we show that WB leads to
better empirical performance than TS for several reward distributions bounded
on $[0,1]$. For the contextual bandit setting, we give practical guidelines
that make bootstrapping simple and efficient to implement and result in good
empirical performance on real-world datasets.
Multi-Task Zipping via Layer-wise Neuron Sharing
Authors: Xiaoxi He, Zimu Zhou, Lothar Thiele
Date: 2018-05-24 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Model Compression

Future mobile devices are anticipated to perceive, understand and react to
the world on their own by running multiple correlated deep neural networks
on-device. Yet the complexity of these neural networks needs to be trimmed down
both within-model and cross-model to fit in mobile storage and memory. Previous
studies focus on squeezing the redundancy within a single neural network. In
this work, we aim to reduce the redundancy across multiple models. We propose
Multi-Task Zipping (MTZ), a framework to automatically merge correlated,
pre-trained deep neural networks for cross-model compression. Central in MTZ is
a layer-wise neuron sharing and incoming weight updating scheme that induces a
minimal change in the error function. MTZ inherits information from each model
and demands light retraining to re-boost the accuracy of individual tasks.
Evaluations show that MTZ is able to fully merge the hidden layers of two
VGG-16 networks with a 3.18% increase in the test error averaged on ImageNet
and CelebA, or share 39.61% parameters between the two networks with <0.5%
increase in the test errors for both tasks. The number of iterations to retrain
the combined network is at least 17.8 times lower than that of training a
single VGG-16 network. Moreover, experiments show that MTZ is also able to
effectively merge multiple residual networks.
Improving Landmark Localization with Semi-Supervised Learning
Authors: Sina Honari, Pavlo Molchanov, Stephen Tyree, Pascal Vincent, Christopher Pal, Jan Kautz
Date: 2017-09-05 00:00:00
Proceeding: CVPR 2018 6
Tasks: Face Alignment, Small Data Image Classification

We present two techniques to improve landmark localization in images from
partially annotated datasets. Our primary goal is to leverage the common
situation where precise landmark locations are only provided for a small data
subset, but where class labels for classification or regression tasks related
to the landmarks are more abundantly available. First, we propose the framework
of sequential multitasking and explore it here through an architecture for
landmark localization where training with class labels acts as an auxiliary
signal to guide the landmark localization on unlabeled data. A key aspect of
our approach is that errors can be backpropagated through a complete landmark
localization model. Second, we propose and explore an unsupervised learning
technique for landmark localization based on having a model predict equivariant
landmarks with respect to transformations applied to the image. We show that
these techniques, improve landmark prediction considerably and can learn
effective detectors even when only a small fraction of the dataset has landmark
labels. We present results on two toy datasets and four real datasets, with
hands and faces, and report new state-of-the-art on two datasets in the wild,
e.g. with only 5\% of labeled images we outperform previous state-of-the-art
trained on the AFLW dataset.
Style Transfer Through Back-Translation
Authors: Shrimai Prabhumoye, Yulia Tsvetkov, Ruslan Salakhutdinov, Alan W. black
Date: 2018-04-24 00:00:00
Proceeding: ACL 2018 7
Tasks: Sentence, Style Transfer, Text Style Transfer, Translation

Style transfer is the task of rephrasing the text to contain specific
stylistic properties without changing the intent or affect within the context.
This paper introduces a new method for automatic style transfer. We first learn
a latent representation of the input sentence which is grounded in a language
translation model in order to better preserve the meaning of the sentence while
reducing stylistic properties. Then adversarial generation techniques are used
to make the output match the desired style. We evaluate this technique on three
different style transformations: sentiment, gender and political slant.
Compared to two state-of-the-art style transfer modeling techniques we show
improvements both in automatic evaluation of style transfer and in manual
evaluation of meaning preservation and fluency.