pwc-archive/papers-with-abstracts huggingface dataset:
Cross-Domain 3D Equivariant Image Embeddings
Authors: Carlos Esteves, Avneesh Sud, Zhengyi Luo, Kostas Daniilidis, Ameesh Makadia
Date: 2018-12-06 00:00:00
Tasks: 3D Shape Classification, Novel View Synthesis, Object, Pose Estimation

Spherical convolutional networks have been introduced recently as tools to learn powerful feature representations of 3D shapes. Spherical CNNs are equivariant to 3D rotations making them ideally suited to applications where 3D data may be observed in arbitrary orientations. In this paper we learn 2D image embeddings with a similar equivariant structure: embedding the image of a 3D object should commute with rotations of the object. We introduce a cross-domain embedding from 2D images into a spherical CNN latent space. This embedding encodes images with 3D shape properties and is equivariant to 3D rotations of the observed object. The model is supervised only by target embeddings obtained from a spherical CNN pretrained for 3D shape classification. We show that learning a rich embedding for images with appropriate geometric structure is sufficient for tackling varied applications, such as relative pose estimation and novel view synthesis, without requiring additional task-specific supervision.
PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding
Authors: Kaichun Mo, Shilin Zhu, Angel X. Chang, Li Yi, Subarna Tripathi, Leonidas J. Guibas, Hao Su
Date: 2018-12-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Instance Segmentation, 3D Semantic Segmentation, Benchmarking, Instance Segmentation, Segmentation, Semantic Segmentation

We present PartNet: a consistent, large-scale dataset of 3D objects annotated
with fine-grained, instance-level, and hierarchical 3D part information. Our
dataset consists of 573,585 part instances over 26,671 3D models covering 24
object categories. This dataset enables and serves as a catalyst for many tasks
such as shape analysis, dynamic 3D scene modeling and simulation, affordance
analysis, and others. Using our dataset, we establish three benchmarking tasks
for evaluating 3D part recognition: fine-grained semantic segmentation,
hierarchical semantic segmentation, and instance segmentation. We benchmark
four state-of-the-art 3D deep learning algorithms for fine-grained semantic
segmentation and three baseline methods for hierarchical semantic segmentation.
We also propose a novel method for part instance segmentation and demonstrate
its superior performance over existing methods.
Formal Synthesis of Analytic Controllers for Sampled-Data Systems via Genetic Programming
Authors: Cees F. Verdier, Manuel Mazo Jr
Date: 2018-12-06 00:00:00

This paper presents an automatic formal controller synthesis method for
nonlinear sampled-data systems with safety and reachability specifications.
Fundamentally, the presented method is not restricted to polynomial systems and
controllers. We consider periodically switched controllers based on a Control
Lyapunov Barrier-like functions. The proposed method utilizes genetic
programming to synthesize these functions as well as the controller modes.
Correctness of the controller are subsequently verified by means of a
Satisfiability Modulo Theories solver. Effectiveness of the proposed
methodology is demonstrated on multiple systems.
On stochastic gradient Langevin dynamics with dependent data streams in the logconcave case
Authors: M. Barkhagen, N. H. Chau, É. Moulines, M. Rásonyi, S. Sabanis, Y. Zhang
Date: 2018-12-06 00:00:00
Tasks: Stochastic Optimization

We study the problem of sampling from a probability distribution $\pi$ on $\rset^d$ which has a density \wrt\ the Lebesgue measure known up to a normalization factor $x \mapsto \rme^{-U(x)} / \int_{\rset^d} \rme^{-U(y)} \rmd y$. We analyze a sampling method based on the Euler discretization of the Langevin stochastic differential equations under the assumptions that the potential $U$ is continuously differentiable, $\nabla U$ is Lipschitz, and $U$ is strongly concave. We focus on the case where the gradient of the log-density cannot be directly computed but unbiased estimates of the gradient from possibly dependent observations are available. This setting can be seen as a combination of a stochastic approximation (here stochastic gradient) type algorithms with discretized Langevin dynamics. We obtain an upper bound of the Wasserstein-2 distance between the law of the iterates of this algorithm and the target distribution $\pi$ with constants depending explicitly on the Lipschitz and strong convexity constants of the potential and the dimension of the space. Finally, under weaker assumptions on $U$ and its gradient but in the presence of independent observations, we obtain analogous results in Wasserstein-2 distance.
Video Action Transformer Network
Authors: Rohit Girdhar, João Carreira, Carl Doersch, Andrew Zisserman
Date: 2018-12-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Recognition, Recognizing And Localizing Human Actions

We introduce the Action Transformer model for recognizing and localizing human actions in video clips. We repurpose a Transformer-style architecture to aggregate features from the spatiotemporal context around the person whose actions we are trying to classify. We show that by using high-resolution, person-specific, class-agnostic queries, the model spontaneously learns to track individual people and to pick up on semantic context from the actions of others. Additionally its attention mechanism learns to emphasize hands and faces, which are often crucial to discriminate an action - all without explicit supervision other than boxes and class labels. We train and test our Action Transformer network on the Atomic Visual Actions (AVA) dataset, outperforming the state-of-the-art by a significant margin using only raw RGB frames as input.
Coarse-Graining Auto-Encoders for Molecular Dynamics
Authors: Wujie Wang, Rafael Gómez-Bombarelli
Date: 2018-12-06 00:00:00

Molecular dynamics simulations provide theoretical insight into the
microscopic behavior of materials in condensed phase and, as a predictive tool,
enable computational design of new compounds. However, because of the large
temporal and spatial scales involved in thermodynamic and kinetic phenomena in
materials, atomistic simulations are often computationally unfeasible.
Coarse-graining methods allow simulating larger systems, by reducing the
dimensionality of the simulation, and propagating longer timesteps, by
averaging out fast motions. Coarse-graining involves two coupled learning
problems; defining the mapping from an all-atom to a reduced representation,
and the parametrization of a Hamiltonian over coarse-grained coordinates.
Multiple statistical mechanics approaches have addressed the latter, but the
former is generally a hand-tuned process based on chemical intuition. Here we
present Autograin, an optimization framework based on auto-encoders to learn
both tasks simultaneously. Autograin is trained to learn the optimal mapping
between all-atom and reduced representation, using the reconstruction loss to
facilitate the learning of coarse-grained variables. In addition, a
force-matching method is applied to variationally determine the coarse-grained
potential energy function. This procedure is tested on a number of model
systems including single-molecule and bulk-phase periodic simulations.
Online Model Distillation for Efficient Video Inference
Authors: Ravi Teja Mullapudi, Steven Chen, Keyi Zhang, Deva Ramanan, Kayvon Fatahalian
Date: 2018-12-06 00:00:00
Proceeding: ICCV 2019 10
Tasks: model, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

High-quality computer vision models typically address the problem of understanding the general distribution of real-world images. However, most cameras observe only a very small fraction of this distribution. This offers the possibility of achieving more efficient inference by specializing compact, low-cost models to the specific distribution of frames observed by a single camera. In this paper, we employ the technique of model distillation (supervising a low-cost student model using the output of a high-cost teacher) to specialize accurate, low-cost semantic segmentation models to a target video stream. Rather than learn a specialized student model on offline data from the video stream, we train the student in an online fashion on the live video, intermittently running the teacher to provide a target for learning. Online model distillation yields semantic segmentation models that closely approximate their Mask R-CNN teacher with 7 to 17$\times$ lower inference runtime cost (11 to 26$\times$ in FLOPs), even when the target video's distribution is non-stationary. Our method requires no offline pretraining on the target video stream, achieves higher accuracy and lower cost than solutions based on flow or video object segmentation, and can exhibit better temporal stability than the original teacher. We also provide a new video dataset for evaluating the efficiency of inference over long running video streams.
Differentially Private Fair Learning
Authors: Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman
Date: 2018-12-06 00:00:00
Tasks: Attribute, Fairness

Motivated by settings in which predictive models may be required to be non-discriminatory with respect to certain attributes (such as race), but even collecting the sensitive attribute may be forbidden or restricted, we initiate the study of fair learning under the constraint of differential privacy. We design two learning algorithms that simultaneously promise differential privacy and equalized odds, a 'fairness' condition that corresponds to equalizing false positive and negative rates across protected groups. Our first algorithm is a private implementation of the equalized odds post-processing approach of [Hardt et al., 2016]. This algorithm is appealingly simple, but must be able to use protected group membership explicitly at test time, which can be viewed as a form of 'disparate treatment'. Our second algorithm is a differentially private version of the oracle-efficient in-processing approach of [Agarwal et al., 2018] that can be used to find the optimal fair classifier, given access to a subroutine that can solve the original (not necessarily fair) learning problem. This algorithm is more complex but need not have access to protected group membership at test time. We identify new tradeoffs between fairness, accuracy, and privacy that emerge only when requiring all three properties, and show that these tradeoffs can be milder if group membership may be used at test time. We conclude with a brief experimental evaluation.
Provably Efficient Maximum Entropy Exploration
Authors: Elad Hazan, Sham M. Kakade, Karan Singh, Abby Van Soest
Date: 2018-12-06 00:00:00

Suppose an agent is in a (possibly unknown) Markov Decision Process in the
absence of a reward signal, what might we hope that an agent can efficiently
learn to do? This work studies a broad class of objectives that are defined
solely as functions of the state-visitation frequencies that are induced by how
the agent behaves. For example, one natural, intrinsically defined, objective
problem is for the agent to learn a policy which induces a distribution over
state space that is as uniform as possible, which can be measured in an
entropic sense. We provide an efficient algorithm to optimize such such
intrinsically defined objectives, when given access to a black box planning
oracle (which is robust to function approximation). Furthermore, when
restricted to the tabular setting where we have sample based access to the MDP,
our proposed algorithm is provably efficient, both in terms of its sample and
computational complexities. Key to our algorithmic methodology is utilizing the
conditional gradient method (a.k.a. the Frank-Wolfe algorithm) which utilizes
an approximate MDP solver.
$β$-VAEs can retain label information even at high compression
Authors: Emily Fertig, Aryan Arbabi, Alexander A. Alemi
Date: 2018-12-06 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper, we investigate the degree to which the encoding of a
$\beta$-VAE captures label information across multiple architectures on Binary
Static MNIST and Omniglot. Even though they are trained in a completely
unsupervised manner, we demonstrate that a $\beta$-VAE can retain a large
amount of label information, even when asked to learn a highly compressed
representation.
Theoretical Guarantees of Deep Embedding Losses Under Label Noise
Authors: Nam Le, Jean-Marc Odobez
Date: 2018-12-06 00:00:00
Tasks: Triplet, Weakly-supervised Learning

Collecting labeled data to train deep neural networks is costly and even
impractical for many tasks. Thus, research effort has been focused in
automatically curated datasets or unsupervised and weakly supervised learning.
The common problem in these directions is learning with unreliable label
information. In this paper, we address the tolerance of deep embedding learning
losses against label noise, i.e. when the observed labels are different from
the true labels. Specifically, we provide the sufficient conditions to achieve
theoretical guarantees for the 2 common loss functions: marginal loss and
triplet loss. From these theoretical results, we can estimate how sampling
strategies and initialization can affect the level of resistance against label
noise. The analysis also helps providing more effective guidelines in
unsupervised and weakly supervised deep embedding learning.
Recursive Visual Attention in Visual Dialog
Authors: Yulei Niu, Hanwang Zhang, Manli Zhang, Jianhong Zhang, Zhiwu Lu, Ji-Rong Wen
Date: 2018-12-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Question Answering, Visual Dialog, Visual Question Answering, Visual Question Answering (VQA)

Visual dialog is a challenging vision-language task, which requires the agent
to answer multi-round questions about an image. It typically needs to address
two major problems: (1) How to answer visually-grounded questions, which is the
core challenge in visual question answering (VQA); (2) How to infer the
co-reference between questions and the dialog history. An example of visual
co-reference is: pronouns (\eg, ``they'') in the question (\eg, ``Are they on
or off?'') are linked with nouns (\eg, ``lamps'') appearing in the dialog
history (\eg, ``How many lamps are there?'') and the object grounded in the
image. In this work, to resolve the visual co-reference for visual dialog, we
propose a novel attention mechanism called Recursive Visual Attention (RvA).
Specifically, our dialog agent browses the dialog history until the agent has
sufficient confidence in the visual co-reference resolution, and refines the
visual attention recursively. The quantitative and qualitative experimental
results on the large-scale VisDial v0.9 and v1.0 datasets demonstrate that the
proposed RvA not only outperforms the state-of-the-art methods, but also
achieves reasonable recursion and interpretable attention maps without
additional annotations. The code is available at
\url{https://github.com/yuleiniu/rva}.
Feature Analysis for Assessing the Quality of Wikipedia Articles through Supervised Classification
Authors: Elias Bassani, Marco Viviani
Date: 2018-12-06 00:00:00
Tasks: Articles, General Classification, Misinformation

Nowadays, thanks to Web 2.0 technologies, people have the possibility to
generate and spread contents on different social media in a very easy way. In
this context, the evaluation of the quality of the information that is
available online is becoming more and more a crucial issue. In fact, a constant
flow of contents is generated every day by often unknown sources, which are not
certified by traditional authoritative entities. This requires the development
of appropriate methodologies that can evaluate in a systematic way these
contents, based on `objective' aspects connected with them. This would help
individuals, who nowadays tend to increasingly form their opinions based on
what they read online and on social media, to come into contact with
information that is actually useful and verified. Wikipedia is nowadays one of
the biggest online resources on which users rely as a source of information.
The amount of collaboratively generated content that is sent to the online
encyclopedia every day can let to the possible creation of low-quality articles
(and, consequently, misinformation) if not properly monitored and revised. For
this reason, in this paper, the problem of automatically assessing the quality
of Wikipedia articles is considered. In particular, the focus is on the
analysis of hand-crafted features that can be employed by supervised machine
learning techniques to perform the classification of Wikipedia articles on
qualitative bases. With respect to prior literature, a wider set of
characteristics connected to Wikipedia articles are taken into account and
illustrated in detail. Evaluations are performed by considering a labeled
dataset provided in a prior work, and different supervised machine learning
algorithms, which produced encouraging results with respect to the considered
features.
Deep Reinforcement Learning and the Deadly Triad
Authors: Hado van Hasselt, Yotam Doron, Florian Strub, Matteo Hessel, Nicolas Sonnerat, Joseph Modayil
Date: 2018-12-06 00:00:00
Tasks: Deep Reinforcement Learning, Learning Theory, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We know from reinforcement learning theory that temporal difference learning
can fail in certain cases. Sutton and Barto (2018) identify a deadly triad of
function approximation, bootstrapping, and off-policy learning. When these
three properties are combined, learning can diverge with the value estimates
becoming unbounded. However, several algorithms successfully combine these
three properties, which indicates that there is at least a partial gap in our
understanding. In this work, we investigate the impact of the deadly triad in
practice, in the context of a family of popular deep reinforcement learning
models - deep Q-networks trained with experience replay - analysing how the
components of this system play a role in the emergence of the deadly triad, and
in the agent's performance
Local Conditioning: Exact Message Passing for Cyclic Undirected Distributed Networks
Authors: Matthew G. Reyes
Date: 2018-12-06 00:00:00
Tasks: Clustering

This paper addresses practical implementation of summing out, expanding, and
reordering of messages in Local Conditioning (LC) for undirected networks. In
particular, incoming messages conditioned on potentially different subsets of
the receiving node's relevant set must be expanded to be conditioned on this
relevant set, then reordered so that corresponding columns of the conditioned
matrices can be fused through element-wise multiplication. An outgoing message
is then reduced by summing out loop cutset nodes that are upstream of the
outgoing edge. The emphasis on implementation is the primary contribution over
the theoretical justification of LC given in Fay et al. Nevertheless, the
complexity of Local Conditioning in grid networks is still no better than that
of Clustering.
Pathological Evidence Exploration in Deep Retinal Image Diagnosis
Authors: Yuhao Niu, Lin Gu, Feng Lu, Feifan Lv, Zongji Wang, Imari Sato, Zijian Zhang, Yangyan Xiao, Xunzhang Dai, Tingting Cheng
Date: 2018-12-06 00:00:00
Tasks: Medical Diagnosis

Though deep learning has shown successful performance in classifying the
label and severity stage of certain disease, most of them give few evidence on
how to make prediction. Here, we propose to exploit the interpretability of
deep learning application in medical diagnosis. Inspired by Koch's Postulates,
a well-known strategy in medical research to identify the property of pathogen,
we define a pathological descriptor that can be extracted from the activated
neurons of a diabetic retinopathy detector. To visualize the symptom and
feature encoded in this descriptor, we propose a GAN based method to synthesize
pathological retinal image given the descriptor and a binary vessel
segmentation. Besides, with this descriptor, we can arbitrarily manipulate the
position and quantity of lesions. As verified by a panel of 5 licensed
ophthalmologists, our synthesized images carry the symptoms that are directly
related to diabetic retinopathy diagnosis. The panel survey also shows that our
generated images is both qualitatively and quantitatively superior to existing
methods.
MMA Training: Direct Input Space Margin Maximization through Adversarial Training
Authors: Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui, Ruitong Huang
Date: 2018-12-06 00:00:00
Proceeding: ICLR 2020 1
Tasks: Adversarial Defense, Adversarial Robustness

We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundary. Our study shows that maximizing margins can be achieved by minimizing the adversarial loss on the decision boundary at the "shortest successful perturbation", demonstrating a close connection between adversarial losses and the margins. We propose Max-Margin Adversarial (MMA) training to directly maximize the margins to achieve adversarial robustness. Instead of adversarial training with a fixed $\epsilon$, MMA offers an improvement by enabling adaptive selection of the "correct" $\epsilon$ as the margin individually for each datapoint. In addition, we rigorously analyze adversarial training with the perspective of margin maximization, and provide an alternative interpretation for adversarial training, maximizing either a lower or an upper bound of the margins. Our experiments empirically confirm our theory and demonstrate MMA training's efficacy on the MNIST and CIFAR10 datasets w.r.t. $\ell_\infty$ and $\ell_2$ robustness. Code and models are available at https://github.com/BorealisAI/mma_training.
Traversing Latent Space using Decision Ferns
Authors: Yan Zuo, Gil Avraham, Tom Drummond
Date: 2018-12-06 00:00:00

The practice of transforming raw data to a feature space so that inference
can be performed in that space has been popular for many years. Recently, rapid
progress in deep neural networks has given both researchers and practitioners
enhanced methods that increase the richness of feature representations, be it
from images, text or speech. In this work we show how a constructed latent
space can be explored in a controlled manner and argue that this complements
well founded inference methods. For constructing the latent space a Variational
Autoencoder is used. We present a novel controller module that allows for
smooth traversal in the latent space and construct an end-to-end trainable
framework. We explore the applicability of our method for performing spatial
transformations as well as kinematics for predicting future latent vectors of a
video sequence.
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data
Authors: Pierre-Alexandre Mattei, Jes Frellsen
Date: 2018-12-06 00:00:00
Tasks: Imputation

We consider the problem of handling missing data with deep latent variable
models (DLVMs). First, we present a simple technique to train DLVMs when the
training set contains missing-at-random data. Our approach, called MIWAE, is
based on the importance-weighted autoencoder (IWAE), and maximises a
potentially tight lower bound of the log-likelihood of the observed data.
Compared to the original IWAE, our algorithm does not induce any additional
computational overhead due to the missing data. We also develop Monte Carlo
techniques for single and multiple imputation using a DLVM trained on an
incomplete data set. We illustrate our approach by training a convolutional
DLVM on a static binarisation of MNIST that contains 50% of missing pixels.
Leveraging multiple imputation, a convolutional network trained on these
incomplete digits has a test performance similar to one trained on complete
data. On various continuous and binary data sets, we also show that MIWAE
provides accurate single imputations, and is highly competitive with
state-of-the-art methods.