pwc-archive/papers-with-abstracts huggingface dataset:
Deep learning to represent sub-grid processes in climate models
Authors: Stephan Rasp, Michael S. Pritchard, Pierre Gentine
Date: 2018-06-12 00:00:00
Tasks: Deep Learning

The representation of nonlinear sub-grid processes, especially clouds, has
been a major source of uncertainty in climate models for decades.
Cloud-resolving models better represent many of these processes and can now be
run globally but only for short-term simulations of at most a few years because
of computational limitations. Here we demonstrate that deep learning can be
used to capture many advantages of cloud-resolving modeling at a fraction of
the computational cost. We train a deep neural network to represent all
atmospheric sub-grid processes in a climate model by learning from a
multi-scale model in which convection is treated explicitly. The trained neural
network then replaces the traditional sub-grid parameterizations in a global
general circulation model in which it freely interacts with the resolved
dynamics and the surface-flux scheme. The prognostic multi-year simulations are
stable and closely reproduce not only the mean climate of the cloud-resolving
simulation but also key aspects of variability, including precipitation
extremes and the equatorial wave spectrum. Furthermore, the neural network
approximately conserves energy despite not being explicitly instructed to.
Finally, we show that the neural network parameterization generalizes to new
surface forcing patterns but struggles to cope with temperatures far outside
its training manifold. Our results show the feasibility of using deep learning
for climate model parameterization. In a broader context, we anticipate that
data-driven Earth System Model development could play a key role in reducing
climate prediction uncertainty in the coming decade.
Grounded Textual Entailment
Authors: Hoa Trong Vu, Claudio Greco, Aliia Erofeeva, Somayeh Jafaritazehjan, Guido Linders, Marc Tanti, Alberto Testoni, Raffaella Bernardi, Albert Gatt
Date: 2018-06-14 00:00:00
Proceeding: COLING 2018 8
Tasks: Natural Language Inference

Capturing semantic relations between sentences, such as entailment, is a
long-standing challenge for computational semantics. Logic-based models analyse
entailment in terms of possible worlds (interpretations, or situations) where a
premise P entails a hypothesis H iff in all worlds where P is true, H is also
true. Statistical models view this relationship probabilistically, addressing
it in terms of whether a human would likely infer H from P. In this paper, we
wish to bridge these two perspectives, by arguing for a visually-grounded
version of the Textual Entailment task. Specifically, we ask whether models can
perform better if, in addition to P and H, there is also an image
(corresponding to the relevant "world" or "situation"). We use a multimodal
version of the SNLI dataset (Bowman et al., 2015) and we compare "blind" and
visually-augmented models of textual entailment. We show that visual
information is beneficial, but we also conduct an in-depth error analysis that
reveals that current multimodal models are not performing "grounding" in an
optimal fashion.
Evaluation of Unsupervised Compositional Representations
Authors: Hanan Aldarmaki, Mona Diab
Date: 2018-06-12 00:00:00
Proceeding: COLING 2018 8
Tasks: General Classification

We evaluated various compositional models, from bag-of-words representations
to compositional RNN-based models, on several extrinsic supervised and
unsupervised evaluation benchmarks. Our results confirm that weighted vector
averaging can outperform context-sensitive models in most benchmarks, but
structural features encoded in RNN models can also be useful in certain
classification tasks. We analyzed some of the evaluation datasets to identify
the aspects of meaning they measure and the characteristics of the various
models that explain their performance variance.
Self-Imitation Learning
Authors: Junhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee
Date: 2018-06-14 00:00:00
Proceeding: ICML 2018 7
Tasks: Atari Games, Imitation Learning, MuJoCo

This paper proposes Self-Imitation Learning (SIL), a simple off-policy
actor-critic algorithm that learns to reproduce the agent's past good
decisions. This algorithm is designed to verify our hypothesis that exploiting
past good experiences can indirectly drive deep exploration. Our empirical
results show that SIL significantly improves advantage actor-critic (A2C) on
several hard exploration Atari games and is competitive to the state-of-the-art
count-based exploration methods. We also show that SIL improves proximal policy
optimization (PPO) on MuJoCo tasks.
Teaching Multiple Concepts to a Forgetful Learner
Authors: Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, Adish Singla
Date: 2018-05-21 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Scheduling

How can we help a forgetful learner learn multiple concepts within a limited time frame? While there have been extensive studies in designing optimal schedules for teaching a single concept given a learner's memory model, existing approaches for teaching multiple concepts are typically based on heuristic scheduling techniques without theoretical guarantees. In this paper, we look at the problem from the perspective of discrete optimization and introduce a novel algorithmic framework for teaching multiple concepts with strong performance guarantees. Our framework is both generic, allowing the design of teaching schedules for different memory models, and also interactive, allowing the teacher to adapt the schedule to the underlying forgetting mechanisms of the learner. Furthermore, for a well-known memory model, we are able to identify a regime of model parameters where our framework is guaranteed to achieve high performance. We perform extensive evaluations using simulations along with real user studies in two concrete applications: (i) an educational app for online vocabulary teaching; and (ii) an app for teaching novices how to recognize animal species from images. Our results demonstrate the effectiveness of our algorithm compared to popular heuristic approaches.
Learning in POMDPs with Monte Carlo Tree Search
Authors: Sammie Katt, Frans A. Oliehoek, Christopher Amato
Date: 2018-06-14 00:00:00
Proceeding: ICML 2017 8

The POMDP is a powerful framework for reasoning under outcome and information
uncertainty, but constructing an accurate POMDP model is difficult.
Bayes-Adaptive Partially Observable Markov Decision Processes (BA-POMDPs)
extend POMDPs to allow the model to be learned during execution. BA-POMDPs are
a Bayesian RL approach that, in principle, allows for an optimal trade-off
between exploitation and exploration. Unfortunately, BA-POMDPs are currently
impractical to solve for any non-trivial domain. In this paper, we extend the
Monte-Carlo Tree Search method POMCP to BA-POMDPs and show that the resulting
method, which we call BA-POMCP, is able to tackle problems that previous
solution methods have been unable to solve. Additionally, we introduce several
techniques that exploit the BA-POMDP structure to improve the efficiency of
BA-POMCP along with proof of their convergence.
VoxCeleb2: Deep Speaker Recognition
Authors: Joon Son Chung, Arsha Nagrani, Andrew Zisserman
Date: 2018-06-14 00:00:00
Tasks: Speaker Recognition, Speaker Verification

The objective of this paper is speaker recognition under noisy and
unconstrained conditions.
  We make two key contributions. First, we introduce a very large-scale
audio-visual speaker recognition dataset collected from open-source media.
Using a fully automated pipeline, we curate VoxCeleb2 which contains over a
million utterances from over 6,000 speakers. This is several times larger than
any publicly available speaker recognition dataset.
  Second, we develop and compare Convolutional Neural Network (CNN) models and
training strategies that can effectively recognise identities from voice under
various conditions. The models trained on the VoxCeleb2 dataset surpass the
performance of previous works on a benchmark dataset by a significant margin.
DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
Authors: Berta Bescos, José M. Fácil, Javier Civera, José Neira
Date: 2018-06-14 00:00:00
Tasks: Autonomous Vehicles, object-detection, Object Detection

The assumption of scene rigidity is typical in SLAM algorithms. Such a strong
assumption limits the use of most visual SLAM systems in populated real-world
environments, which are the target of several relevant applications like
service robotics or autonomous vehicles. In this paper we present DynaSLAM, a
visual SLAM system that, building over ORB-SLAM2 [1], adds the capabilities of
dynamic object detection and background inpainting. DynaSLAM is robust in
dynamic scenarios for monocular, stereo and RGB-D configurations. We are
capable of detecting the moving objects either by multi-view geometry, deep
learning or both. Having a static map of the scene allows inpainting the frame
background that has been occluded by such dynamic objects. We evaluate our
system in public monocular, stereo and RGB-D datasets. We study the impact of
several accuracy/speed trade-offs to assess the limits of the proposed
methodology. DynaSLAM outperforms the accuracy of standard visual SLAM
baselines in highly dynamic scenarios. And it also estimates a map of the
static parts of the scene, which is a must for long-term applications in
real-world environments.
Stochastic Variance-Reduced Policy Gradient
Authors: Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli
Date: 2018-06-14 00:00:00
Proceeding: ICML 2018 7
Tasks: Reinforcement Learning

In this paper, we propose a novel reinforcement- learning algorithm
consisting in a stochastic variance-reduced version of policy gradient for
solving Markov Decision Processes (MDPs). Stochastic variance-reduced gradient
(SVRG) methods have proven to be very successful in supervised learning.
However, their adaptation to policy gradient is not straightforward and needs
to account for I) a non-concave objective func- tion; II) approximations in the
full gradient com- putation; and III) a non-stationary sampling pro- cess. The
result is SVRPG, a stochastic variance- reduced policy gradient algorithm that
leverages on importance weights to preserve the unbiased- ness of the gradient
estimate. Under standard as- sumptions on the MDP, we provide convergence
guarantees for SVRPG with a convergence rate that is linear under increasing
batch sizes. Finally, we suggest practical variants of SVRPG, and we
empirically evaluate them on continuous MDPs.
From Self-ception to Image Self-ception: A method to represent an image with its own approximations
Authors: Hamed Shah-Hosseini
Date: 2018-06-14 00:00:00

A concept of defining images based on its own approximate ones is proposed
here, which is called 'Self-ception'. In this regard, an algorithm is proposed
to implement the self-ception for images, which we call it 'Image Self-ception'
since we use it for images. We can control the accuracy of this self-ception
representation by deciding how many segments or regions we want to use for the
representation. Some self-ception images are included in the paper. The video
versions of the proposed image self-ception algorithm in action are shown in a
YouTube channel (find it by Googling image self-ception).
Gender Prediction in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System
Authors: Ankush Khandelwal, Sahil Swami, Syed Sarfaraz Akhtar, Manish Shrivastava
Date: 2018-06-14 00:00:00
Tasks: Author Profiling, Gender Prediction, General Classification, Language Identification, text-classification, Text Classification, Transliteration

The rapid expansion in the usage of social media networking sites leads to a
huge amount of unprocessed user generated data which can be used for text
mining. Author profiling is the problem of automatically determining profiling
aspects like the author's gender and age group through a text is gaining much
popularity in computational linguistics. Most of the past research in author
profiling is concentrated on English texts \cite{1,2}. However many users often
change the language while posting on social media which is called code-mixing,
and it develops some challenges in the field of text classification and author
profiling like variations in spelling, non-grammatical structure and
transliteration \cite{3}. There are very few English-Hindi code-mixed annotated
datasets of social media content present online \cite{4}. In this paper, we
analyze the task of author's gender prediction in code-mixed content and
present a corpus of English-Hindi texts collected from Twitter which is
annotated with author's gender. We also explore language identification of
every word in this corpus. We present a supervised classification baseline
system which uses various machine learning algorithms to identify the gender of
an author using a text, based on character and word level features.
A Survey on Open Information Extraction
Authors: Christina Niklaus, Matthias Cetto, André Freitas, Siegfried Handschuh
Date: 2018-06-14 00:00:00
Proceeding: COLING 2018 8
Tasks: Open Information Extraction, Survey

We provide a detailed overview of the various approaches that were proposed
to date to solve the task of Open Information Extraction. We present the major
challenges that such systems face, show the evolution of the suggested
approaches over time and depict the specific issues they address. In addition,
we provide a critique of the commonly applied evaluation procedures for
assessing the performance of Open IE systems and highlight some directions for
future work.
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
Authors: Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson
Date: 2018-06-14 00:00:00
Proceeding: ICLR 2019 5
Tasks: Domain Adaptation, Semi-Supervised Image Classification

Presently the most successful approaches to semi-supervised learning are
based on consistency regularization, whereby a model is trained to be robust to
small perturbations of its inputs and parameters. To understand consistency
regularization, we conceptually explore how loss geometry interacts with
training procedures. The consistency loss dramatically improves generalization
performance over supervised-only training; however, we show that SGD struggles
to converge on the consistency loss and continues to make large steps that lead
to changes in predictions on the test data. Motivated by these observations, we
propose to train consistency-based methods with Stochastic Weight Averaging
(SWA), a recent approach which averages weights along the trajectory of SGD
with a modified learning rate schedule. We also propose fast-SWA, which further
accelerates convergence by averaging multiple points within each cycle of a
cyclical learning rate schedule. With weight averaging, we achieve the best
known semi-supervised results on CIFAR-10 and CIFAR-100, over many different
quantities of labeled training data. For example, we achieve 5.0% error on
CIFAR-10 with only 4000 labels, compared to the previous best result in the
literature of 6.3%.
Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation
Authors: Jichao Zhang, Yezhi Shu, Songhua Xu, Gongze Cao, Fan Zhong, Meng Liu, Xueying Qin
Date: 2018-05-19 00:00:00
Tasks: Attribute, Image-to-Image Translation, Multi-Task Learning, Style Transfer, Translation

Recent Image-to-Image Translation algorithms have achieved significant progress in neural style transfer and image attribute manipulation tasks. However, existing approaches require exhaustively labelling training data, which is labor demanding, difficult to scale up, and hard to migrate into new domains. To overcome such a key limitation, we propose Sparsely Grouped Generative Adversarial Networks (SG-GAN) as a novel approach that can translate images on sparsely grouped datasets where only a few samples for training are labelled. Using a novel one-input multi-output architecture, SG-GAN is well-suited for tackling sparsely grouped learning and multi-task learning. The proposed model can translate images among multiple groups using only a single commonly trained model. To experimentally validate advantages of the new model, we apply the proposed method to tackle a series of attribute manipulation tasks for facial images. Experimental results demonstrate that SG-GAN can generate image translation results of comparable quality with baselines methods on adequately labelled datasets and results of superior quality on sparsely grouped datasets. The official implementation is publicly available:https://github.com/zhangqianhui/Sparsely-Grouped-GAN.
Tract orientation mapping for bundle-specific tractography
Authors: Jakob Wasserthal, Peter F. Neher, Klaus H. Maier-Hein
Date: 2018-06-14 00:00:00
Tasks: Clustering, Decoder, Diffusion  MRI

While the major white matter tracts are of great interest to numerous studies
in neuroscience and medicine, their manual dissection in larger cohorts from
diffusion MRI tractograms is time-consuming, requires expert knowledge and is
hard to reproduce. Tract orientation mapping (TOM) is a novel concept that
facilitates bundle-specific tractography based on a learned mapping from the
original fiber orientation distribution function (fODF) peaks to a list of
tract orientation maps (also abbr. TOM). Each TOM represents one of the known
tracts with each voxel containing no more than one orientation vector. TOMs can
act as a prior or even as direct input for tractography. We use an
encoder-decoder fully-convolutional neural network architecture to learn the
required mapping. In comparison to previous concepts for the reconstruction of
specific bundles, the presented one avoids various cumbersome processing steps
like whole brain tractography, atlas registration or clustering. We compare it
to four state of the art bundle recognition methods on 20 different bundles in
a total of 105 subjects from the Human Connectome Project. Results are
anatomically convincing even for difficult tracts, while reaching low angular
errors, unprecedented runtimes and top accuracy values (Dice). Our code and our
data are openly available.
LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled Variables
Authors: James Herring, James Nagy, Lars Ruthotto
Date: 2017-05-28 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Many inverse problems involve two or more sets of variables that represent
different physical quantities but are tightly coupled with each other. For
example, image super-resolution requires joint estimation of the image and
motion parameters from noisy measurements. Exploiting this structure is key for
efficiently solving these large-scale optimization problems, which are often
ill-conditioned.
  In this paper, we present a new method called Linearize And Project (LAP)
that offers a flexible framework for solving inverse problems with coupled
variables. LAP is most promising for cases when the subproblem corresponding to
one of the variables is considerably easier to solve than the other. LAP is
based on a Gauss-Newton method, and thus after linearizing the residual, it
eliminates one block of variables through projection. Due to the linearization,
this block can be chosen freely. Further, LAP supports direct, iterative, and
hybrid regularization as well as constraints. Therefore LAP is attractive,
e.g., for ill-posed imaging problems. These traits differentiate LAP from
common alternatives for this type of problem such as variable projection
(VarPro) and block coordinate descent (BCD). Our numerical experiments compare
the performance of LAP to BCD and VarPro using three coupled problems whose
forward operators are linear with respect to one block and nonlinear for the
other set of variables.
Fruit recognition from images using deep learning
Authors: Horea Mureşan, Mihai Oltean
Date: 2017-12-02 00:00:00
Tasks: Deep Learning

In this paper we introduce a new, high-quality, dataset of images containing
fruits. We also present the results of some numerical experiment for training a
neural network to detect fruits. We discuss the reason why we chose to use
fruits in this project by proposing a few applications that could use this kind
of neural network.
Autoregressive Quantile Networks for Generative Modeling
Authors: Georg Ostrovski, Will Dabney, Rémi Munos
Date: 2018-06-14 00:00:00
Proceeding: ICML 2018 7
Tasks: Diversity, quantile regression, regression

We introduce autoregressive implicit quantile networks (AIQN), a
fundamentally different approach to generative modeling than those commonly
used, that implicitly captures the distribution using quantile regression. AIQN
is able to achieve superior perceptual quality and improvements in evaluation
metrics, without incurring a loss of sample diversity. The method can be
applied to many existing models and architectures. In this work we extend the
PixelCNN model with AIQN and demonstrate results on CIFAR-10 and ImageNet using
Inception score, FID, non-cherry-picked samples, and inpainting results. We
consistently observe that AIQN yields a highly stable algorithm that improves
perceptual quality while maintaining a highly diverse distribution.
Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos
Authors: Armine Vardazaryan, Didier Mutter, Jacques Marescaux, Nicolas Padoy
Date: 2018-06-14 00:00:00
Tasks: Surgical tool detection, Weakly-supervised Learning

Surgical tool localization is an essential task for the automatic analysis of
endoscopic videos. In the literature, existing methods for tool localization,
tracking and segmentation require training data that is fully annotated,
thereby limiting the size of the datasets that can be used and the
generalization of the approaches. In this work, we propose to circumvent the
lack of annotated data with weak supervision. We propose a deep architecture,
trained solely on image level annotations, that can be used for both tool
presence detection and localization in surgical videos. Our architecture relies
on a fully convolutional neural network, trained end-to-end, enabling us to
localize surgical tools without explicit spatial annotations. We demonstrate
the benefits of our approach on a large public dataset, Cholec80, which is
fully annotated with binary tool presence information and of which 5 videos
have been fully annotated with bounding boxes and tool centers for the
evaluation.