pwc-archive/papers-with-abstracts huggingface dataset:
Catch and Prolong: recurrent neural network for seeking track-candidates
Authors: Dmitriy Baranov, Gennady Ososkov, Pavel Goncharov, Andrei Tsytrinov
Date: 2018-11-14 00:00:00

One of the most important problems of data processing in high energy and
nuclear physics is the event reconstruction. Its main part is the track
reconstruction procedure which consists in looking for all tracks that
elementary particles leave when they pass through a detector among a huge
number of points, so-called hits, produced when flying particles fire detector
coordinate planes. Unfortunately, the tracking is seriously impeded by the
famous shortcoming of multiwired, strip and GEM detectors due to appearance in
them a lot of fake hits caused by extra spurious crossings of fired strips.
Since the number of those fakes is several orders of magnitude greater than for
true hits, one faces with the quite serious difficulty to unravel possible
track-candidates via true hits ignoring fakes. We introduce a renewed method
that is a significant improvement of our previous two-stage approach based on
hit preprocessing using directed K-d tree search followed a deep neural
classifier. We combine these two stages in one by applying recurrent neural
network that simultaneously determines whether a set of points belongs to a
true track or not and predicts where to look for the next point of track on the
next coordinate plane of the detector. We show that proposed deep network is
more accurate, faster and does not require any special preprocessing stage.
Preliminary results of our approach for simulated events of the BM@N GEM
detector are presented.
On the use of FHT, its modification for practical applications and the structure of Hough image
Authors: M. Aliev, E. I. Ershov, D. P. Nikolaev
Date: 2018-11-14 00:00:00

This work focuses on the Fast Hough Transform (FHT) algorithm proposed by
M.L. Brady. We propose how to modify the standard FHT to calculate sums along
lines within any given range of their inclination angles. We also describe a
new way to visualise Hough-image based on regrouping of accumulator space
around its center. Finally, we prove that using Brady parameterization
transforms any line into a figure of type "angle".
Machine Learning Analysis of Heterogeneity in the Effect of Student Mindset Interventions
Authors: Fredrik D. Johansson
Date: 2018-11-14 00:00:00
Tasks: BIG-bench Machine Learning

We study heterogeneity in the effect of a mindset intervention on
student-level performance through an observational dataset from the National
Study of Learning Mindsets (NSLM). Our analysis uses machine learning (ML) to
address the following associated problems: assessing treatment group overlap
and covariate balance, imputing conditional average treatment effects, and
interpreting imputed effects. By comparing several different model families we
illustrate the flexibility of both off-the-shelf and purpose-built estimators.
We find that the mindset intervention has a positive average effect of 0.26,
95%-CI [0.22, 0.30], and that heterogeneity in the range of [0.1, 0.4] is
moderated by school-level achievement level, poverty concentration, urbanicity,
and student prior expectations.
HMRF-EM-image: Implementation of the Hidden Markov Random Field Model and its Expectation-Maximization Algorithm
Authors: Quan Wang
Date: 2012-07-15 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

In this project, we study the hidden Markov random field (HMRF) model and its
expectation-maximization (EM) algorithm. We implement a MATLAB toolbox named
HMRF-EM-image for 2D image segmentation using the HMRF-EM framework. This
toolbox also implements edge-prior-preserving image segmentation, and can be
easily reconfigured for other problems, such as 3D image segmentation.
OpenCFU, a New Free and Open-Source Software to Count Cell Colonies and Other Circular Objects
Authors: Quentin Geissmann
Date: 2012-10-18 00:00:00

Counting circular objects such as cell colonies is an important source of
information for biologists. Although this task is often time-consuming and
subjective, it is still predominantly performed manually. The aim of the
present work is to provide a new tool to enumerate circular objects from
digital pictures and video streams. Here, I demonstrate that the created
program, OpenCFU, is very robust, accurate and fast. In addition, it provides
control over the processing parameters and is implemented in an in- tuitive and
modern interface. OpenCFU is a cross-platform and open-source software freely
available at http://opencfu.sourceforge.net.
DARCCC: Detecting Adversaries by Reconstruction from Class Conditional Capsules
Authors: Nicholas Frosst, Sara Sabour, Geoffrey Hinton
Date: 2018-11-16 00:00:00

We present a simple technique that allows capsule models to detect
adversarial images. In addition to being trained to classify images, the
capsule model is trained to reconstruct the images from the pose parameters and
identity of the correct top-level capsule. Adversarial images do not look like
a typical member of the predicted class and they have much larger
reconstruction errors when the reconstruction is produced from the top-level
capsule for that class. We show that setting a threshold on the $l2$ distance
between the input image and its reconstruction from the winning capsule is very
effective at detecting adversarial images for three different datasets. The
same technique works quite well for CNNs that have been trained to reconstruct
the image from all or part of the last hidden layer before the softmax. We then
explore a stronger, white-box attack that takes the reconstruction error into
account. This attack is able to fool our detection technique but in order to
make the model change its prediction to another class, the attack must
typically make the "adversarial" image resemble images of the other class.
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
Authors: Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Mia Xu Chen, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, Zhifeng Chen
Date: 2018-11-16 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Fine-Grained Image Classification, image-classification, Image Classification, Machine Translation, Translation

Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks. In many cases, increasing model capacity beyond the memory limit of a single accelerator has required developing special algorithms or infrastructure. These solutions are often architecture-specific and do not transfer to other tasks. To address the need for efficient and task-independent model parallelism, we introduce GPipe, a pipeline parallelism library that allows scaling any network that can be expressed as a sequence of layers. By pipelining different sub-sequences of layers on separate accelerators, GPipe provides the flexibility of scaling a variety of different networks to gigantic sizes efficiently. Moreover, GPipe utilizes a novel batch-splitting pipelining algorithm, resulting in almost linear speedup when a model is partitioned across multiple accelerators. We demonstrate the advantages of GPipe by training large-scale neural networks on two different tasks with distinct network architectures: (i) Image Classification: We train a 557-million-parameter AmoebaNet model and attain a top-1 accuracy of 84.4% on ImageNet-2012, (ii) Multilingual Neural Machine Translation: We train a single 6-billion-parameter, 128-layer Transformer model on a corpus spanning over 100 languages and achieve better quality than all bilingual models.
Grasp2Vec: Learning Object Representations from Self-Supervised Grasping
Authors: Eric Jang, Coline Devin, Vincent Vanhoucke, Sergey Levine
Date: 2018-11-16 00:00:00
Tasks: Object, Reinforcement Learning, Representation Learning

Well structured visual representations can make robot learning faster and can
improve generalization. In this paper, we study how we can acquire effective
object-centric representations for robotic manipulation tasks without human
labeling by using autonomous robot interaction with the environment. Such
representation learning methods can benefit from continuous refinement of the
representation as the robot collects more experience, allowing them to scale
effectively without human intervention. Our representation learning approach is
based on object persistence: when a robot removes an object from a scene, the
representation of that scene should change according to the features of the
object that was removed. We formulate an arithmetic relationship between
feature vectors from this observation, and use it to learn a representation of
scenes and objects that can then be used to identify object instances, localize
them in the scene, and perform goal-directed grasping tasks where the robot
must retrieve commanded objects from a bin. The same grasping procedure can
also be used to automatically collect training data for our method, by
recording images of scenes, grasping and removing an object, and recording the
outcome. Our experiments demonstrate that this self-supervised approach for
tasked grasping substantially outperforms direct reinforcement learning from
images and prior representation learning methods.
Exploring Gameplay With AI Agents
Authors: Fernando de Mesentier Silva, Igor Borovikov, John Kolen, Navid Aghdaie, Kazi Zaman
Date: 2018-11-16 00:00:00

The process of playtesting a game is subjective, expensive and incomplete. In
this paper, we present a playtesting approach that explores the game space with
automated agents and collects data to answer questions posed by the designers.
Rather than have agents interacting with an actual game client, this approach
recreates the bare bone mechanics of the game as a separate system. Our agent
is able to play in minutes what would take testers days of organic gameplay.
The analysis of thousands of game simulations exposed imbalances in game
actions, identified inconsequential rewards and evaluated the effectiveness of
optional strategic choices. Our test case game, The Sims Mobile, was recently
released and the findings shown here influenced design changes that resulted in
improved player experience.
Automatic Paper Summary Generation from Visual and Textual Information
Authors: Shintaro Yamamoto, Yoshihiro Fukuhara, Ryota Suzuki, Shigeo Morishima, Hirokatsu Kataoka
Date: 2018-11-16 00:00:00
Tasks: Sentence

Due to the recent boom in artificial intelligence (AI) research, including
computer vision (CV), it has become impossible for researchers in these fields
to keep up with the exponentially increasing number of manuscripts. In response
to this situation, this paper proposes the paper summary generation (PSG) task
using a simple but effective method to automatically generate an academic paper
summary from raw PDF data. We realized PSG by combination of vision-based
supervised components detector and language-based unsupervised important
sentence extractor, which is applicable for a trained format of manuscripts. We
show the quantitative evaluation of ability of simple vision-based components
extraction, and the qualitative evaluation that our system can extract both
visual item and sentence that are helpful for understanding. After processing
via our PSG, the 979 manuscripts accepted by the Conference on Computer Vision
and Pattern Recognition (CVPR) 2018 are available. It is believed that the
proposed method will provide a better way for researchers to stay caught with
important academic papers.
Mode Variational LSTM Robust to Unseen Modes of Variation: Application to Facial Expression Recognition
Authors: Wissam J. Baddar, Yong Man Ro
Date: 2018-11-16 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Spatio-temporal feature encoding is essential for encoding the dynamics in
video sequences. Recurrent neural networks, particularly long short-term memory
(LSTM) units, have been popular as an efficient tool for encoding
spatio-temporal features in sequences. In this work, we investigate the effect
of mode variations on the encoded spatio-temporal features using LSTMs. We show
that the LSTM retains information related to the mode variation in the
sequence, which is irrelevant to the task at hand (e.g. classification facial
expressions). Actually, the LSTM forget mechanism is not robust enough to mode
variations and preserves information that could negatively affect the encoded
spatio-temporal features. We propose the mode variational LSTM to encode
spatio-temporal features robust to unseen modes of variation. The mode
variational LSTM modifies the original LSTM structure by adding an additional
cell state that focuses on encoding the mode variation in the input sequence.
To efficiently regulate what features should be stored in the additional cell
state, additional gating functionality is also introduced. The effectiveness of
the proposed mode variational LSTM is verified using the facial expression
recognition task. Comparative experiments on publicly available datasets
verified that the proposed mode variational LSTM outperforms existing methods.
Moreover, a new dynamic facial expression dataset with different modes of
variation, including various modes like pose and illumination variations, was
collected to comprehensively evaluate the proposed mode variational LSTM.
Experimental results verified that the proposed mode variational LSTM encodes
spatio-temporal features robust to unseen modes of variation.
Image Pre-processing Using OpenCV Library on MORPH-II Face Database
Authors: Benjamin Yip, Rachel Towner, Troy Kling, Cuixian Chen, Yishi Wang
Date: 2018-11-16 00:00:00
Tasks: MORPH

This paper outlines the steps taken toward pre-processing the 55,134 images
of the MORPH-II non-commercial dataset. Following the introduction, section two
begins with an overview of each step in the pre-processing pipeline. Section
three expands upon each stage of the process and includes details on all
calculations made, by providing the OpenCV functionality paired with each step.
The last portion of this paper discusses the potential improvements to this
pre-processing pipeline that became apparent in retrospect.
Exact Recovery in the Hypergraph Stochastic Block Model: a Spectral Algorithm
Authors: Sam Cole, Yizhe Zhu
Date: 2018-11-16 00:00:00
Tasks: Stochastic Block Model

We consider the exact recovery problem in the hypergraph stochastic block model (HSBM) with $k$ blocks of equal size. More precisely, we consider a random $d$-uniform hypergraph $H$ with $n$ vertices partitioned into $k$ clusters of size $s = n / k$. Hyperedges $e$ are added independently with probability $p$ if $e$ is contained within a single cluster and $q$ otherwise, where $0 \leq q < p \leq 1$. We present a spectral algorithm which recovers the clusters exactly with high probability, given mild conditions on $n, k, p, q$, and $d$. Our algorithm is based on the adjacency matrix of $H$, which is a symmetric $n \times n$ matrix whose $(u, v)$-th entry is the number of hyperedges containing both $u$ and $v$. To the best of our knowledge, our algorithm is the first to guarantee exact recovery when the number of clusters $k=\Theta(\sqrt{n})$.
Pre-training Graph Neural Networks with Kernels
Authors: Nicolò Navarin, Dinh V. Tran, Alessandro Sperduti
Date: 2018-11-16 00:00:00

Many machine learning techniques have been proposed in the last few years to
process data represented in graph-structured form. Graphs can be used to model
several scenarios, from molecules and materials to RNA secondary structures.
Several kernel functions have been defined on graphs that coupled with
kernelized learning algorithms, have shown state-of-the-art performances on
many tasks. Recently, several definitions of Neural Networks for Graph (GNNs)
have been proposed, but their accuracy is not yet satisfying. In this paper, we
propose a task-independent pre-training methodology that allows a GNN to learn
the representation induced by state-of-the-art graph kernels. Then, the
supervised learning phase will fine-tune this representation for the task at
hand. The proposed technique is agnostic on the adopted GNN architecture and
kernel function, and shows consistent improvements in the predictive
performance of GNNs in our preliminary experimental results.
On the Complexity of Exploration in Goal-Driven Navigation
Authors: Maruan Al-Shedivat, Lisa Lee, Ruslan Salakhutdinov, Eric Xing
Date: 2018-11-16 00:00:00
Tasks: Navigate

Building agents that can explore their environments intelligently is a
challenging open problem. In this paper, we make a step towards understanding
how a hierarchical design of the agent's policy can affect its exploration
capabilities. First, we design EscapeRoom environments, where the agent must
figure out how to navigate to the exit by accomplishing a number of
intermediate tasks (\emph{subgoals}), such as finding keys or opening doors.
Our environments are procedurally generated and vary in complexity, which can
be controlled by the number of subgoals and relationships between them. Next,
we propose to measure the complexity of each environment by constructing
dependency graphs between the goals and analytically computing \emph{hitting
times} of a random walk in the graph. We empirically evaluate Proximal Policy
Optimization (PPO) with sparse and shaped rewards, a variation of policy
sketches, and a hierarchical version of PPO (called HiPPO) akin to h-DQN. We
show that analytically estimated \emph{hitting time} in goal dependency graphs
is an informative metric of the environment complexity. We conjecture that the
result should hold for environments other than navigation. Finally, we show
that solving environments beyond certain level of complexity requires
hierarchical approaches.
A Generalized Meta-loss function for regression and classification using privileged information
Authors: Amina Asif, Muhammad Dawood, Fayyaz ul Amir Afsar Minhas
Date: 2018-11-16 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, regression

Learning using privileged information (LUPI) is a powerful heterogenous
feature space machine learning framework that allows a machine learning model
to learn from highly informative or privileged features which are available
during training only to generate test predictions using input space features
which are available both during training and testing. LUPI can significantly
improve prediction performance in a variety of machine learning problems.
However, existing large margin and neural network implementations of learning
using privileged information are mostly designed for classification tasks. In
this work, we have proposed a simple yet effective formulation that allows us
to perform regression using privileged information through a custom loss
function. Apart from regression, our formulation allows general application of
LUPI to classification and other related problems as well. We have verified the
correctness, applicability and effectiveness of our method on regression and
classification problems over different synthetic and real-world problems. To
test the usefulness of the proposed model in real-world problems, we have
evaluated our method on the problem of protein binding affinity prediction. The
proposed LUPI regression-based model has shown to outperform the current
state-of-the-art predictor.
The Perfect Match: 3D Point Cloud Matching with Smoothed Densities
Authors: Zan Gojcic, Caifa Zhou, Jan D. Wegner, Andreas Wieser
Date: 2018-11-16 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Point Cloud Matching, Descriptive, Point Cloud Registration

We propose 3DSmoothNet, a full workflow to match 3D point clouds with a siamese deep learning architecture and fully convolutional layers using a voxelized smoothed density value (SDV) representation. The latter is computed per interest point and aligned to the local reference frame (LRF) to achieve rotation invariance. Our compact, learned, rotation invariant 3D point cloud descriptor achieves 94.9% average recall on the 3DMatch benchmark data set, outperforming the state-of-the-art by more than 20 percent points with only 32 output dimensions. This very low output dimension allows for near realtime correspondence search with 0.1 ms per feature point on a standard PC. Our approach is sensor- and sceneagnostic because of SDV, LRF and learning highly descriptive features with fully convolutional layers. We show that 3DSmoothNet trained only on RGB-D indoor scenes of buildings achieves 79.0% average recall on laser scans of outdoor vegetation, more than double the performance of our closest, learning-based competitors. Code, data and pre-trained models are available online at https://github.com/zgojcic/3DSmoothNet.
Residual Convolutional Neural Network Revisited with Active Weighted Mapping
Authors: Jung HyoungHo, Lee Ryong, Lee Sanghwan, Hwang Wonjun
Date: 2018-11-16 00:00:00

In visual recognition, the key to the performance improvement of ResNet is
the success in establishing the stack of deep sequential convolutional layers
using identical mapping by a shortcut connection. It results in multiple paths
of data flow under a network and the paths are merged with the equal weights.
However, it is questionable whether it is correct to use the fixed and
predefined weights at the mapping units of all paths. In this paper, we
introduce the active weighted mapping method which infers proper weight values
based on the characteristic of input data on the fly. The weight values of each
mapping unit are not fixed but changed as the input image is changed, and the
most proper weight values for each mapping unit are derived according to the
input image. For this purpose, channel-wise information is embedded from both
the shortcut connection and convolutional block, and then the fully connected
layers are used to estimate the weight values for the mapping units. We train
the backbone network and the proposed module alternately for a more stable
learning of the proposed method. Results of the extensive experiments show that
the proposed method works successfully on the various backbone architectures
from ResNet to DenseNet. We also verify the superiority and generality of the
proposed method on various datasets in comparison with the baseline.
Cost-Aware Fine-Grained Recognition for IoTs Based on Sequential Fixations
Authors: Hanxiao Wang, Venkatesh Saligrama, Stan Sclaroff, Vitaly Ablavsky
Date: 2018-11-16 00:00:00
Proceeding: ICCV 2019 10
Tasks: Classification, Decision Making, Deep Reinforcement Learning, Foveation, General Classification, Reinforcement Learning

We consider the problem of fine-grained classification on an edge camera device that has limited power. The edge device must sparingly interact with the cloud to minimize communication bits to conserve power, and the cloud upon receiving the edge inputs returns a classification label. To deal with fine-grained classification, we adopt the perspective of sequential fixation with a foveated field-of-view to model cloud-edge interactions. We propose a novel deep reinforcement learning-based foveation model, DRIFT, that sequentially generates and recognizes mixed-acuity images.Training of DRIFT requires only image-level category labels and encourages fixations to contain task-relevant information, while maintaining data efficiency. Specifically, wetrain a foveation actor network with a novel Deep Deterministic Policy Gradient by Conditioned Critic and Coaching (DDPGC3) algorithm. In addition, we propose to shape the reward to provide informative feedback after each fixation to better guide RL training. We demonstrate the effectiveness of DRIFT on this task by evaluating on five fine-grained classification benchmark datasets, and show that the proposed approach achieves state-of-the-art performance with over 3X reduction in transmitted pixels.