pwc-archive/papers-with-abstracts huggingface dataset:
Error Analysis and Improving the Accuracy of Winograd Convolution for Deep Neural Networks
Authors: Barbara Barabasz, Andrew Anderson, Kirk M. Soodhalter, David Gregg
Date: 2018-03-29 00:00:00

Popular deep neural networks (DNNs) spend the majority of their execution
time computing convolutions. The Winograd family of algorithms can greatly
reduce the number of arithmetic operations required and is present in many DNN
software frameworks. However, the performance gain is at the expense of a
reduction in floating point (FP) numerical accuracy. In this paper, we analyse
the worst case FP error and prove the estimation of norm and conditioning of
the algorithm. We show that the bound grows exponentially with the size of the
convolution, but the error bound of the \textit{modified} algorithm is smaller
than the original one. We propose several methods for reducing FP error. We
propose a canonical evaluation ordering based on Huffman coding that reduces
summation error. We study the selection of sampling "points" experimentally and
find empirically good points for the most important sizes. We identify the main
factors associated with good points. In addition, we explore other methods to
reduce FP error, including mixed-precision convolution, and pairwise summation
across DNN channels. Using our methods we can significantly reduce FP error for
a given block size, which allows larger block sizes and reduced computation.
A Review of Literature on Parallel Constraint Solving
Authors: Ian P. Gent, Ciaran McCreesh, Ian Miguel, Neil C. A. Moore, Peter Nightingale, Patrick Prosser, Chris Unsworth
Date: 2018-03-29 00:00:00

As multicore computing is now standard, it seems irresponsible for
constraints researchers to ignore the implications of it. Researchers need to
address a number of issues to exploit parallelism, such as: investigating which
constraint algorithms are amenable to parallelisation; whether to use shared
memory or distributed computation; whether to use static or dynamic
decomposition; and how to best exploit portfolios and cooperating search. We
review the literature, and see that we can sometimes do quite well, some of the
time, on some instances, but we are far from a general solution. Yet there
seems to be little overall guidance that can be given on how best to exploit
multicore computers to speed up constraint solving. We hope at least that this
survey will provide useful pointers to future researchers wishing to correct
this situation.
  Under consideration in Theory and Practice of Logic Programming (TPLP).
Context-aware Synthesis for Video Frame Interpolation
Authors: Simon Niklaus, Feng Liu
Date: 2018-03-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: Optical Flow Estimation, Video Frame Interpolation

Video frame interpolation algorithms typically estimate optical flow or its
variations and then use it to guide the synthesis of an intermediate frame
between two consecutive original frames. To handle challenges like occlusion,
bidirectional flow between the two input frames is often estimated and used to
warp and blend the input frames. However, how to effectively blend the two
warped frames still remains a challenging problem. This paper presents a
context-aware synthesis approach that warps not only the input frames but also
their pixel-wise contextual information and uses them to interpolate a
high-quality intermediate frame. Specifically, we first use a pre-trained
neural network to extract per-pixel contextual information for input frames. We
then employ a state-of-the-art optical flow algorithm to estimate bidirectional
flow between them and pre-warp both input frames and their context maps.
Finally, unlike common approaches that blend the pre-warped frames, our method
feeds them and their context maps to a video frame synthesis neural network to
produce the interpolated frame in a context-aware fashion. Our neural network
is fully convolutional and is trained end to end. Our experiments show that our
method can handle challenging scenarios such as occlusion and large motion and
outperforms representative state-of-the-art approaches.
Graphite: Iterative Generative Modeling of Graphs
Authors: Aditya Grover, Aaron Zweig, Stefano Ermon
Date: 2018-03-28 00:00:00
Tasks: Density Estimation, General Classification, Link Prediction, Node Classification, Variational Inference

Graphs are a fundamental abstraction for modeling relational data. However, graphs are discrete and combinatorial in nature, and learning representations suitable for machine learning tasks poses statistical and computational challenges. In this work, we propose Graphite, an algorithmic framework for unsupervised learning of representations over nodes in large graphs using deep latent variable generative models. Our model parameterizes variational autoencoders (VAE) with graph neural networks, and uses a novel iterative graph refinement strategy inspired by low-rank approximations for decoding. On a wide variety of synthetic and benchmark datasets, Graphite outperforms competing approaches for the tasks of density estimation, link prediction, and node classification. Finally, we derive a theoretical connection between message passing in graph neural networks and mean-field variational inference.
Weakly Aggregative Modal Logic: Characterization and Interpolation (new version)
Authors: Jixin Liu, Yanjing Wang, Yifeng Ding
Date: 2018-03-29 00:00:00

Weakly Aggregative Modal Logic (WAML) is a collection of disguised polyadic modal logics with n-ary modalities whose arguments are all the same. WAML has some interesting applications on epistemic logic and logic of games, so we study some basic model theoretical aspects of WAML in this paper. Specifically, we give a van Benthem-Rosen characterization theorem of WAML based on an intuitive notion of bisimulation and show that each basic WAML system K_n lacks Craig Interpolation.
V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map
Authors: Gyeongsik Moon, Ju Yong Chang, Kyoung Mu Lee
Date: 2017-11-20 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Hand Pose Estimation, 3D Human Pose Estimation, Hand Pose Estimation, Pose Estimation

Most of the existing deep learning-based methods for 3D hand and human pose
estimation from a single depth map are based on a common framework that takes a
2D depth map and directly regresses the 3D coordinates of keypoints, such as
hand or human body joints, via 2D convolutional neural networks (CNNs). The
first weakness of this approach is the presence of perspective distortion in
the 2D depth map. While the depth map is intrinsically 3D data, many previous
methods treat depth maps as 2D images that can distort the shape of the actual
object through projection from 3D to 2D space. This compels the network to
perform perspective distortion-invariant estimation. The second weakness of the
conventional approach is that directly regressing 3D coordinates from a 2D
image is a highly non-linear mapping, which causes difficulty in the learning
procedure. To overcome these weaknesses, we firstly cast the 3D hand and human
pose estimation problem from a single depth map into a voxel-to-voxel
prediction that uses a 3D voxelized grid and estimates the per-voxel likelihood
for each keypoint. We design our model as a 3D CNN that provides accurate
estimates while running in real-time. Our system outperforms previous methods
in almost all publicly available 3D hand and human pose estimation datasets and
placed first in the HANDS 2017 frame-based 3D hand pose estimation challenge.
The code is available in https://github.com/mks0601/V2V-PoseNet_RELEASE.
Video Captioning via Hierarchical Reinforcement Learning
Authors: Xin Wang, Wenhu Chen, Jiawei Wu, Yuan-Fang Wang, William Yang Wang
Date: 2017-11-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Video Captioning

Video captioning is the task of automatically generating a textual
description of the actions in a video. Although previous work (e.g.
sequence-to-sequence model) has shown promising results in abstracting a coarse
description of a short video, it is still very challenging to caption a video
containing multiple fine-grained actions with a detailed description. This
paper aims to address the challenge by proposing a novel hierarchical
reinforcement learning framework for video captioning, where a high-level
Manager module learns to design sub-goals and a low-level Worker module
recognizes the primitive actions to fulfill the sub-goal. With this
compositional framework to reinforce video captioning at different levels, our
approach significantly outperforms all the baseline methods on a newly
introduced large-scale dataset for fine-grained video captioning. Furthermore,
our non-ensemble model has already achieved the state-of-the-art results on the
widely-used MSR-VTT dataset.
Nonlinear Dimensionality Reduction on Graphs
Authors: Yanning Shen, Panagiotis A. Traganitis, Georgios B. Giannakis
Date: 2018-01-29 00:00:00
Tasks: Dimensionality Reduction, Time Series, Time Series Analysis

In this era of data deluge, many signal processing and machine learning tasks
are faced with high-dimensional datasets, including images, videos, as well as
time series generated from social, commercial and brain network interactions.
Their efficient processing calls for dimensionality reduction techniques
capable of properly compressing the data while preserving task-related
characteristics, going beyond pairwise data correlations. The present paper
puts forth a nonlinear dimensionality reduction framework that accounts for
data lying on known graphs. The novel framework encompasses most of the
existing dimensionality reduction methods, but it is also capable of capturing
and preserving possibly nonlinear correlations that are ignored by linear
methods. Furthermore, it can take into account information from multiple
graphs. The proposed algorithms were tested on synthetic as well as real
datasets to corroborate their effectiveness.
Best arm identification in multi-armed bandits with delayed feedback
Authors: Aditya Grover, Todor Markov, Peter Attia, Norman Jin, Nicholas Perkins, Bryan Cheong, Michael Chen, Zi Yang, Stephen Harris, William Chueh, Stefano Ermon
Date: 2018-03-29 00:00:00
Tasks: Hyperparameter Optimization, Multi-Armed Bandits

We propose a generalization of the best arm identification problem in
stochastic multi-armed bandits (MAB) to the setting where every pull of an arm
is associated with delayed feedback. The delay in feedback increases the
effective sample complexity of standard algorithms, but can be offset if we
have access to partial feedback received before a pull is completed. We propose
a general framework to model the relationship between partial and delayed
feedback, and as a special case we introduce efficient algorithms for settings
where the partial feedback are biased or unbiased estimators of the delayed
feedback. Additionally, we propose a novel extension of the algorithms to the
parallel MAB setting where an agent can control a batch of arms. Our
experiments in real-world settings, involving policy search and hyperparameter
optimization in computational sustainability domains for fast charging of
batteries and wildlife corridor construction, demonstrate that exploiting the
structure of partial feedback can lead to significant improvements over
baselines in both sequential and parallel MAB.
Learning Free-Form Deformations for 3D Object Reconstruction
Authors: Dominic Jack, Jhony K. Pontes, Sridha Sridharan, Clinton Fookes, Sareh Shirazi, Frederic Maire, Anders Eriksson
Date: 2018-03-29 00:00:00
Tasks: 3D Object Reconstruction, 3D Reconstruction, 3D Semantic Segmentation, Form, Object, Object Reconstruction, Semantic Segmentation

Representing 3D shape in deep learning frameworks in an accurate, efficient
and compact manner still remains an open challenge. Most existing work
addresses this issue by employing voxel-based representations. While these
approaches benefit greatly from advances in computer vision by generalizing 2D
convolutions to the 3D setting, they also have several considerable drawbacks.
The computational complexity of voxel-encodings grows cubically with the
resolution thus limiting such representations to low-resolution 3D
reconstruction. In an attempt to solve this problem, point cloud
representations have been proposed. Although point clouds are more efficient
than voxel representations as they only cover surfaces rather than volumes,
they do not encode detailed geometric information about relationships between
points. In this paper we propose a method to learn free-form deformations (FFD)
for the task of 3D reconstruction from a single image. By learning to deform
points sampled from a high-quality mesh, our trained model can be used to
produce arbitrarily dense point clouds or meshes with fine-grained geometry. We
evaluate our proposed framework on both synthetic and real-world data and
achieve state-of-the-art results on point-cloud and volumetric metrics.
Additionally, we qualitatively demonstrate its applicability to label
transferring for 3D semantic segmentation.
B-DCGAN:Evaluation of Binarized DCGAN for FPGA
Authors: Hideo Terada, Hayaru Shouno
Date: 2018-03-29 00:00:00
Tasks: Binarization, Edge-computing

We are trying to implement deep neural networks in the edge computing environment for real-world applications such as the IoT(Internet of Things), the FinTech etc., for the purpose of utilizing the significant achievement of Deep Learning in recent years. Especially, we now focus algorithm implementation on FPGA, because FPGA is one of the promising devices for low-cost and low-power implementation of the edge computer. In this work, we introduce Binary-DCGAN(B-DCGAN) - Deep Convolutional GAN model with binary weights and activations, and with using integer-valued operations in forward pass(train-time and run-time). And we show how to implement B-DCGAN on FPGA(Xilinx Zynq). Using the B-DCGAN, we do feasibility study of FPGA's characteristic and performance for Deep Learning. Because the binarization and using integer-valued operation reduce the memory capacity and the number of the circuit gates, it is very effective for FPGA implementation. On the other hand, the quality of generated data from the model will be decreased by these reductions. So we investigate the influence of these reductions.
Referring Relationships
Authors: Ranjay Krishna, Ines Chami, Michael Bernstein, Li Fei-Fei
Date: 2018-03-28 00:00:00
Proceeding: CVPR 2018 6

Images are not simply sets of objects: each image represents a web of
interconnected relationships. These relationships between entities carry
semantic meaning and help a viewer differentiate between instances of an
entity. For example, in an image of a soccer match, there may be multiple
persons present, but each participates in different relationships: one is
kicking the ball, and the other is guarding the goal. In this paper, we
formulate the task of utilizing these "referring relationships" to disambiguate
between entities of the same category. We introduce an iterative model that
localizes the two entities in the referring relationship, conditioned on one
another. We formulate the cyclic condition between the entities in a
relationship by modelling predicates that connect the entities as shifts in
attention from one entity to another. We demonstrate that our model can not
only outperform existing approaches on three datasets --- CLEVR, VRD and Visual
Genome --- but also that it produces visually meaningful predicate shifts, as
an instance of interpretable neural networks. Finally, we show that by
modelling predicates as attention shifts, we can even localize entities in the
absence of their category, allowing our model to find completely unseen
categories.
Radical analysis network for zero-shot learning in printed Chinese character recognition
Authors: Jianshu Zhang, Yixing Zhu, Jun Du, Li-Rong Dai
Date: 2017-11-03 00:00:00
Tasks: Decoder, Zero-Shot Learning

Chinese characters have a huge set of character categories, more than 20,000
and the number is still increasing as more and more novel characters continue
being created. However, the enormous characters can be decomposed into a
compact set of about 500 fundamental and structural radicals. This paper
introduces a novel radical analysis network (RAN) to recognize printed Chinese
characters by identifying radicals and analyzing two-dimensional spatial
structures among them. The proposed RAN first extracts visual features from
input by employing convolutional neural networks as an encoder. Then a decoder
based on recurrent neural networks is employed, aiming at generating captions
of Chinese characters by detecting radicals and two-dimensional structures
through a spatial attention mechanism. The manner of treating a Chinese
character as a composition of radicals rather than a single character class
largely reduces the size of vocabulary and enables RAN to possess the ability
of recognizing unseen Chinese character classes, namely zero-shot learning.
An LP-based hyperparameter optimization model for language modeling
Authors: Amir Hossein Akhavan Rahnama, Mehdi Toloo, Nezer Jacob Zaidenberg
Date: 2018-03-29 00:00:00
Tasks: Hyperparameter Optimization, Language Modeling, Language Modelling

In order to find hyperparameters for a machine learning model, algorithms
such as grid search or random search are used over the space of possible values
of the models hyperparameters. These search algorithms opt the solution that
minimizes a specific cost function. In language models, perplexity is one of
the most popular cost functions. In this study, we propose a fractional
nonlinear programming model that finds the optimal perplexity value. The
special structure of the model allows us to approximate it by a linear
programming model that can be solved using the well-known simplex algorithm. To
the best of our knowledge, this is the first attempt to use optimization
techniques to find perplexity values in the language modeling literature. We
apply our model to find hyperparameters of a language model and compare it to
the grid search algorithm. Furthermore, we illustrating that it results in
lower perplexity values. We perform this experiment on a real-world dataset
from SwiftKey to validate our proposed approach.
Learning Latent Super-Events to Detect Multiple Activities in Videos
Authors: AJ Piergiovanni, Michael S. Ryoo
Date: 2017-12-05 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Detection, Activity Detection

In this paper, we introduce the concept of learning latent super-events from
activity videos, and present how it benefits activity detection in continuous
videos. We define a super-event as a set of multiple events occurring together
in videos with a particular temporal organization; it is the opposite concept
of sub-events. Real-world videos contain multiple activities and are rarely
segmented (e.g., surveillance videos), and learning latent super-events allows
the model to capture how the events are temporally related in videos. We design
temporal structure filters that enable the model to focus on particular
sub-intervals of the videos, and use them together with a soft attention
mechanism to learn representations of latent super-events. Super-event
representations are combined with per-frame or per-segment CNNs to provide
frame-level annotations. Our approach is designed to be fully differentiable,
enabling end-to-end learning of latent super-event representations jointly with
the activity detector using them. Our experiments with multiple public video
datasets confirm that the proposed concept of latent super-event learning
significantly benefits activity detection, advancing the state-of-the-arts.
Pointwise Convolutional Neural Networks
Authors: Binh-Son Hua, Minh-Khoi Tran, Sai-Kit Yeung
Date: 2017-12-14 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object, Object Recognition, Segmentation, Semantic Segmentation

Deep learning with 3D data such as reconstructed point clouds and CAD models
has received great research interests recently. However, the capability of
using point clouds with convolutional neural network has been so far not fully
explored. In this paper, we present a convolutional neural network for semantic
segmentation and object recognition with 3D point clouds. At the core of our
network is pointwise convolution, a new convolution operator that can be
applied at each point of a point cloud. Our fully convolutional network design,
while being surprisingly simple to implement, can yield competitive accuracy in
both semantic segmentation and object recognition task.
Detecting Adversarial Examples via Neural Fingerprinting
Authors: Sumanth Dathathri, Stephan Zheng, Tianwei Yin, Richard M. Murray, Yisong Yue
Date: 2018-03-11 00:00:00

Deep neural networks are vulnerable to adversarial examples, which dramatically alter model output using small input changes. We propose Neural Fingerprinting, a simple, yet effective method to detect adversarial examples by verifying whether model behavior is consistent with a set of secret fingerprints, inspired by the use of biometric and cryptographic signatures. The benefits of our method are that 1) it is fast, 2) it is prohibitively expensive for an attacker to reverse-engineer which fingerprints were used, and 3) it does not assume knowledge of the adversary. In this work, we pose a formal framework to analyze fingerprints under various threat models, and characterize Neural Fingerprinting for linear models. For complex neural networks, we empirically demonstrate that Neural Fingerprinting significantly improves on state-of-the-art detection mechanisms by detecting the strongest known adversarial attacks with 98-100% AUC-ROC scores on the MNIST, CIFAR-10 and MiniImagenet (20 classes) datasets. In particular, the detection accuracy of Neural Fingerprinting generalizes well to unseen test-data under various black- and whitebox threat models, and is robust over a wide range of hyperparameters and choices of fingerprints.
Region Detection in Markov Random Fields: Gaussian Case
Authors: Ilya Soloveychik, Vahid Tarokh
Date: 2018-02-12 00:00:00
Tasks: Model Selection

We consider the problem of model selection in Gaussian Markov fields in the
sample deficient scenario. The benchmark information-theoretic results in the
case of d-regular graphs require the number of samples to be at least
proportional to the logarithm of the number of vertices to allow consistent
graph recovery. When the number of samples is less than this amount, reliable
detection of all edges is impossible. In many applications, it is more
important to learn the distribution of the edge (coupling) parameters over the
network than the specific locations of the edges. Assuming that the entire
graph can be partitioned into a number of spatial regions with similar edge
parameters and reasonably regular boundaries, we develop new
information-theoretic sample complexity bounds and show that a bounded number
of samples can be sufficient to consistently recover these regions. Finally, we
introduce and analyze an efficient region growing algorithm capable of
recovering the regions with high accuracy. We show that it is consistent and
demonstrate its performance benefits in synthetic simulations.