pwc-archive/papers-with-abstracts huggingface dataset:
A Unified Framework for Multi-View Multi-Class Object Pose Estimation
Authors: Chi Li, Jin Bai, Gregory D. Hager
Date: 2018-03-21 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object, Pose Estimation

One core challenge in object pose estimation is to ensure accurate and robust
performance for large numbers of diverse foreground objects amidst complex
background clutter. In this work, we present a scalable framework for
accurately inferring six Degree-of-Freedom (6-DoF) pose for a large number of
object classes from single or multiple views. To learn discriminative pose
features, we integrate three new capabilities into a deep Convolutional Neural
Network (CNN): an inference scheme that combines both classification and pose
regression based on a uniform tessellation of the Special Euclidean group in
three dimensions (SE(3)), the fusion of class priors into the training process
via a tiled class map, and an additional regularization using deep supervision
with an object mask. Further, an efficient multi-view framework is formulated
to address single-view ambiguity. We show that this framework consistently
improves the performance of the single-view network. We evaluate our method on
three large-scale benchmarks: YCB-Video, JHUScene-50 and ObjectNet-3D. Our
approach achieves competitive or superior performance over the current
state-of-the-art methods.
Clustering to Reduce Spatial Data Set Size
Authors: Geoff Boeing
Date: 2018-03-21 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

Traditionally it had been a problem that researchers did not have access to
enough spatial data to answer pressing research questions or build compelling
visualizations. Today, however, the problem is often that we have too much
data. Spatially redundant or approximately redundant points may refer to a
single feature (plus noise) rather than many distinct spatial features. We use
a machine learning approach with density-based clustering to compress such
spatial data into a set of representative features.
NISP: Pruning Networks using Neuron Importance Score Propagation
Authors: Ruichi Yu, Ang Li, Chun-Fu Chen, Jui-Hsin Lai, Vlad I. Morariu, Xintong Han, Mingfei Gao, Ching-Yung Lin, Larry S. Davis
Date: 2017-11-16 00:00:00
Proceeding: CVPR 2018 6
Tasks: Network Pruning

To reduce the significant redundancy in deep Convolutional Neural Networks
(CNNs), most existing methods prune neurons by only considering statistics of
an individual layer or two consecutive layers (e.g., prune one layer to
minimize the reconstruction error of the next layer), ignoring the effect of
error propagation in deep networks. In contrast, we argue that it is essential
to prune neurons in the entire neuron network jointly based on a unified goal:
minimizing the reconstruction error of important responses in the "final
response layer" (FRL), which is the second-to-last layer before classification,
for a pruned network to retrain its predictive power. Specifically, we apply
feature ranking techniques to measure the importance of each neuron in the FRL,
and formulate network pruning as a binary integer optimization problem and
derive a closed-form solution to it for pruning neurons in earlier layers.
Based on our theoretical analysis, we propose the Neuron Importance Score
Propagation (NISP) algorithm to propagate the importance scores of final
responses to every neuron in the network. The CNN is pruned by removing neurons
with least importance, and then fine-tuned to retain its predictive power. NISP
is evaluated on several datasets with multiple CNN models and demonstrated to
achieve significant acceleration and compression with negligible accuracy loss.
Incremental Learning-to-Learn with Statistical Guarantees
Authors: Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil
Date: 2018-03-21 00:00:00
Tasks: Incremental Learning, regression

In learning-to-learn the goal is to infer a learning algorithm that works
well on a class of tasks sampled from an unknown meta distribution. In contrast
to previous work on batch learning-to-learn, we consider a scenario where tasks
are presented sequentially and the algorithm needs to adapt incrementally to
improve its performance on future tasks. Key to this setting is for the
algorithm to rapidly incorporate new observations into the model as they
arrive, without keeping them in memory. We focus on the case where the
underlying algorithm is ridge regression parameterized by a positive
semidefinite matrix. We propose to learn this matrix by applying a stochastic
strategy to minimize the empirical error incurred by ridge regression on future
tasks sampled from the meta distribution. We study the statistical properties
of the proposed algorithm and prove non-asymptotic bounds on its excess
transfer risk, that is, the generalization performance on new tasks from the
same meta distribution. We compare our online learning-to-learn approach with a
state of the art batch method, both theoretically and empirically.
Probabilistic Video Generation using Holistic Attribute Control
Authors: Jiawei He, Andreas Lehrmann, Joseph Marino, Greg Mori, Leonid Sigal
Date: 2018-03-21 00:00:00
Proceeding: ECCV 2018 9
Tasks: Attribute, Future prediction, Video Generation

Videos express highly structured spatio-temporal patterns of visual data. A
video can be thought of as being governed by two factors: (i) temporally
invariant (e.g., person identity), or slowly varying (e.g., activity),
attribute-induced appearance, encoding the persistent content of each frame,
and (ii) an inter-frame motion or scene dynamics (e.g., encoding evolution of
the person ex-ecuting the action). Based on this intuition, we propose a
generative framework for video generation and future prediction. The proposed
framework generates a video (short clip) by decoding samples sequentially drawn
from a latent space distribution into full video frames. Variational
Autoencoders (VAEs) are used as a means of encoding/decoding frames into/from
the latent space and RNN as a wayto model the dynamics in the latent space. We
improve the video generation consistency through temporally-conditional
sampling and quality by structuring the latent space with attribute controls;
ensuring that attributes can be both inferred and conditioned on during
learning/generation. As a result, given attributes and/orthe first frame, our
model is able to generate diverse but highly consistent sets ofvideo sequences,
accounting for the inherent uncertainty in the prediction task. Experimental
results on Chair CAD, Weizmann Human Action, and MIT-Flickr datasets, along
with detailed comparison to the state-of-the-art, verify effectiveness of the
framework.
Fastest Convergence for Q-learning
Authors: Adithya M. Devraj, Sean P. Meyn
Date: 2017-07-12 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The Zap Q-learning algorithm introduced in this paper is an improvement of
Watkins' original algorithm and recent competitors in several respects. It is a
matrix-gain algorithm designed so that its asymptotic variance is optimal.
Moreover, an ODE analysis suggests that the transient behavior is a close match
to a deterministic Newton-Raphson implementation. This is made possible by a
two time-scale update equation for the matrix gain sequence.
  The analysis suggests that the approach will lead to stable and efficient
computation even for non-ideal parameterized settings. Numerical experiments
confirm the quick convergence, even in such non-ideal cases.
  A secondary goal of this paper is tutorial. The first half of the paper
contains a survey on reinforcement learning algorithms, with a focus on minimum
variance algorithms.
A Supervised STDP-based Training Algorithm for Living Neural Networks
Authors: Yuan Zeng, Kevin Devincentis, Yao Xiao, Zubayer Ibne Ferdous, Xiaochen Guo, Zhiyuan Yan, Yevgeny Berdichevsky
Date: 2017-10-30 00:00:00
Tasks: BIG-bench Machine Learning, Drug Discovery, General Classification, Handwritten Digit Recognition, image-classification, Image Classification, object-detection, Object Detection, speech-recognition, Speech Recognition

Neural networks have shown great potential in many applications like speech
recognition, drug discovery, image classification, and object detection. Neural
network models are inspired by biological neural networks, but they are
optimized to perform machine learning tasks on digital computers. The proposed
work explores the possibilities of using living neural networks in vitro as
basic computational elements for machine learning applications. A new
supervised STDP-based learning algorithm is proposed in this work, which
considers neuron engineering constrains. A 74.7% accuracy is achieved on the
MNIST benchmark for handwritten digit recognition.
Olive Oil is Made of Olives, Baby Oil is Made for Babies: Interpreting Noun Compounds using Paraphrases in a Neural Model
Authors: Vered Shwartz, Chris Waterson
Date: 2018-03-21 00:00:00
Proceeding: HLT 2018 6
Tasks: Memorization, Relation

Automatic interpretation of the relation between the constituents of a noun
compound, e.g. olive oil (source) and baby oil (purpose) is an important task
for many NLP applications. Recent approaches are typically based on either
noun-compound representations or paraphrases. While the former has initially
shown promising results, recent work suggests that the success stems from
memorizing single prototypical words for each relation. We explore a neural
paraphrasing approach that demonstrates superior performance when such
memorization is not possible.
Jet Charge and Machine Learning
Authors: Katherine Fraser, Matthew D. Schwartz
Date: 2018-03-21 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

Modern machine learning techniques, such as convolutional, recurrent and
recursive neural networks, have shown promise for jet substructure at the Large
Hadron Collider. For example, they have demonstrated effectiveness at boosted
top or W boson identification or for quark/gluon discrimination. We explore
these methods for the purpose of classifying jets according to their electric
charge. We find that both neural networks that incorporate distance within the
jet as an input and boosted decision trees including radial distance
information can provide significant improvement in jet charge extraction over
current methods. Specifically, convolutional, recurrent, and recursive networks
can provide the largest improvement over traditional methods, in part by
effectively utilizing distance within the jet or clustering history. The
advantages of using a fixed-size input representation (as with the CNN) or a
small input representation (as with the RNN) suggest that both convolutional
and recurrent networks will be essential to the future of modern machine
learning at colliders.
The Implicit Bias of Gradient Descent on Separable Data
Authors: Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, Nathan Srebro
Date: 2017-10-27 00:00:00
Proceeding: ICLR 2018 1

We examine gradient descent on unregularized logistic regression problems, with homogeneous linear predictors on linearly separable datasets. We show the predictor converges to the direction of the max-margin (hard margin SVM) solution. The result also generalizes to other monotone decreasing loss functions with an infimum at infinity, to multi-class problems, and to training a weight layer in a deep network in a certain restricted setting. Furthermore, we show this convergence is very slow, and only logarithmic in the convergence of the loss itself. This can help explain the benefit of continuing to optimize the logistic or cross-entropy loss even after the training error is zero and the training loss is extremely small, and, as we show, even if the validation loss increases. Our methodology can also aid in understanding implicit regularization n more complex models and with other optimization methods.
Demystifying MMD GANs
Authors: Mikołaj Bińkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton
Date: 2018-01-04 00:00:00
Proceeding: ICLR 2018 1

We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical contribution, we clarify the situation with bias in GAN loss functions raised by recent work: we show that gradient estimators used in the optimization process for both MMD GANs and Wasserstein GANs are unbiased, but learning a discriminator based on samples leads to biased gradients for the generator parameters. We also discuss the issue of kernel choice for the MMD critic, and characterize the kernel corresponding to the energy distance used for the Cramer GAN critic. Being an integral probability metric, the MMD benefits from training strategies recently developed for Wasserstein GANs. In experiments, the MMD GAN is able to employ a smaller critic network than the Wasserstein GAN, resulting in a simpler and faster-training algorithm with matching performance. We also propose an improved measure of GAN convergence, the Kernel Inception Distance, and show how to use it to dynamically adapt learning rates during GAN training.
Stacked Cross Attention for Image-Text Matching
Authors: Kuang-Huei Lee, Xi Chen, Gang Hua, Houdong Hu, Xiaodong He
Date: 2018-03-21 00:00:00
Proceeding: ECCV 2018 9
Tasks: Cross-Modal Retrieval, Image Retrieval, Image-text matching, Retrieval, Sentence, Sentence Retrieval, Text Matching, Text Retrieval, text similarity

In this paper, we study the problem of image-text matching. Inferring the
latent semantic alignment between objects or other salient stuff (e.g. snow,
sky, lawn) and the corresponding words in sentences allows to capture
fine-grained interplay between vision and language, and makes image-text
matching more interpretable. Prior work either simply aggregates the similarity
of all possible pairs of regions and words without attending differentially to
more and less important words or regions, or uses a multi-step attentional
process to capture limited number of semantic alignments which is less
interpretable. In this paper, we present Stacked Cross Attention to discover
the full latent alignments using both image regions and words in a sentence as
context and infer image-text similarity. Our approach achieves the
state-of-the-art results on the MS-COCO and Flickr30K datasets. On Flickr30K,
our approach outperforms the current best methods by 22.1% relatively in text
retrieval from image query, and 18.2% relatively in image retrieval with text
query (based on Recall@1). On MS-COCO, our approach improves sentence retrieval
by 17.8% relatively and image retrieval by 16.6% relatively (based on Recall@1
using the 5K test set). Code has been made available at:
https://github.com/kuanghuei/SCAN.
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
Authors: Miles E. Lopes, Shusen Wang, Michael W. Mahoney
Date: 2018-03-21 00:00:00
Proceeding: ICML 2018 7

Over the course of the past decade, a variety of randomized algorithms have
been proposed for computing approximate least-squares (LS) solutions in
large-scale settings. A longstanding practical issue is that, for any given
input, the user rarely knows the actual error of an approximate solution
(relative to the exact solution). Likewise, it is difficult for the user to
know precisely how much computation is needed to achieve the desired error
tolerance. Consequently, the user often appeals to worst-case error bounds that
tend to offer only qualitative guidance. As a more practical alternative, we
propose a bootstrap method to compute a posteriori error estimates for
randomized LS algorithms. These estimates permit the user to numerically assess
the error of a given solution, and to predict how much work is needed to
improve a "preliminary" solution. In addition, we provide theoretical
consistency results for the method, which are the first such results in this
context (to the best of our knowledge). From a practical standpoint, the method
also has considerable flexibility, insofar as it can be applied to several
popular sketching algorithms, as well as a variety of error metrics. Moreover,
the extra step of error estimation does not add much cost to an underlying
sketching algorithm. Finally, we demonstrate the effectiveness of the method
with empirical results.
Monocular Depth Estimation by Learning from Heterogeneous Datasets
Authors: Akhil Gurram, Onay Urfalioglu, Ibrahim Halfaoui, Fahd Bouzaraa, Antonio M. Lopez
Date: 2018-03-21 00:00:00
Tasks: Autonomous Driving, Depth Estimation, Monocular Depth Estimation, Semantic Segmentation

Depth estimation provides essential information to perform autonomous driving
and driver assistance. Especially, Monocular Depth Estimation is interesting
from a practical point of view, since using a single camera is cheaper than
many other options and avoids the need for continuous calibration strategies as
required by stereo-vision approaches. State-of-the-art methods for Monocular
Depth Estimation are based on Convolutional Neural Networks (CNNs). A promising
line of work consists of introducing additional semantic information about the
traffic scene when training CNNs for depth estimation. In practice, this means
that the depth data used for CNN training is complemented with images having
pixel-wise semantic labels, which usually are difficult to annotate (e.g.
crowded urban images). Moreover, so far it is common practice to assume that
the same raw training data is associated with both types of ground truth, i.e.,
depth and semantic labels. The main contribution of this paper is to show that
this hard constraint can be circumvented, i.e., that we can train CNNs for
depth estimation by leveraging the depth and semantic information coming from
heterogeneous datasets. In order to illustrate the benefits of our approach, we
combine KITTI depth and Cityscapes semantic segmentation datasets,
outperforming state-of-the-art results on Monocular Depth Estimation.
Continuum directions for supervised dimension reduction
Authors: Sungkyu Jung
Date: 2016-06-20 00:00:00
Tasks: Dimensionality Reduction, General Classification

Dimension reduction of multivariate data supervised by auxiliary information
is considered. A series of basis for dimension reduction is obtained as
minimizers of a novel criterion. The proposed method is akin to continuum
regression, and the resulting basis is called continuum directions. With a
presence of binary supervision data, these directions continuously bridge the
principal component, mean difference and linear discriminant directions, thus
ranging from unsupervised to fully supervised dimension reduction.
High-dimensional asymptotic studies of continuum directions for binary
supervision reveal several interesting facts. The conditions under which the
sample continuum directions are inconsistent, but their classification
performance is good, are specified. While the proposed method can be directly
used for binary and multi-category classification, its generalizations to
incorporate any form of auxiliary data are also presented. The proposed method
enjoys fast computation, and the performance is better or on par with more
computer-intensive alternatives.
Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and its Variance Estimate
Authors: Indrayudh Ghosal, Giles Hooker
Date: 2018-03-21 00:00:00
Tasks: Prediction Intervals

In this paper we propose using the principle of boosting to reduce the bias of a random forest prediction in the regression setting. From the original random forest fit we extract the residuals and then fit another random forest to these residuals. We call the sum of these two random forests a \textit{one-step boosted forest}. We show with simulated and real data that the one-step boosted forest has a reduced bias compared to the original random forest. The paper also provides a variance estimate of the one-step boosted forest by an extension of the infinitesimal Jackknife estimator. Using this variance estimate we can construct prediction intervals for the boosted forest and we show that they have good coverage probabilities. Combining the bias reduction and the variance estimate we show that the one-step boosted forest has a significant reduction in predictive mean squared error and thus an improvement in predictive performance. When applied on datasets from the UCI database, one-step boosted forest performs better than random forest and gradient boosting machine algorithms. Theoretically we can also extend such a boosting process to more than one step and the same principles outlined in this paper can be used to find variance estimates for such predictors. Such boosting will reduce bias even further but it risks over-fitting and also increases the computational burden.
Adversarial Defense based on Structure-to-Signal Autoencoders
Authors: Joachim Folz, Sebastian Palacio, Joern Hees, Damian Borth, Andreas Dengel
Date: 2018-03-21 00:00:00
Tasks: Adversarial Attack, Adversarial Defense, Decoder

Adversarial attack methods have demonstrated the fragility of deep neural
networks. Their imperceptible perturbations are frequently able fool
classifiers into potentially dangerous misclassifications. We propose a novel
way to interpret adversarial perturbations in terms of the effective input
signal that classifiers actually use. Based on this, we apply specially trained
autoencoders, referred to as S2SNets, as defense mechanism. They follow a
two-stage training scheme: first unsupervised, followed by a fine-tuning of the
decoder, using gradients from an existing classifier. S2SNets induce a shift in
the distribution of gradients propagated through them, stripping them from
class-dependent signal. We analyze their robustness against several white-box
and gray-box scenarios on the large ImageNet dataset. Our approach reaches
comparable resilience in white-box attack scenarios as other state-of-the-art
defenses in gray-box scenarios. We further analyze the relationships of
AlexNet, VGG 16, ResNet 50 and Inception v3 in adversarial space, and found
that VGG 16 is the easiest to fool, while perturbations from ResNet 50 are the
most transferable.
Musical Chair: Efficient Real-Time Recognition Using Collaborative IoT Devices
Authors: Ramyad Hadidi, Jiashen Cao, Matthew Woodward, Michael S. Ryoo, Hyesoon Kim
Date: 2018-02-05 00:00:00
Tasks: Action Recognition, CPU, GPU, Temporal Action Localization

The prevalence of Internet of things (IoT) devices and abundance of sensor
data has created an increase in real-time data processing such as recognition
of speech, image, and video. While currently such processes are offloaded to
the computationally powerful cloud system, a localized and distributed approach
is desirable because (i) it preserves the privacy of users and (ii) it omits
the dependency on cloud services. However, IoT networks are usually composed of
resource-constrained devices, and a single device is not powerful enough to
process real-time data. To overcome this challenge, we examine data and model
parallelism for such devices in the context of deep neural networks. We propose
Musical Chair to enable efficient, localized, and dynamic real-time recognition
by harvesting the aggregated computational power from the resource-constrained
devices in the same IoT network as input sensors. Musical chair adapts to the
availability of computing devices at runtime and adjusts to the inherit
dynamics of IoT networks. To demonstrate Musical Chair, on a network of
Raspberry PIs (up to 12) each connected to a camera, we implement a
state-of-the-art action recognition model for videos and two recognition models
for images. Compared to the Tegra TX2, an embedded low-power platform with a
six-core CPU and a GPU, our distributed action recognition system achieves not
only similar energy consumption but also twice the performance of the TX2.
Furthermore, in image recognition, Musical Chair achieves similar performance
and saves dynamic energy.