pwc-archive/papers-with-abstracts huggingface dataset:
Efficient transfer learning and online adaptation with latent variable models for continuous control
Authors: Christian F. Perez, Felipe Petroski Such, Theofanis Karaletsos
Date: 2018-12-08 00:00:00
Tasks: Bayesian Inference, continuous-control, Continuous Control, Friction, Transfer Learning, Variational Inference

Traditional model-based RL relies on hand-specified or learned models of
transition dynamics of the environment. These methods are sample efficient and
facilitate learning in the real world but fail to generalize to subtle
variations in the underlying dynamics, e.g., due to differences in mass,
friction, or actuators across robotic agents or across time. We propose using
variational inference to learn an explicit latent representation of unknown
environment properties that accelerates learning and facilitates generalization
on novel environments at test time. We use Online Bayesian Inference of these
learned latents to rapidly adapt online to changes in environments without
retaining large replay buffers of recent data. Combined with a neural network
ensemble that models dynamics and captures uncertainty over dynamics, our
approach demonstrates positive transfer during training and online adaptation
on the continuous control task HalfCheetah.
Learning Graph Representation via Formal Concept Analysis
Authors: Yuka Yoneda, Mahito Sugiyama, Takashi Washio
Date: 2018-12-08 00:00:00

We present a novel method that can learn a graph representation from
multivariate data. In our representation, each node represents a cluster of
data points and each edge represents the subset-superset relationship between
clusters, which can be mutually overlapped. The key to our method is to use
formal concept analysis (FCA), which can extract hierarchical relationships
between clusters based on the algebraic closedness property. We empirically
show that our method can effectively extract hierarchical structures of
clusters compared to the baseline method.
Biometric Recognition System (Algorithm)
Authors: Rahul Kumar Jaiswal, Gaurav Saxena
Date: 2018-12-08 00:00:00
Tasks: fingerprint verification, Image Enhancement

Fingerprints are the most widely deployed form of biometric identification.
No two individuals share the same fingerprint because they have unique
biometric identifiers. This paper presents an efficient fingerprint
verification algorithm which improves matching accuracy. Fingerprint images get
degraded and corrupted due to variations in skin and impression conditions.
Thus, image enhancement techniques are employed prior to singular point
detection and minutiae extraction. Singular point is the point of maximum
curvature. It is determined by the normal of each fingerprint ridge, and then
following them inward towards the centre. The local ridge features known as
minutiae is extracted using cross-number method to find ridge endings and ridge
bifurcations. The proposed algorithm chooses a radius and draws a circle with
core point as centre, making fingerprint images rotationally invariant and
uniform. The radius can be varied according to the accuracy depending on the
particular application. Morphological techniques such as clean, spur and
H-break is employed to remove noise, followed by removing spurious minutiae.
Templates are created based on feature vector extraction and databases are made
for verification and identification for the fingerprint images taken from
Fingerprint Verification Competition (FVC2002). Minimum Euclidean distance is
calculated between saved template and the test fingerprint image template and
compared with the set threshold for matching decision. For the performance
evaluation of the proposed algorithm various measures, equal error rate (EER),
Dmin at EER, accuracy and threshold are evaluated and plotted. The measures
demonstrate that the proposed algorithm is more effective and robust.
Learning Montezuma's Revenge from a Single Demonstration
Authors: Tim Salimans, Richard Chen
Date: 2018-12-08 00:00:00
Tasks: Montezuma's Revenge, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a new method for learning from a single demonstration to solve
hard exploration tasks like the Atari game Montezuma's Revenge. Instead of
imitating human demonstrations, as proposed in other recent works, our approach
is to maximize rewards directly. Our agent is trained using off-the-shelf
reinforcement learning, but starts every episode by resetting to a state from a
demonstration. By starting from such demonstration states, the agent requires
much less exploration to learn a game compared to when it starts from the
beginning of the game at every episode. We analyze reinforcement learning for
tasks with sparse rewards in a simple toy environment, where we show that the
run-time of standard RL methods scales exponentially in the number of states
between rewards. Our method reduces this to quadratic scaling, opening up many
tasks that were previously infeasible. We then apply our method to Montezuma's
Revenge, for which we present a trained agent achieving a high-score of 74,500,
better than any previously published result.
What is the Effect of Importance Weighting in Deep Learning?
Authors: Jonathon Byrd, Zachary C. Lipton
Date: 2018-12-08 00:00:00
Tasks: Causal Inference, Deep Learning, Domain Adaptation, L2 Regularization, Reinforcement Learning

Importance-weighted risk minimization is a key ingredient in many machine learning algorithms for causal inference, domain adaptation, class imbalance, and off-policy reinforcement learning. While the effect of importance weighting is well-characterized for low-capacity misspecified models, little is known about how it impacts over-parameterized, deep neural networks. This work is inspired by recent theoretical results showing that on (linearly) separable data, deep linear networks optimized by SGD learn weight-agnostic solutions, prompting us to ask, for realistic deep networks, for which many practical datasets are separable, what is the effect of importance weighting? We present the surprising finding that while importance weighting impacts models early in training, its effect diminishes over successive epochs. Moreover, while L2 regularization and batch normalization (but not dropout), restore some of the impact of importance weighting, they express the effect via (seemingly) the wrong abstraction: why should practitioners tweak the L2 regularization, and by how much, to produce the correct weighting effect? Our experiments confirm these findings across a range of architectures and datasets.
Unsupervised Learning of Monocular Depth Estimation with Bundle Adjustment, Super-Resolution and Clip Loss
Authors: Lipu Zhou, Jiamin Ye, Montiel Abello, Shengze Wang, Michael Kaess
Date: 2018-12-08 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation, Super-Resolution

We present a novel unsupervised learning framework for single view depth
estimation using monocular videos. It is well known in 3D vision that enlarging
the baseline can increase the depth estimation accuracy, and jointly optimizing
a set of camera poses and landmarks is essential. In previous monocular
unsupervised learning frameworks, only part of the photometric and geometric
constraints within a sequence are used as supervisory signals. This may result
in a short baseline and overfitting. Besides, previous works generally estimate
a low resolution depth from a low resolution impute image. The low resolution
depth is then interpolated to recover the original resolution. This strategy
may generate large errors on object boundaries, as the depth of background and
foreground are mixed to yield the high resolution depth. In this paper, we
introduce a bundle adjustment framework and a super-resolution network to solve
the above two problems. In bundle adjustment, depths and poses of an image
sequence are jointly optimized, which increases the baseline by establishing
the relationship between farther frames. The super resolution network learns to
estimate a high resolution depth from a low resolution image. Additionally, we
introduce the clip loss to deal with moving objects and occlusion. Experimental
results on the KITTI dataset show that the proposed algorithm outperforms the
state-of-the-art unsupervised methods using monocular sequences, and achieves
comparable or even better result compared to unsupervised methods using stereo
sequences.
Multi-Dimensional Scaling on Groups
Authors: Mark Blumstein, Henry Kvinge
Date: 2018-12-08 00:00:00
Tasks: Dimensionality Reduction

Leveraging the intrinsic symmetries in data for clear and efficient analysis is an important theme in signal processing and other data-driven sciences. A basic example of this is the ubiquity of the discrete Fourier transform which arises from translational symmetry (i.e. time-delay/phase-shift). Particularly important in this area is understanding how symmetries inform the algorithms that we apply to our data. In this paper we explore the behavior of the dimensionality reduction algorithm multi-dimensional scaling (MDS) in the presence of symmetry. We show that understanding the properties of the underlying symmetry group allows us to make strong statements about the output of MDS even before applying the algorithm itself. In analogy to Fourier theory, we show that in some cases only a handful of fundamental "frequencies" (irreducible representations derived from the corresponding group) contribute information for the MDS Euclidean embedding.
An Unsupervised Approach for Aspect Category Detection Using Soft Cosine Similarity Measure
Authors: Erfan Ghadery, Sajad Movahedi, Heshaam Faili, Azadeh Shakery
Date: 2018-12-08 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Category Detection, Feature Engineering, Sentence, Sentiment Analysis

Aspect category detection is one of the important and challenging subtasks of aspect-based sentiment analysis. Given a set of pre-defined categories, this task aims to detect categories which are indicated implicitly or explicitly in a given review sentence. Supervised machine learning approaches perform well to accomplish this subtask. Note that, the performance of these methods depends on the availability of labeled train data, which is often difficult and costly to obtain. Besides, most of these supervised methods require feature engineering to perform well. In this paper, we propose an unsupervised method to address aspect category detection task without the need for any feature engineering. Our method utilizes clusters of unlabeled reviews and soft cosine similarity measure to accomplish aspect category detection task. Experimental results on SemEval-2014 restaurant dataset shows that proposed unsupervised approach outperforms several baselines by a substantial margin.
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process
Authors: Jeremiah Zhe Liu, John Paisley, Marianthi-Anna Kioumourtzoglou, Brent A. Coull
Date: 2018-12-08 00:00:00
Tasks: Ensemble Learning, Model Selection, Prediction, Variational Inference

Ensemble learning is a mainstay in modern data science practice. Conventional
ensemble algorithms assigns to base models a set of deterministic, constant
model weights that (1) do not fully account for variations in base model
accuracy across subgroups, nor (2) provide uncertainty estimates for the
ensemble prediction, which could result in mis-calibrated (i.e. precise but
biased) predictions that could in turn negatively impact the algorithm
performance in real-word applications. In this work, we present an adaptive,
probabilistic approach to ensemble learning using dependent tail-free process
as ensemble weight prior. Given input feature $\mathbf{x}$, our method
optimally combines base models based on their predictive accuracy in the
feature space $\mathbf{x} \in \mathcal{X}$, and provides interpretable
uncertainty estimates both in model selection and in ensemble prediction. To
encourage scalable and calibrated inference, we derive a structured variational
inference algorithm that jointly minimize KL objective and the model's
calibration score (i.e. Continuous Ranked Probability Score (CRPS)). We
illustrate the utility of our method on both a synthetic nonlinear function
regression task, and on the real-world application of spatio-temporal
integration of particle pollution prediction models in New England.
Signal recognition and background suppression by matched filters and neural networks for Tunka-Rex
Authors: D. Shipilov, P. A. Bezyazeekov, N. M. Budnev, D. Chernykh, O. Fedorov, O. A. Gress, A. Haungs, R. Hiller, T. Huege, Y. Kazarina, M. Kleifges, E. E. Korosteleva, D. Kostunin, L. A. Kuzmichev, V. Lenok, N. Lubsandorzhiev, T. Marshalkina, R. Monkhoev, E. Osipova, A. Pakhorukov, L. Pankov, V. V. Prosin, F. G. Schröder, A. Zagorodnikov
Date: 2018-12-08 00:00:00

The Tunka Radio Extension (Tunka-Rex) is a digital antenna array, which measures the radio emission of the cosmic-ray air-showers in the frequency band of 30-80 MHz. Tunka-Rex is co-located with TAIGA experiment in Siberia and consists of 63 antennas, 57 of them are in a densely instrumented area of about 1 km\textsuperscript{2}. In the present work we discuss the improvements of the signal reconstruction applied for the Tunka-Rex. At the first stage we implemented matched filtering using averaged signals as template. The simulation study has shown that matched filtering allows one to decrease the threshold of signal detection and increase its purity. However, the maximum performance of matched filtering is achievable only in case of white noise, while in reality the noise is not fully random due to different reasons. To recognize hidden features of the noise and treat them, we decided to use convolutional neural network with autoencoder architecture. Taking the recorded trace as an input, the autoencoder returns denoised trace, i.e. removes all signal-unrelated amplitudes. We present the comparison between standard method of signal reconstruction, matched filtering and autoencoder, and discuss the prospects of application of neural networks for lowering the threshold of digital antenna arrays for cosmic-ray detection.
Secure Federated Transfer Learning
Authors: Yang Liu, Yan Kang, Chaoping Xing, Tianjian Chen, Qiang Yang
Date: 2018-12-08 00:00:00
Tasks: BIG-bench Machine Learning, Privacy Preserving, Transfer Learning

Machine learning relies on the availability of a vast amount of data for training. However, in reality, most data are scattered across different organizations and cannot be easily integrated under many legal and practical constraints. In this paper, we introduce a new technique and framework, known as federated transfer learning (FTL), to improve statistical models under a data federation. The federation allows knowledge to be shared without compromising user privacy, and enables complimentary knowledge to be transferred in the network. As a result, a target-domain party can build more flexible and powerful models by leveraging rich labels from a source-domain party. A secure transfer cross validation approach is also proposed to guard the FTL performance under the federation. The framework requires minimal modifications to the existing model structure and provides the same level of accuracy as the non-privacy-preserving approach. This framework is very flexible and can be effectively adapted to various secure multi-party machine learning tasks.
GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud
Authors: Li Yi, Wang Zhao, He Wang, Minhyuk Sung, Leonidas Guibas
Date: 2018-12-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Instance Segmentation, 3D Object Detection, Instance Segmentation, Object, regression, Segmentation, Semantic Segmentation

We introduce a novel 3D object proposal approach named Generative Shape
Proposal Network (GSPN) for instance segmentation in point cloud data. Instead
of treating object proposal as a direct bounding box regression problem, we
take an analysis-by-synthesis strategy and generate proposals by reconstructing
shapes from noisy observations in a scene. We incorporate GSPN into a novel 3D
instance segmentation framework named Region-based PointNet (R-PointNet) which
allows flexible proposal refinement and instance segmentation generation. We
achieve state-of-the-art performance on several 3D instance segmentation tasks.
The success of GSPN largely comes from its emphasis on geometric understandings
during object proposal, which greatly reducing proposals with low objectness.
A deep learning-based remaining useful life prediction approach for bearings
Authors: Cheng Cheng, Guijun Ma, Yong Zhang, Mingyang Sun, Fei Teng, Han Ding, Ye Yuan
Date: 2018-12-08 00:00:00
Tasks: Deep Learning

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions.
Detecting Adversarial Examples in Convolutional Neural Networks
Authors: Stefanos Pertigkiozoglou, Petros Maragos
Date: 2018-12-08 00:00:00
Tasks: image-classification, Image Classification

The great success of convolutional neural networks has caused a massive
spread of the use of such models in a large variety of Computer Vision
applications. However, these models are vulnerable to certain inputs, the
adversarial examples, which although are not easily perceived by humans, they
can lead a neural network to produce faulty results. This paper focuses on the
detection of adversarial examples, which are created for convolutional neural
networks that perform image classification. We propose three methods for
detecting possible adversarial examples and after we analyze and compare their
performance, we combine their best aspects to develop an even more robust
approach. The first proposed method is based on the regularization of the
feature vector that the neural network produces as output. The second method
detects adversarial examples by using histograms, which are created from the
outputs of the hidden layers of the neural network. These histograms create a
feature vector which is used as the input of an SVM classifier, which
classifies the original input either as an adversarial or as a real input.
Finally, for the third method we introduce the concept of the residual image,
which contains information about the parts of the input pattern that are
ignored by the neural network. This method aims at the detection of possible
adversarial examples, by using the residual image and reinforcing the parts of
the input pattern that are ignored by the neural network. Each one of these
methods has some novelties and by combining them we can further improve the
detection results. For the proposed methods and their combination, we present
the results of detecting adversarial examples on the MNIST dataset. The
combination of the proposed methods offers some improvements over similar state
of the art approaches.
Learning to Assemble Neural Module Tree Networks for Visual Grounding
Authors: Daqing Liu, Hanwang Zhang, Feng Wu, Zheng-Jun Zha
Date: 2018-12-08 00:00:00
Proceeding: ICCV 2019 10
Tasks: Dependency Parsing, Natural Language Visual Grounding, Sentence, Sentence Embedding, Sentence-Embedding, Triplet, Visual Grounding, Visual Reasoning

Visual grounding, a task to ground (i.e., localize) natural language in images, essentially requires composite visual reasoning. However, existing methods over-simplify the composite nature of language into a monolithic sentence embedding or a coarse composition of subject-predicate-object triplet. In this paper, we propose to ground natural language in an intuitive, explainable, and composite fashion as it should be. In particular, we develop a novel modular network called Neural Module Tree network (NMTree) that regularizes the visual grounding along the dependency parsing tree of the sentence, where each node is a neural module that calculates visual attention according to its linguistic feature, and the grounding score is accumulated in a bottom-up direction where as needed. NMTree disentangles the visual grounding from the composite reasoning, allowing the former to only focus on primitive and easy-to-generalize patterns. To reduce the impact of parsing errors, we train the modules and their assembly end-to-end by using the Gumbel-Softmax approximation and its straight-through gradient estimator, accounting for the discrete nature of module assembly. Overall, the proposed NMTree consistently outperforms the state-of-the-arts on several benchmarks. Qualitative results show explainable grounding score calculation in great detail.
No Peek: A Survey of private distributed deep learning
Authors: Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar, Otkrist Gupta, Abhimanyu Dubey
Date: 2018-12-08 00:00:00
Tasks: Deep Learning, Federated Learning, Survey

We survey distributed deep learning models for training or inference without
accessing raw data from clients. These methods aim to protect confidential
patterns in data while still allowing servers to train models. The distributed
deep learning methods of federated learning, split learning and large batch
stochastic gradient descent are compared in addition to private and secure
approaches of differential privacy, homomorphic encryption, oblivious transfer
and garbled circuits in the context of neural networks. We study their
benefits, limitations and trade-offs with regards to computational resources,
data leakage and communication efficiency and also share our anticipated future
trends.
Sampling-based Bayesian Inference with gradient uncertainty
Authors: Chanwoo Park, Jae Myung Kim, Seok Hyeon Ha, Jungwoo Lee
Date: 2018-12-08 00:00:00
Tasks: Bayesian Inference

Deep neural networks(NNs) have achieved impressive performance, often exceed human performance on many computer vision tasks. However, one of the most challenging issues that still remains is that NNs are overconfident in their predictions, which can be very harmful when this arises in safety critical applications. In this paper, we show that predictive uncertainty can be efficiently estimated when we incorporate the concept of gradients uncertainty into posterior sampling. The proposed method is tested on two different datasets, MNIST for in-distribution confusing examples and notMNIST for out-of-distribution data. We show that our method is able to efficiently represent predictive uncertainty on both datasets.