pwc-archive/papers-with-abstracts huggingface dataset:
Semi-supervised Domain Adaptation via Minimax Entropy
Authors: Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, Kate Saenko
Date: 2019-04-13 00:00:00
Proceeding: ICCV 2019 10
Tasks: Domain Adaptation, MME, Semi-supervised Domain Adaptation

Contemporary domain adaptation methods are very effective at aligning feature distributions of source and target domains without any target supervision. However, we show that these techniques perform poorly when even a few labeled examples are available in the target. To address this semi-supervised domain adaptation (SSDA) setting, we propose a novel Minimax Entropy (MME) approach that adversarially optimizes an adaptive few-shot model. Our base model consists of a feature encoding network, followed by a classification layer that computes the features' similarity to estimated prototypes (representatives of each class). Adaptation is achieved by alternately maximizing the conditional entropy of unlabeled target data with respect to the classifier and minimizing it with respect to the feature encoder. We empirically demonstrate the superiority of our method over many baselines, including conventional feature alignment and few-shot methods, setting a new state of the art for SSDA.
Topic Grouper: An Agglomerative Clustering Approach to Topic Modeling
Authors: Daniel Pfeifer, Jochen L. Leidner
Date: 2019-04-13 00:00:00
Tasks: Articles, Clustering

We introduce Topic Grouper as a complementary approach in the field of
probabilistic topic modeling. Topic Grouper creates a disjunctive partitioning
of the training vocabulary in a stepwise manner such that resulting partitions
represent topics. It is governed by a simple generative model, where the
likelihood to generate the training documents via topics is optimized. The
algorithm starts with one-word topics and joins two topics at every step. It
therefore generates a solution for every desired number of topics ranging
between the size of the training vocabulary and one. The process represents an
agglomerative clustering that corresponds to a binary tree of topics. A
resulting tree may act as a containment hierarchy, typically with more general
topics towards the root of tree and more specific topics towards the leaves.
Topic Grouper is not governed by a background distribution such as the
Dirichlet and avoids hyper parameter optimizations.
  We show that Topic Grouper has reasonable predictive power and also a
reasonable theoretical and practical complexity. Topic Grouper can deal well
with stop words and function words and tends to push them into their own
topics. Also, it can handle topic distributions, where some topics are more
frequent than others. We present typical examples of computed topics from
evaluation datasets, where topics appear conclusive and coherent. In this
context, the fact that each word belongs to exactly one topic is not a major
limitation; in some scenarios this can even be a genuine advantage, e.g.~a
related shopping basket analysis may aid in optimizing groupings of articles in
sales catalogs.
Low-Latency Speaker-Independent Continuous Speech Separation
Authors: Takuya Yoshioka, Zhuo Chen, Changliang Liu, Xiong Xiao, Hakan Erdogan, Dimitrios Dimitriadis
Date: 2019-04-13 00:00:00
Tasks: speech-recognition, Speech Recognition, Speech Separation

Speaker independent continuous speech separation (SI-CSS) is a task of
converting a continuous audio stream, which may contain overlapping voices of
unknown speakers, into a fixed number of continuous signals each of which
contains no overlapping speech segment. A separated, or cleaned, version of
each utterance is generated from one of SI-CSS's output channels
nondeterministically without being split up and distributed to multiple
channels. A typical application scenario is transcribing multi-party
conversations, such as meetings, recorded with microphone arrays. The output
signals can be simply sent to a speech recognition engine because they do not
include speech overlaps. The previous SI-CSS method uses a neural network
trained with permutation invariant training and a data-driven beamformer and
thus requires much processing latency. This paper proposes a low-latency SI-CSS
method whose performance is comparable to that of the previous method in a
microphone array-based meeting transcription task.This is achieved (1) by using
a new speech separation network architecture combined with a double buffering
scheme and (2) by performing enhancement with a set of fixed beamformers
followed by a neural post-filter.
Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering
Authors: Bo Chen, Xiaotao Gu, Yu-Feng Hu, Siliang Tang, Guoping Hu, Yueting Zhuang, Xiang Ren
Date: 2019-04-13 00:00:00
Proceeding: NAACL 2019 6
Tasks: Clustering, Entity Typing

Recently, distant supervision has gained great success on Fine-grained Entity
Typing (FET). Despite its efficiency in reducing manual labeling efforts, it
also brings the challenge of dealing with false entity type labels, as distant
supervision assigns labels in a context agnostic manner. Existing works
alleviated this issue with partial-label loss, but usually suffer from
confirmation bias, which means the classifier fit a pseudo data distribution
given by itself. In this work, we propose to regularize distantly supervised
models with Compact Latent Space Clustering (CLSC) to bypass this problem and
effectively utilize noisy data yet. Our proposed method first dynamically
constructs a similarity graph of different entity mentions; infer the labels of
noisy instances via label propagation. Based on the inferred labels, mention
embeddings are updated accordingly to encourage entity mentions with close
semantics to form a compact cluster in the embedding space,thus leading to
better classification performance. Extensive experiments on standard benchmarks
show that our CLSC model consistently outperforms state-of-the-art distantly
supervised entity typing systems by a significant margin.
Legal Area Classification: A Comparative Study of Text Classifiers on Singapore Supreme Court Judgments
Authors: Jerrold Soh Tsin Howe, Lim How Khang, Ian Ernst Chai
Date: 2019-04-13 00:00:00
Proceeding: WS 2019 6
Tasks: BIG-bench Machine Learning, General Classification, Language Modeling, Language Modelling

This paper conducts a comparative study on the performance of various machine
learning (``ML'') approaches for classifying judgments into legal areas. Using
a novel dataset of 6,227 Singapore Supreme Court judgments, we investigate how
state-of-the-art NLP methods compare against traditional statistical models
when applied to a legal corpus that comprised few but lengthy documents. All
approaches tested, including topic model, word embedding, and language
model-based classifiers, performed well with as little as a few hundred
judgments. However, more work needs to be done to optimize state-of-the-art
methods for the legal domain.
Transformable Bottleneck Networks
Authors: Kyle Olszewski, Sergey Tulyakov, Oliver Woodford, Hao Li, Linjie Luo
Date: 2019-04-13 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Reconstruction, Decoder, Novel View Synthesis

We propose a novel approach to performing fine-grained 3D manipulation of image content via a convolutional neural network, which we call the Transformable Bottleneck Network (TBN). It applies given spatial transformations directly to a volumetric bottleneck within our encoder-bottleneck-decoder architecture. Multi-view supervision encourages the network to learn to spatially disentangle the feature space within the bottleneck. The resulting spatial structure can be manipulated with arbitrary spatial transformations. We demonstrate the efficacy of TBNs for novel view synthesis, achieving state-of-the-art results on a challenging benchmark. We demonstrate that the bottlenecks produced by networks trained for this task contain meaningful spatial structure that allows us to intuitively perform a variety of image manipulations in 3D, well beyond the rigid transformations seen during training. These manipulations include non-uniform scaling, non-rigid warping, and combining content from different images. Finally, we extract explicit 3D structure from the bottleneck, performing impressive 3D reconstruction from a single input image.
Dynamic Node Embeddings from Edge Streams
Authors: John Boaz Lee, Giang Nguyen, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim
Date: 2019-04-12 00:00:00
Tasks: Representation Learning, valid

Networks evolve continuously over time with the addition, deletion, and changing of links and nodes. Such temporal networks (or edge streams) consist of a sequence of timestamped edges and are seemingly ubiquitous. Despite the importance of accurately modeling the temporal information, most embedding methods ignore it entirely or approximate the temporal network using a sequence of static snapshot graphs. In this work, we propose using the notion of temporal walks for learning dynamic embeddings from temporal networks. Temporal walks capture the temporally valid interactions (e.g., flow of information, spread of disease) in the dynamic network in a lossless fashion. Based on the notion of temporal walks, we describe a general class of embeddings called continuous-time dynamic network embeddings (CTDNEs) that completely avoid the issues and problems that arise when approximating the temporal network as a sequence of static snapshot graphs. Unlike previous work, CTDNEs learn dynamic node embeddings directly from the temporal network at the finest temporal granularity and thus use only temporally valid information. As such CTDNEs naturally support online learning of the node embeddings in a streaming real-time fashion. Finally, the experiments demonstrate the effectiveness of this class of embedding methods that leverage temporal walks as it achieves an average gain in AUC of 11.9% across all methods and graphs.
Learning Shape Templates with Structured Implicit Functions
Authors: Kyle Genova, Forrester Cole, Daniel Vlasic, Aaron Sarna, William T. Freeman, Thomas Funkhouser
Date: 2019-04-12 00:00:00
Proceeding: ICCV 2019 10
Tasks: Semantic Segmentation

Template 3D shapes are useful for many tasks in graphics and vision,
including fitting observation data, analyzing shape collections, and
transferring shape attributes. Because of the variety of geometry and topology
of real-world shapes, previous methods generally use a library of hand-made
templates. In this paper, we investigate learning a general shape template from
data. To allow for widely varying geometry and topology, we choose an implicit
surface representation based on composition of local shape elements. While long
known to computer graphics, this representation has not yet been explored in
the context of machine learning for vision. We show that structured implicit
functions are suitable for learning and allow a network to smoothly and
simultaneously fit multiple classes of shapes. The learned shape template
supports applications such as shape exploration, correspondence, abstraction,
interpolation, and semantic segmentation from an RGB image.
Remaining Useful Life Estimation Using Functional Data Analysis
Authors: Qiyao Wang, Shuai Zheng, Ahmed Farahat, Susumu Serita, Chetan Gupta
Date: 2019-04-12 00:00:00
Tasks: Management, Time Series, Time Series Analysis

Remaining Useful Life (RUL) of an equipment or one of its components is
defined as the time left until the equipment or component reaches its end of
useful life. Accurate RUL estimation is exceptionally beneficial to Predictive
Maintenance, and Prognostics and Health Management (PHM). Data driven
approaches which leverage the power of algorithms for RUL estimation using
sensor and operational time series data are gaining popularity. Existing
algorithms, such as linear regression, Convolutional Neural Network (CNN),
Hidden Markov Models (HMMs), and Long Short-Term Memory (LSTM), have their own
limitations for the RUL estimation task. In this work, we propose a novel
Functional Data Analysis (FDA) method called functional Multilayer Perceptron
(functional MLP) for RUL estimation. Functional MLP treats time series data
from multiple equipment as a sample of random continuous processes over time.
FDA explicitly incorporates both the correlations within the same equipment and
the random variations across different equipment's sensor time series into the
model. FDA also has the benefit of allowing the relationship between RUL and
sensor variables to vary over time. We implement functional MLP on the
benchmark NASA C-MAPSS data and evaluate the performance using two
popularly-used metrics. Results show the superiority of our algorithm over all
the other state-of-the-art methods.
Detecting Anemia from Retinal Fundus Images
Authors: Akinori Mitani, Yun Liu, Abigail Huang, Greg S. Corrado, Lily Peng, Dale R. Webster, Naama Hammel, Avinash V. Varadarajan
Date: 2019-04-12 00:00:00
Tasks: Diagnostic

Despite its high prevalence, anemia is often undetected due to the
invasiveness and cost of screening and diagnostic tests. Though some
non-invasive approaches have been developed, they are less accurate than
invasive methods, resulting in an unmet need for more accurate non-invasive
methods. Here, we show that deep learning-based algorithms can detect anemia
and quantify several related blood measurements using retinal fundus images
both in isolation and in combination with basic metadata such as patient
demographics. On a validation dataset of 11,388 patients from the UK Biobank,
our algorithms achieved a mean absolute error of 0.63 g/dL (95% confidence
interval (CI) 0.62-0.64) in quantifying hemoglobin concentration and an area
under receiver operating characteristic curve (AUC) of 0.88 (95% CI 0.86-0.89)
in detecting anemia. This work shows the potential of automated non-invasive
anemia screening based on fundus images, particularly in diabetic patients, who
may have regular retinal imaging and are at increased risk of further morbidity
and mortality from anemia.
Patch redundancy in images: a statistical testing framework and some applications
Authors: De Bortoli Valentin, Desolneux Agnès, Galerne Bruno, Leclaire Arthur
Date: 2019-04-12 00:00:00
Tasks: Denoising

In this work we introduce a statistical framework in order to analyze the
spatial redundancy in natural images. This notion of spatial redundancy must be
defined locally and thus we give some examples of functions (auto-similarity
and template similarity) which, given one or two images, computes a similarity
measurement between patches. Two patches are said to be similar if the
similarity measurement is small enough. To derive a criterion for taking a
decision on the similarity between two patches we present an a contrario model.
Namely, two patches are said to be similar if the associated similarity
measurement is unlikely to happen in a background model. Choosing Gaussian
random fields as background models we derive non-asymptotic expressions for the
probability distribution function of similarity measurements. We introduce a
fast algorithm in order to assess redundancy in natural images and present
applications in denoising, periodicity analysis and texture ranking.
Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing
Authors: Jianguo Chen, Kenli Li, Qingying Deng, Keqin Li, Philip S. Yu
Date: 2019-04-12 00:00:00
Tasks: Edge-computing

In this paper, we propose a Distributed Intelligent Video Surveillance (DIVS)
system using Deep Learning (DL) algorithms and deploy it in an edge computing
environment. We establish a multi-layer edge computing architecture and a
distributed DL training model for the DIVS system. The DIVS system can migrate
computing workloads from the network center to network edges to reduce huge
network communication overhead and provide low-latency and accurate video
analysis solutions. We implement the proposed DIVS system and address the
problems of parallel training, model synchronization, and workload balancing.
Task-level parallel and model-level parallel training methods are proposed to
further accelerate the video analysis process. In addition, we propose a model
parameter updating method to achieve model synchronization of the global DL
model in a distributed EC environment. Moreover, a dynamic data migration
approach is proposed to address the imbalance of workload and computational
power of edge nodes. Experimental results showed that the EC architecture can
provide elastic and scalable computing power, and the proposed DIVS system can
efficiently handle video surveillance and analysis tasks.
Multi-View Stereo by Temporal Nonparametric Fusion
Authors: Yuxin Hou, Juho Kannala, Arno Solin
Date: 2019-04-12 00:00:00
Proceeding: ICCV 2019 10
Tasks: Decoder, Depth Estimation, Disparity Estimation

We propose a novel idea for depth estimation from multi-view image-pose pairs, where the model has capability to leverage information from previous latent-space encodings of the scene. This model uses pairs of images and poses, which are passed through an encoder--decoder model for disparity estimation. The novelty lies in soft-constraining the bottleneck layer by a nonparametric Gaussian process prior. We propose a pose-kernel structure that encourages similar poses to have resembling latent spaces. The flexibility of the Gaussian process (GP) prior provides adapting memory for fusing information from previous views. We train the encoder--decoder and the GP hyperparameters jointly end-to-end. In addition to a batch method, we derive a lightweight estimation scheme that circumvents standard pitfalls in scaling Gaussian process inference, and demonstrate how our scheme can run in real-time on smart devices.
Macrocanonical Models for Texture Synthesis
Authors: De Bortoli Valentin, Desolneux Agnès, Galerne Bruno, Leclaire Arthur
Date: 2019-04-12 00:00:00
Tasks: Texture Synthesis

In this article we consider macrocanonical models for texture synthesis. In
these models samples are generated given an input texture image and a set of
features which should be matched in expectation. It is known that if the images
are quantized, macrocanonical models are given by Gibbs measures, using the
maximum entropy principle. We study conditions under which this result extends
to real-valued images. If these conditions hold, finding a macrocanonical model
amounts to minimizing a convex function and sampling from an associated Gibbs
measure. We analyze an algorithm which alternates between sampling and
minimizing. We present experiments with neural network features and study the
drawbacks and advantages of using this sampling scheme.
Information Theoretic Lower Bounds on Negative Log Likelihood
Authors: Luis A. Lastras
Date: 2019-04-12 00:00:00

In this article we use rate-distortion theory, a branch of information theory
devoted to the problem of lossy compression, to shed light on an important
problem in latent variable modeling of data: is there room to improve the
model? One way to address this question is to find an upper bound on the
probability (equivalently a lower bound on the negative log likelihood) that
the model can assign to some data as one varies the prior and/or the likelihood
function in a latent variable model. The core of our contribution is to
formally show that the problem of optimizing priors in latent variable models
is exactly an instance of the variational optimization problem that information
theorists solve when computing rate-distortion functions, and then to use this
to derive a lower bound on negative log likelihood. Moreover, we will show that
if changing the prior can improve the log likelihood, then there is a way to
change the likelihood function instead and attain the same log likelihood, and
thus rate-distortion theory is of relevance to both optimizing priors as well
as optimizing likelihood functions. We will experimentally argue for the
usefulness of quantities derived from rate-distortion theory in latent variable
modeling by applying them to a problem in image modeling.
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
Authors: Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum
Date: 2019-04-12 00:00:00
Tasks: Deep Reinforcement Learning, Imitation Learning, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A critical flaw of existing inverse reinforcement learning (IRL) methods is their inability to significantly outperform the demonstrator. This is because IRL typically seeks a reward function that makes the demonstrator appear near-optimal, rather than inferring the underlying intentions of the demonstrator that may have been poorly executed in practice. In this paper, we introduce a novel reward-learning-from-observation algorithm, Trajectory-ranked Reward EXtrapolation (T-REX), that extrapolates beyond a set of (approximately) ranked demonstrations in order to infer high-quality reward functions from a set of potentially poor demonstrations. When combined with deep reinforcement learning, T-REX outperforms state-of-the-art imitation learning and IRL methods on multiple Atari and MuJoCo benchmark tasks and achieves performance that is often more than twice the performance of the best demonstration. We also demonstrate that T-REX is robust to ranking noise and can accurately extrapolate intention by simply watching a learner noisily improve at a task over time.
Towards Accurate One-Stage Object Detection with AP-Loss
Authors: Kean Chen, Jianguo Li, Weiyao Lin, John See, Ji Wang, Ling-Yu Duan, Zhibo Chen, Changwei He, Junni Zou
Date: 2019-04-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Classification, General Classification, Object, object-detection, Object Detection

One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme foreground-background class imbalance issue due to the large number of anchors. This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the Average-Precision loss (AP-loss) for the ranking problem. Due to its non-differentiability and non-convexity, the AP-loss cannot be optimized directly. For this purpose, we develop a novel optimization algorithm, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks. We verify good convergence property of the proposed algorithm theoretically and empirically. Experimental results demonstrate notable performance improvement in state-of-the-art one-stage detectors based on AP-loss over different kinds of classification-losses on various benchmarks, without changing the network architectures. Code is available at https://github.com/cccorn/AP-loss.