pwc-archive/papers-with-abstracts huggingface dataset:
$HS^2$: Active Learning over Hypergraphs
Authors: I Chien, Huozhi Zhou, Pan Li
Date: 2018-11-25 00:00:00
Tasks: Active Learning

We propose a hypergraph-based active learning scheme which we term $HS^2$,
$HS^2$ generalizes the previously reported algorithm $S^2$ originally proposed
for graph-based active learning with pointwise queries [Dasarathy et al., COLT
2015]. Our $HS^2$ method can accommodate hypergraph structures and allows one
to ask both pointwise queries and pairwise queries. Based on a novel parametric
system particularly designed for hypergraphs, we derive theoretical results on
the query complexity of $HS^2$ for the above described generalized settings.
Both the theoretical and empirical results show that $HS^2$ requires a
significantly fewer number of queries than $S^2$ when one uses $S^2$ over a
graph obtained from the corresponding hypergraph via clique expansion.
Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification
Authors: Zheng Li, Ying WEI, Yu Zhang, Xiang Zhang, Xin Li, Qiang Yang
Date: 2018-11-16 00:00:00
Proceeding: AAAI 2019 2018 11
Tasks: General Classification, Sentence, Sentiment Analysis, Sentiment Classification

Aspect-level sentiment classification (ASC) aims at identifying sentiment
polarities towards aspects in a sentence, where the aspect can behave as a
general Aspect Category (AC) or a specific Aspect Term (AT). However, due to
the especially expensive and labor-intensive labeling, existing public corpora
in AT-level are all relatively small. Meanwhile, most of the previous methods
rely on complicated structures with given scarce data, which largely limits the
efficacy of the neural models. In this paper, we exploit a new direction named
coarse-to-fine task transfer, which aims to leverage knowledge learned from a
rich-resource source domain of the coarse-grained AC task, which is more easily
accessible, to improve the learning in a low-resource target domain of the
fine-grained AT task. To resolve both the aspect granularity inconsistency and
feature mismatch between domains, we propose a Multi-Granularity Alignment
Network (MGAN). In MGAN, a novel Coarse2Fine attention guided by an auxiliary
task can help the AC task modeling at the same fine-grained level with the AT
task. To alleviate the feature false alignment, a contrastive feature alignment
method is adopted to align aspect-specific feature representations
semantically. In addition, a large-scale multi-domain dataset for the AC task
is provided. Empirically, extensive experiments demonstrate the effectiveness
of the MGAN.
Word Mover's Embedding: From Word2Vec to Document Embedding
Authors: Lingfei Wu, Ian E. H. Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock
Date: 2018-10-30 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Document Embedding, General Classification, Sentence, Sentence Embedding, Sentence-Embedding, text-classification, Text Classification, Word Embeddings

While the celebrated Word2Vec technique yields semantically rich
representations for individual words, there has been relatively less success in
extending to generate unsupervised sentences or documents embeddings. Recent
work has demonstrated that a distance measure between documents called
\emph{Word Mover's Distance} (WMD) that aligns semantically similar words,
yields unprecedented KNN classification accuracy. However, WMD is expensive to
compute, and it is hard to extend its use beyond a KNN classifier. In this
paper, we propose the \emph{Word Mover's Embedding } (WME), a novel approach to
building an unsupervised document (sentence) embedding from pre-trained word
embeddings. In our experiments on 9 benchmark text classification datasets and
22 textual similarity tasks, the proposed technique consistently matches or
outperforms state-of-the-art techniques, with significantly higher accuracy on
problems of short length.
On the Implicit Assumptions of GANs
Authors: Ke Li, Jitendra Malik
Date: 2018-11-29 00:00:00

Generative adversarial nets (GANs) have generated a lot of excitement.
Despite their popularity, they exhibit a number of well-documented issues in
practice, which apparently contradict theoretical guarantees. A number of
enlightening papers have pointed out that these issues arise from unjustified
assumptions that are commonly made, but the message seems to have been lost
amid the optimism of recent years. We believe the identified problems deserve
more attention, and highlight the implications on both the properties of GANs
and the trajectory of research on probabilistic models. We recently proposed an
alternative method that sidesteps these problems.
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks
Authors: Akhilan Boopathy, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel
Date: 2018-11-29 00:00:00

Verifying robustness of neural network classifiers has attracted great
interests and attention due to the success of deep neural networks and their
unexpected vulnerability to adversarial perturbations. Although finding minimum
adversarial distortion of neural networks (with ReLU activations) has been
shown to be an NP-complete problem, obtaining a non-trivial lower bound of
minimum distortion as a provable robustness guarantee is possible. However,
most previous works only focused on simple fully-connected layers (multilayer
perceptrons) and were limited to ReLU activations. This motivates us to propose
a general and efficient framework, CNN-Cert, that is capable of certifying
robustness on general convolutional neural networks. Our framework is general
-- we can handle various architectures including convolutional layers,
max-pooling layers, batch normalization layer, residual blocks, as well as
general activation functions; our approach is efficient -- by exploiting the
special structure of convolutional layers, we achieve up to 17 and 11 times of
speed-up compared to the state-of-the-art certification algorithms (e.g.
Fast-Lin, CROWN) and 366 times of speed-up compared to the dual-LP approach
while our algorithm obtains similar or even better verification bounds. In
addition, CNN-Cert generalizes state-of-the-art algorithms e.g. Fast-Lin and
CROWN. We demonstrate by extensive experiments that our method outperforms
state-of-the-art lower-bound-based certification algorithms in terms of both
bound quality and speed.
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling
Authors: Josue Nassar, Scott W. Linderman, Monica Bugallo, Il Memming Park
Date: 2018-11-29 00:00:00
Proceeding: ICLR 2019 5
Tasks: Data Augmentation

Many real-world systems studied are governed by complex, nonlinear dynamics. By modeling these dynamics, we can gain insight into how these systems work, make predictions about how they will behave, and develop strategies for controlling them. While there are many methods for modeling nonlinear dynamical systems, existing techniques face a trade off between offering interpretable descriptions and making accurate predictions. Here, we develop a class of models that aims to achieve both simultaneously, smoothly interpolating between simple descriptions and more complex, yet also more accurate models. Our probabilistic model achieves this multi-scale property through a hierarchy of locally linear dynamics that jointly approximate global nonlinear dynamics. We call it the tree-structured recurrent switching linear dynamical system. To fit this model, we present a fully-Bayesian sampling procedure using Polya-Gamma data augmentation to allow for fast and conjugate Gibbs sampling. Through a variety of synthetic and real examples, we show how these models outperform existing methods in both interpretability and predictive capability.
Diverse Image Synthesis from Semantic Layouts via Conditional IMLE
Authors: Ke Li, Tianhao Zhang, Jitendra Malik
Date: 2018-11-29 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Generation, Semantic Segmentation

Most existing methods for conditional image synthesis are only able to generate a single plausible image for any given input, or at best a fixed number of plausible images. In this paper, we focus on the problem of generating images from semantic segmentation maps and present a simple new method that can generate an arbitrary number of images with diverse appearance for the same semantic layout. Unlike most existing approaches which adopt the GAN framework, our method is based on the recently introduced Implicit Maximum Likelihood Estimation (IMLE) framework. Compared to the leading approach, our method is able to generate more diverse images while producing fewer artifacts despite using the same architecture. The learned latent space also has sensible structure despite the lack of supervision that encourages such behaviour. Videos and code are available at https://people.eecs.berkeley.edu/~ke.li/projects/imle/scene_layouts/.
Sym-parameterized Dynamic Inference for Mixed-Domain Image Translation
Authors: Simyung Chang, Seonguk Park, John Yang, Nojun Kwak
Date: 2018-11-29 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image-to-Image Translation, Translation

Recent advances in image-to-image translation have led to some ways to generate multiple domain images through a single network. However, there is still a limit in creating an image of a target domain without a dataset on it. We propose a method that expands the concept of `multi-domain' from data to the loss area and learns the combined characteristics of each domain to dynamically infer translations of images in mixed domains. First, we introduce Sym-parameter and its learning method for variously mixed losses while synchronizing them with input conditions. Then, we propose Sym-parameterized Generative Network (SGN) which is empirically confirmed of learning mixed characteristics of various data and losses, and translating images to any mixed-domain without ground truths, such as 30% Van Gogh and 20% Monet and 40% snowy.
Smoothed Analysis in Unsupervised Learning via Decoupling
Authors: Aditya Bhaskara, Aidao Chen, Aidan Perreault, Aravindan Vijayaraghavan
Date: 2018-11-29 00:00:00

Smoothed analysis is a powerful paradigm in overcoming worst-case
intractability in unsupervised learning and high-dimensional data analysis.
While polynomial time smoothed analysis guarantees have been obtained for
worst-case intractable problems like tensor decompositions and learning
mixtures of Gaussians, such guarantees have been hard to obtain for several
other important problems in unsupervised learning. A core technical challenge
in analyzing algorithms is obtaining lower bounds on the least singular value
for random matrix ensembles with dependent entries, that are given by
low-degree polynomials of a few base underlying random variables.
  In this work, we address this challenge by obtaining high-confidence lower
bounds on the least singular value of new classes of structured random matrix
ensembles of the above kind. We then use these bounds to design algorithms with
polynomial time smoothed analysis guarantees for the following three important
problems in unsupervised learning:
  1. Robust subspace recovery, when the fraction $\alpha$ of inliers in the
d-dimensional subspace $T \subset \mathbb{R}^n$ is at least $\alpha >
(d/n)^\ell$ for any constant integer $\ell>0$. This contrasts with the known
worst-case intractability when $\alpha< d/n$, and the previous smoothed
analysis result which needed $\alpha > d/n$ (Hardt and Moitra, 2013).
  2. Learning overcomplete hidden markov models, where the size of the state
space is any polynomial in the dimension of the observations. This gives the
first polynomial time guarantees for learning overcomplete HMMs in a smoothed
analysis model.
  3. Higher order tensor decompositions, where we generalize the so-called
FOOBI algorithm of Cardoso to find order-$\ell$ rank-one tensors in a subspace.
This allows us to obtain polynomially robust decomposition algorithms for
$2\ell$'th order tensors with rank $O(n^{\ell})$.
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Authors: Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
Date: 2018-11-29 00:00:00
Proceeding: ICML 2019 6
Tasks: Disentanglement, Representation Learning

The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. In this paper, we provide a sober look at recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train more than 12000 models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on seven different data sets. We observe that while the different methods successfully enforce properties ``encouraged'' by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision. Furthermore, increased disentanglement does not seem to lead to a decreased sample complexity of learning for downstream tasks. Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets.
Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments
Authors: Howard Chen, Alane Suhr, Dipendra Misra, Noah Snavely, Yoav Artzi
Date: 2018-11-29 00:00:00
Proceeding: CVPR 2019 6
Tasks: Position, Spatial Reasoning, Vision and Language Navigation

We study the problem of jointly reasoning about language and vision through a navigation and spatial reasoning task. We introduce the Touchdown task and dataset, where an agent must first follow navigation instructions in a real-life visual urban environment, and then identify a location described in natural language to find a hidden object at the goal position. The data contains 9,326 examples of English instructions and spatial descriptions paired with demonstrations. Empirical analysis shows the data presents an open challenge to existing methods, and qualitative linguistic analysis shows that the data displays richer use of spatial reasoning compared to related resources.
Evaluation of Complex-Valued Neural Networks on Real-Valued Classification Tasks
Authors: Nils Mönning, Suresh Manandhar
Date: 2018-11-29 00:00:00
Tasks: General Classification

Complex-valued neural networks are not a new concept, however, the use of
real-valued models has often been favoured over complex-valued models due to
difficulties in training and performance. When comparing real-valued versus
complex-valued neural networks, existing literature often ignores the number of
parameters, resulting in comparisons of neural networks with vastly different
sizes. We find that when real and complex neural networks of similar capacity
are compared, complex models perform equal to or slightly worse than
real-valued models for a range of real-valued classification tasks. The use of
complex numbers allows neural networks to handle noise on the complex plane.
When classifying real-valued data with a complex-valued neural network, the
imaginary parts of the weights follow their real parts. This behaviour is
indicative for a task that does not require a complex-valued model. We further
investigated this in a synthetic classification task. We can transfer many
activation functions from the real to the complex domain using different
strategies. The weight initialisation of complex neural networks, however,
remains a significant problem.
A Multiclass Multiple Instance Learning Method with Exact Likelihood
Authors: Xi-Lin Li
Date: 2018-11-29 00:00:00
Tasks: General Classification, Multiple Instance Learning, object-detection, Object Detection

We study a multiclass multiple instance learning (MIL) problem where the
labels only suggest whether any instance of a class exists or does not exist in
a training sample or example. No further information, e.g., the number of
instances of each class, relative locations or orders of all instances in a
training sample, is exploited. Such a weak supervision learning problem can be
exactly solved by maximizing the model likelihood fitting given observations,
and finds applications to tasks like multiple object detection and localization
for image understanding. We discuss its relationship to the classic
classification problem, the traditional MIL, and connectionist temporal
classification (CTC). We use image recognition as the example task to develop
our method, although it is applicable to data with higher or lower dimensions
without much modification. Experimental results show that our method can be
used to learn all convolutional neural networks for solving real-world multiple
object detection and localization tasks with weak annotations, e.g.,
transcribing house number sequences from the Google street view imagery
dataset.
Graph Multiview Canonical Correlation Analysis
Authors: Jia Chen, Gang Wang, Georgios B. Giannakis
Date: 2018-11-29 00:00:00

Multiview canonical correlation analysis (MCCA) seeks latent low-dimensional
representations encountered with multiview data of shared entities (a.k.a.
common sources). However, existing MCCA approaches do not exploit the geometry
of the common sources, which may be available \emph{a priori}, or can be
constructed using certain domain knowledge. This prior information about the
common sources can be encoded by a graph, and be invoked as a regularizer to
enrich the maximum variance MCCA framework. In this context, the present
paper's novel graph-regularized (G) MCCA approach minimizes the distance
between the wanted canonical variables and the common low-dimensional
representations, while accounting for graph-induced knowledge of the common
sources. Relying on a function capturing the extent low-dimensional
representations of the multiple views are similar, a generalization bound of
GMCCA is established based on Rademacher's complexity. Tailored for setups
where the number of data pairs is smaller than the data vector dimensions, a
graph-regularized dual MCCA approach is also developed. To further deal with
nonlinearities present in the data, graph-regularized kernel MCCA variants are
put forward too. Interestingly, solutions of the graph-regularized linear,
dual, and kernel MCCA, are all provided in terms of generalized eigenvalue
decomposition. Several corroborating numerical tests using real datasets are
provided to showcase the merits of the graph-regularized MCCA variants relative
to several competing alternatives including MCCA, Laplacian-regularized MCCA,
and (graph-regularized) PCA.
Robust Bayesian Cluster Enumeration Based on the $t$ Distribution
Authors: Freweyni K. Teklehaymanot, Michael Muma, Abdelhak M. Zoubir
Date: 2018-11-29 00:00:00
Tasks: Clustering

A major challenge in cluster analysis is that the number of data clusters is mostly unknown and it must be estimated prior to clustering the observed data. In real-world applications, the observed data is often subject to heavy tailed noise and outliers which obscure the true underlying structure of the data. Consequently, estimating the number of clusters becomes challenging. To this end, we derive a robust cluster enumeration criterion by formulating the problem of estimating the number of clusters as maximization of the posterior probability of multivariate $t_\nu$ distributed candidate models. We utilize Bayes' theorem and asymptotic approximations to come up with a robust criterion that possesses a closed-form expression. Further, we refine the derivation and provide a robust cluster enumeration criterion for data sets with finite sample size. The robust criteria require an estimate of cluster parameters for each candidate model as an input. Hence, we propose a two-step cluster enumeration algorithm that uses the expectation maximization algorithm to partition the data and estimate cluster parameters prior to the calculation of one of the robust criteria. The performance of the proposed algorithm is tested and compared to existing cluster enumeration methods using numerical and real data experiments.
Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting
Authors: Artur Bekasov, Iain Murray
Date: 2018-11-29 00:00:00
Tasks: Bayesian Inference

Modern deep neural network models suffer from adversarial examples, i.e.
confidently misclassified points in the input space. It has been shown that
Bayesian neural networks are a promising approach for detecting adversarial
points, but careful analysis is problematic due to the complexity of these
models. Recently Gilmer et al. (2018) introduced adversarial spheres, a toy
set-up that simplifies both practical and theoretical analysis of the problem.
In this work, we use the adversarial sphere set-up to understand the properties
of approximate Bayesian inference methods for a linear model in a noiseless
setting. We compare predictions of Bayesian and non-Bayesian methods,
showcasing the advantages of the former, although revealing open challenges for
deep learning applications.
InverseRenderNet: Learning single image inverse rendering
Authors: Ye Yu, William A. P. Smith
Date: 2018-11-29 00:00:00
Proceeding: CVPR 2019 6
Tasks: Inverse Rendering

We show how to train a fully convolutional neural network to perform inverse
rendering from a single, uncontrolled image. The network takes an RGB image as
input, regresses albedo and normal maps from which we compute lighting
coefficients. Our network is trained using large uncontrolled image collections
without ground truth. By incorporating a differentiable renderer, our network
can learn from self-supervision. Since the problem is ill-posed we introduce
additional supervision: 1. We learn a statistical natural illumination prior,
2. Our key insight is to perform offline multiview stereo (MVS) on images
containing rich illumination variation. From the MVS pose and depth maps, we
can cross project between overlapping views such that Siamese training can be
used to ensure consistent estimation of photometric invariants. MVS depth also
provides direct coarse supervision for normal map estimation. We believe this
is the first attempt to use MVS supervision for learning inverse rendering.
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer Vision
Authors: Mohsen Joneidi, Alireza Zaeemzadeh, Nazanin Rahnavard, Mubarak Shah
Date: 2018-11-29 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Recognition, Active Learning, Computational Efficiency, Data Summarization, Generative Adversarial Network, Temporal Action Localization, Video Summarization

The goal of data selection is to capture the most structural information from
a set of data. This paper presents a fast and accurate data selection method,
in which the selected samples are optimized to span the subspace of all data.
We propose a new selection algorithm, referred to as iterative projection and
matching (IPM), with linear complexity w.r.t. the number of data, and without
any parameter to be tuned. In our algorithm, at each iteration, the maximum
information from the structure of the data is captured by one selected sample,
and the captured information is neglected in the next iterations by projection
on the null-space of previously selected samples. The computational efficiency
and the selection accuracy of our proposed algorithm outperform those of the
conventional methods. Furthermore, the superiority of the proposed algorithm is
shown on active learning for video action recognition dataset on UCF-101;
learning using representatives on ImageNet; training a generative adversarial
network (GAN) to generate multi-view images from a single-view input on CMU
Multi-PIE dataset; and video summarization on UTE Egocentric dataset.