pwc-archive/papers-with-abstracts huggingface dataset:
Towards a Robust Aerial Cinematography Platform: Localizing and Tracking Moving Targets in Unstructured Environments
Authors: Rogerio Bonatti, Cherie Ho, Wenshan Wang, Sanjiban Choudhury, Sebastian Scherer
Date: 2019-04-04 00:00:00
Tasks: Pose Estimation

The use of drones for aerial cinematography has revolutionized several applications and industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely controlling a drone while filming a moving target usually requires multiple expert human operators; hence the need for an autonomous cinematographer. Current approaches have severe real-life limitations such as requiring fully scripted scenes, high-precision motion-capture systems or GPS tags to localize targets, and prior maps of the environment to avoid obstacles and plan for occlusion. In this work, we overcome such limitations and propose a complete system for aerial cinematography that combines: (1) a vision-based algorithm for target localization; (2) a real-time incremental 3D signed-distance map algorithm for occlusion and safety computation; and (3) a real-time camera motion planner that optimizes smoothness, collisions, occlusions and artistic guidelines. We evaluate robustness and real-time performance in series of field experiments and simulations by tracking dynamic targets moving through unknown, unstructured environments. Finally, we verify that despite removing previous limitations, our system achieves state-of-the-art performance. Videos of the system in action can be seen at https://youtu.be/ZE9MnCVmumc
Comparison Network for One-Shot Conditional Object Detection
Authors: Tengfei Zhang, Yue Zhang, Xian Sun, Hao Sun, Menglong Yan, Xue Yang, Kun fu
Date: 2019-04-04 00:00:00
Tasks: Object, object-detection, Object Detection

The current advances in object detection depend on large-scale datasets to get good performance. However, there may not always be sufficient samples in many scenarios, which leads to the research on few-shot detection as well as its extreme variation one-shot detection. In this paper, the one-shot detection has been formulated as a conditional probability problem. With this insight, a novel one-shot conditional object detection (OSCD) framework, referred as Comparison Network (ComparisonNet), has been proposed. Specifically, query and target image features are extracted through a Siamese network as mapped metrics of marginal probabilities. A two-stage detector for OSCD is introduced to compare the extracted query and target features with the learnable metric to approach the optimized non-linear conditional probability. Once trained, ComparisonNet can detect objects of both seen and unseen classes without further training, which also has the advantages including class-agnostic, training-free for unseen classes, and without catastrophic forgetting. Experiments show that the proposed approach achieves state-of-the-art performance on the proposed datasets of Fashion-MNIST and PASCAL VOC.
Approximation Rates for Neural Networks with General Activation Functions
Authors: Jonathan W. Siegel, Jinchao Xu
Date: 2019-04-04 00:00:00

We prove some new results concerning the approximation rate of neural networks with general activation functions. Our first result concerns the rate of approximation of a two layer neural network with a polynomially-decaying non-sigmoidal activation function. We extend the dimension independent approximation rates previously obtained to this new class of activation functions. Our second result gives a weaker, but still dimension independent, approximation rate for a larger class of activation functions, removing the polynomial decay assumption. This result applies to any bounded, integrable activation function. Finally, we show that a stratified sampling approach can be used to improve the approximation rate for polynomially decaying activation functions under mild additional assumptions.
On functions computed on trees
Authors: Roozbeh Farhoodi, Khashayar Filom, Ilenna Simone Jones, Konrad Paul Kording
Date: 2019-04-04 00:00:00

Any function can be constructed using a hierarchy of simpler functions through compositions. Such a hierarchy can be characterized by a binary rooted tree. Each node of this tree is associated with a function which takes as inputs two numbers from its children and produces one output. Since thinking about functions in terms of computation graphs is getting popular we may want to know which functions can be implemented on a given tree. Here, we describe a set of necessary constraints in the form of a system of non-linear partial differential equations that must be satisfied. Moreover, we prove that these conditions are sufficient in both contexts of analytic and bit-valued functions. In the latter case, we explicitly enumerate discrete functions and observe that there are relatively few. Our point of view allows us to compare different neural network architectures in regard to their function spaces. Our work connects the structure of computation graphs with the functions they can implement and has potential applications to neuroscience and computer science.
Improved Inference via Deep Input Transfer
Authors: Saied Asgari Taghanaki, Kumar Abhishek, Ghassan Hamarneh
Date: 2019-04-04 00:00:00
Tasks: Image Segmentation, Lesion Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Skin Lesion Segmentation

Although numerous improvements have been made in the field of image segmentation using convolutional neural networks, the majority of these improvements rely on training with larger datasets, model architecture modifications, novel loss functions, and better optimizers. In this paper, we propose a new segmentation performance boosting paradigm that relies on optimally modifying the network's input instead of the network itself. In particular, we leverage the gradients of a trained segmentation network with respect to the input to transfer it to a space where the segmentation accuracy improves. We test the proposed method on three publicly available medical image segmentation datasets: the ISIC 2017 Skin Lesion Segmentation dataset, the Shenzhen Chest X-Ray dataset, and the CVC-ColonDB dataset, for which our method achieves improvements of 5.8%, 0.5%, and 4.8% in the average Dice scores, respectively.
A Simple Joint Model for Improved Contextual Neural Lemmatization
Authors: Chaitanya Malaviya, Shijie Wu, Ryan Cotterell
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: LEMMA, Lemmatization, Morphological Tagging

English verbs have multiple forms. For instance, talk may also appear as talks, talked or talking, depending on the context. The NLP task of lemmatization seeks to map these diverse forms back to a canonical one, known as the lemma. We present a simple joint neural model for lemmatization and morphological tagging that achieves state-of-the-art results on 20 languages from the Universal Dependencies corpora. Our paper describes the model in addition to training and decoding procedures. Error analysis indicates that joint morphological tagging and lemmatization is especially helpful in low-resource lemmatization and languages that display a larger degree of morphological complexity. Code and pre-trained models are available at https://sigmorphon.github.io/sharedtasks/2019/task2/.
Robust Deep Gaussian Processes
Authors: Jeremias Knoblauch
Date: 2019-04-04 00:00:00
Tasks: Gaussian Processes, Uncertainty Quantification, Variational Inference

This report provides an in-depth overview over the implications and novelty Generalized Variational Inference (GVI) (Knoblauch et al., 2019) brings to Deep Gaussian Processes (DGPs) (Damianou & Lawrence, 2013). Specifically, robustness to model misspecification as well as principled alternatives for uncertainty quantification are motivated with an information-geometric view. These modifications have clear interpretations and can be implemented in less than 100 lines of Python code. Most importantly, the corresponding empirical results show that DGPs can greatly benefit from the presented enhancements.
A Training-free, One-shot Detection Framework For Geospatial Objects In Remote Sensing Images
Authors: Tengfei Zhang, Yue Zhang, Xian Sun, Menglong Yan, Yaoling Wang, Kun fu
Date: 2019-04-04 00:00:00
Tasks: Object, object-detection, Object Detection

Deep learning based object detection has achieved great success. However,
these supervised learning methods are data-hungry and time-consuming. This
restriction makes them unsuitable for limited data and urgent tasks, especially
in the applications of remote sensing. Inspired by the ability of humans to
quickly learn new visual concepts from very few examples, we propose a
training-free, one-shot geospatial object detection framework for remote
sensing images. It consists of (1) a feature extractor with remote sensing
domain knowledge, (2) a multi-level feature fusion method, (3) a novel
similarity metric method, and (4) a 2-stage object detection pipeline.
Experiments on sewage treatment plant and airport detections show that proposed
method has achieved a certain effect. Our method can serve as a baseline for
training-free, one-shot geospatial object detection.
Cost-Sensitive Feature Selection by Optimizing F-Measures
Authors: Meng Liu, Chang Xu, Yong Luo, Chao Xu, Yonggang Wen, DaCheng Tao
Date: 2019-04-04 00:00:00
Tasks: feature selection

Feature selection is beneficial for improving the performance of general
machine learning tasks by extracting an informative subset from the
high-dimensional features. Conventional feature selection methods usually
ignore the class imbalance problem, thus the selected features will be biased
towards the majority class. Considering that F-measure is a more reasonable
performance measure than accuracy for imbalanced data, this paper presents an
effective feature selection algorithm that explores the class imbalance issue
by optimizing F-measures. Since F-measure optimization can be decomposed into a
series of cost-sensitive classification problems, we investigate the
cost-sensitive feature selection by generating and assigning different costs to
each class with rigorous theory guidance. After solving a series of
cost-sensitive feature selection problems, features corresponding to the best
F-measure will be selected. In this way, the selected features will fully
represent the properties of all classes. Experimental results on popular
benchmarks and challenging real-world data sets demonstrate the significance of
cost-sensitive feature selection for the imbalanced data setting and validate
the effectiveness of the proposed method.
Gated-GAN: Adversarial Gated Networks for Multi-Collection Style Transfer
Authors: Xinyuan Chen, Chang Xu, Xiaokang Yang, Li Song, DaCheng Tao
Date: 2019-04-04 00:00:00
Tasks: Decoder, Style Transfer

Style transfer describes the rendering of an image semantic content as
different artistic styles. Recently, generative adversarial networks (GANs)
have emerged as an effective approach in style transfer by adversarially
training the generator to synthesize convincing counterfeits. However,
traditional GAN suffers from the mode collapse issue, resulting in unstable
training and making style transfer quality difficult to guarantee. In addition,
the GAN generator is only compatible with one style, so a series of GANs must
be trained to provide users with choices to transfer more than one kind of
style. In this paper, we focus on tackling these challenges and limitations to
improve style transfer. We propose adversarial gated networks (Gated GAN) to
transfer multiple styles in a single model. The generative networks have three
modules: an encoder, a gated transformer, and a decoder. Different styles can
be achieved by passing input images through different branches of the gated
transformer. To stabilize training, the encoder and decoder are combined as an
autoencoder to reconstruct the input images. The discriminative networks are
used to distinguish whether the input image is a stylized or genuine image. An
auxiliary classifier is used to recognize the style categories of transferred
images, thereby helping the generative networks generate images in multiple
styles. In addition, Gated GAN makes it possible to explore a new style by
investigating styles learned from artists or genres. Our extensive experiments
demonstrate the stability and effectiveness of the proposed model for
multistyle transfer.
Evaluating Style Transfer for Text
Authors: Remi Mir, Bjarke Felbo, Nick Obradovich, Iyad Rahwan
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: Style Transfer

Research in the area of style transfer for text is currently bottlenecked by
a lack of standard evaluation practices. This paper aims to alleviate this
issue by experimentally identifying best practices with a Yelp sentiment
dataset. We specify three aspects of interest (style transfer intensity,
content preservation, and naturalness) and show how to obtain more reliable
measures of them from human evaluation than in previous work. We propose a set
of metrics for automated evaluation and demonstrate that they are more strongly
correlated and in agreement with human judgment: direction-corrected Earth
Mover's Distance, Word Mover's Distance on style-masked texts, and adversarial
classification for the respective aspects. We also show that the three examined
models exhibit tradeoffs between aspects of interest, demonstrating the
importance of evaluating style transfer models at specific points of their
tradeoff plots. We release software with our evaluation metrics to facilitate
research.
Generative Adversarial Networks for text using word2vec intermediaries
Authors: Akshay Budhkar, Krishnapriya Vishnubhotla, Safwan Hossain, Frank Rudzicz
Date: 2019-04-04 00:00:00
Proceeding: WS 2019 8
Tasks: Word Embeddings

Generative adversarial networks (GANs) have shown considerable success,
especially in the realistic generation of images. In this work, we apply
similar techniques for the generation of text. We propose a novel approach to
handle the discrete nature of text, during training, using word embeddings. Our
method is agnostic to vocabulary size and achieves competitive results relative
to methods with various discrete gradient estimators.
Answer-based Adversarial Training for Generating Clarification Questions
Authors: Sudha Rao, Hal Daumé III
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: Generative Adversarial Network, Retrieval, Specificity

We present an approach for generating clarification questions with the goal
of eliciting new information that would make the given textual context more
complete. We propose that modeling hypothetical answers (to clarification
questions) as latent variables can guide our approach into generating more
useful clarification questions. We develop a Generative Adversarial Network
(GAN) where the generator is a sequence-to-sequence model and the discriminator
is a utility function that models the value of updating the context with the
answer to the clarification question. We evaluate on two datasets, using both
automatic metrics and human judgments of usefulness, specificity and relevance,
showing that our approach outperforms both a retrieval-based model and
ablations that exclude the utility model and the adversarial training.
DAGCN: Dual Attention Graph Convolutional Networks
Authors: Fengwen Chen, Shirui Pan, Jing Jiang, Huan Huo, Guodong Long
Date: 2019-04-04 00:00:00
Tasks: General Classification, Graph Classification, Graph Embedding

Graph convolutional networks (GCNs) have recently become one of the most
powerful tools for graph analytics tasks in numerous applications, ranging from
social networks and natural language processing to bioinformatics and
chemoinformatics, thanks to their ability to capture the complex relationships
between concepts. At present, the vast majority of GCNs use a neighborhood
aggregation framework to learn a continuous and compact vector, then performing
a pooling operation to generalize graph embedding for the classification task.
These approaches have two disadvantages in the graph classification task:
(1)when only the largest sub-graph structure ($k$-hop neighbor) is used for
neighborhood aggregation, a large amount of early-stage information is lost
during the graph convolution step; (2) simple average/sum pooling or max
pooling utilized, which loses the characteristics of each node and the topology
between nodes. In this paper, we propose a novel framework called, dual
attention graph convolutional networks (DAGCN) to address these problems. DAGCN
automatically learns the importance of neighbors at different hops using a
novel attention graph convolution layer, and then employs a second attention
component, a self-attention pooling layer, to generalize the graph
representation from the various aspects of a matrix graph embedding. The dual
attention network is trained in an end-to-end manner for the graph
classification task. We compare our model with state-of-the-art graph kernels
and other deep learning methods. The experimental results show that our
framework not only outperforms other baselines but also achieves a better rate
of convergence.
Sublinear quantum algorithms for training linear and kernel-based classifiers
Authors: Tongyang Li, Shouvanik Chakrabarti, Xiaodi Wu
Date: 2019-04-04 00:00:00
Tasks: Quantization

We investigate quantum algorithms for classification, a fundamental problem
in machine learning, with provable guarantees. Given $n$ $d$-dimensional data
points, the state-of-the-art (and optimal) classical algorithm for training
classifiers with constant margin runs in $\tilde{O}(n+d)$ time. We design
sublinear quantum algorithms for the same task running in $\tilde{O}(\sqrt{n}
+\sqrt{d})$ time, a quadratic improvement in both $n$ and $d$. Moreover, our
algorithms use the standard quantization of the classical input and generate
the same classical output, suggesting minimal overheads when used as
subroutines for end-to-end applications. We also demonstrate a tight lower
bound (up to poly-log factors) and discuss the possibility of implementation on
near-term quantum machines. As a side result, we also give sublinear quantum
algorithms for approximating the equilibria of $n$-dimensional matrix zero-sum
games with optimal complexity $\tilde{\Theta}(\sqrt{n})$.
Continuous Direct Sparse Visual Odometry from RGB-D Images
Authors: Maani Ghaffari, William Clark, Anthony Bloch, Ryan M. Eustice, Jessy W. Grizzle
Date: 2019-04-03 00:00:00
Tasks: Simultaneous Localization and Mapping, Visual Odometry

This paper reports on a novel formulation and evaluation of visual odometry from RGB-D images. Assuming a static scene, the developed theoretical framework generalizes the widely used direct energy formulation (photometric error minimization) technique for obtaining a rigid body transformation that aligns two overlapping RGB-D images to a continuous formulation. The continuity is achieved through functional treatment of the problem and representing the process models over RGB-D images in a reproducing kernel Hilbert space; consequently, the registration is not limited to the specific image resolution and the framework is fully analytical with a closed-form derivation of the gradient. We solve the problem by maximizing the inner product between two functions defined over RGB-D images, while the continuous action of the rigid body motion Lie group is captured through the integration of the flow in the corresponding Lie algebra. Energy-based approaches have been extremely successful and the developed framework in this paper shares many of their desired properties such as the parallel structure on both CPUs and GPUs, sparsity, semi-dense tracking, avoiding explicit data association which is computationally expensive, and possible extensions to the simultaneous localization and mapping frameworks. The evaluations on experimental data and comparison with the equivalent energy-based formulation of the problem confirm the effectiveness of the proposed technique, especially, when the lack of structure and texture in the environment is evident.
Including Physics in Deep Learning -- An example from 4D seismic pressure saturation inversion
Authors: Jesper Sören Dramsch, Gustavo Corte, Hamed Amini, Colin MacBeth, Mikael Lüthje
Date: 2019-04-03 00:00:00
Tasks: BIG-bench Machine Learning

Geoscience data often have to rely on strong priors in the face of
uncertainty. Additionally, we often try to detect or model anomalous sparse
data that can appear as an outlier in machine learning models. These are
classic examples of imbalanced learning. Approaching these problems can benefit
from including prior information from physics models or transforming data to a
beneficial domain. We show an example of including physical information in the
architecture of a neural network as prior information. We go on to present
noise injection at training time to successfully transfer the network from
synthetic data to field data.
StereoDRNet: Dilated Residual Stereo Net
Authors: Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs
Date: 2019-04-03 00:00:00
Tasks: 3D Reconstruction

We propose a system that uses a convolution neural network (CNN) to estimate depth from a stereo pair followed by volumetric fusion of the predicted depth maps to produce a 3D reconstruction of a scene. Our proposed depth refinement architecture, predicts view-consistent disparity and occlusion maps that helps the fusion system to produce geometrically consistent reconstructions. We utilize 3D dilated convolutions in our proposed cost filtering network that yields better filtering while almost halving the computational cost in comparison to state of the art cost filtering architectures.For feature extraction we use the Vortex Pooling architecture. The proposed method achieves state of the art results in KITTI 2012, KITTI 2015 and ETH 3D stereo benchmarks. Finally, we demonstrate that our system is able to produce high fidelity 3D scene reconstructions that outperforms the state of the art stereo system.
Multi-task Learning for Japanese Predicate Argument Structure Analysis
Authors: Hikaru Omori, Mamoru Komachi
Date: 2019-04-03 00:00:00
Proceeding: NAACL 2019 6
Tasks: Multi-Task Learning

An event-noun is a noun that has an argument structure similar to a
predicate. Recent works, including those considered state-of-the-art, ignore
event-nouns or build a single model for solving both Japanese predicate
argument structure analysis (PASA) and event-noun argument structure analysis
(ENASA). However, because there are interactions between predicates and
event-nouns, it is not sufficient to target only predicates. To address this
problem, we present a multi-task learning method for PASA and ENASA. Our
multi-task models improved the performance of both tasks compared to a
single-task model by sharing knowledge from each task. Moreover, in PASA, our
models achieved state-of-the-art results in overall F1 scores on the NAIST Text
Corpus. In addition, this is the first work to employ neural networks in ENASA.