pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning for Audio Signal Processing
Authors: Hendrik Purwins, Bo Li, Tuomas Virtanen, Jan Schlüter, Shuo-Yiin Chang, Tara Sainath
Date: 2019-04-30 00:00:00
Tasks: Audio Signal Processing, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Deep Learning, Information Retrieval, Music Information Retrieval, Retrieval, speech-recognition, Speech Recognition

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.
Ensemble Distribution Distillation
Authors: Andrey Malinin, Bruno Mlodozeniec, Mark Gales
Date: 2019-04-30 00:00:00
Proceeding: ICLR 2020 1
Tasks: Diversity

Ensembles of models often yield improvements in system performance. These ensemble approaches have also been empirically shown to yield robust measures of uncertainty, and are capable of distinguishing between different \emph{forms} of uncertainty. However, ensembles come at a computational and memory cost which may be prohibitive for many applications. There has been significant work done on the distillation of an ensemble into a single model. Such approaches decrease computational cost and allow a single model to achieve an accuracy comparable to that of an ensemble. However, information about the \emph{diversity} of the ensemble, which can yield estimates of different forms of uncertainty, is lost. This work considers the novel task of \emph{Ensemble Distribution Distillation} (EnD$^2$) --- distilling the distribution of the predictions from an ensemble, rather than just the average prediction, into a single model. EnD$^2$ enables a single model to retain both the improved classification performance of ensemble distillation as well as information about the diversity of the ensemble, which is useful for uncertainty estimation. A solution for EnD$^2$ based on Prior Networks, a class of models which allow a single neural network to explicitly model a distribution over output distributions, is proposed in this work. The properties of EnD$^2$ are investigated on both an artificial dataset, and on the CIFAR-10, CIFAR-100 and TinyImageNet datasets, where it is shown that EnD$^2$ can approach the classification performance of an ensemble, and outperforms both standard DNNs and Ensemble Distillation on the tasks of misclassification and out-of-distribution input detection.
On the Use of ArXiv as a Dataset
Authors: Colin B. Clement, Matthew Bierbaum, Kevin P. O'Keeffe, Alexander A. Alemi
Date: 2019-04-30 00:00:00
Tasks: Articles, Author Attribution, Benchmarking, Information Retrieval, Knowledge Graphs, Language Modelling, Link Prediction, Recommendation Systems, Semantic Segmentation, Structured Prediction, Text Classification, Text Clustering

The arXiv has collected 1.5 million pre-print articles over 28 years, hosting
literature from scientific fields including Physics, Mathematics, and Computer
Science. Each pre-print features text, figures, authors, citations, categories,
and other metadata. These rich, multi-modal features, combined with the natural
graph structure---created by citation, affiliation, and co-authorship---makes
the arXiv an exciting candidate for benchmarking next-generation models. Here
we take the first necessary steps toward this goal, by providing a pipeline
which standardizes and simplifies access to the arXiv's publicly available
data. We use this pipeline to extract and analyze a 6.7 million edge citation
graph, with an 11 billion word corpus of full-text research articles. We
present some baseline classification results, and motivate application of more
exciting generative graph models.
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Authors: Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan
Date: 2019-04-30 00:00:00
Tasks: Node Classification, Node Classification on Non-Homophilic (Heterophilic) Graphs

Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mixing relationships. To address this weakness, we propose a new model, MixHop, that can learn these relationships, including difference operators, by repeatedly mixing feature representations of neighbors at various distances. Mixhop requires no additional memory or computational complexity, and outperforms on challenging baselines. In addition, we propose sparsity regularization that allows us to visualize how the network prioritizes neighborhood information across different graph datasets. Our analysis of the learned architectures reveals that neighborhood mixing varies per datasets.
Predicting How to Distribute Work Between Algorithms and Humans to Segment an Image Batch
Authors: Danna Gurari, Yinan Zhao, Suyog Dutt Jain, Margrit Betke, Kristen Grauman
Date: 2019-04-30 00:00:00
Tasks: Semantic Segmentation

Foreground object segmentation is a critical step for many image analysis
tasks. While automated methods can produce high-quality results, their failures
disappoint users in need of practical solutions. We propose a resource
allocation framework for predicting how best to allocate a fixed budget of
human annotation effort in order to collect higher quality segmentations for a
given batch of images and automated methods. The framework is based on a
prediction module that estimates the quality of given algorithm-drawn
segmentations. We demonstrate the value of the framework for two novel tasks
related to predicting how to distribute annotation efforts between algorithms
and humans. Specifically, we develop two systems that automatically decide, for
a batch of images, when to recruit humans versus computers to create 1) coarse
segmentations required to initialize segmentation tools and 2) final,
fine-grained segmentations. Experiments demonstrate the advantage of relying on
a mix of human and computer efforts over relying on either resource alone for
segmenting objects in images coming from three diverse modalities (visible,
phase contrast microscopy, and fluorescence microscopy).
Personalized Ranking in eCommerce Search
Authors: Grigor Aslanyan, Aritra Mandal, Prathyusha Senthil Kumar, Amit Jaiswal, Manojkumar Rangasamy Kannadasan
Date: 2019-04-30 00:00:00
Tasks: Re-Ranking, Semantic Similarity, Semantic Textual Similarity

We address the problem of personalization in the context of eCommerce search.
Specifically, we develop personalization ranking features that use in-session
context to augment a generic ranker optimized for conversion and relevance. We
use a combination of latent features learned from item co-clicks in historic
sessions and content-based features that use item title and price.
Personalization in search has been discussed extensively in the existing
literature. The novelty of our work is combining and comparing content-based
and content-agnostic features and showing that they complement each other to
result in a significant improvement of the ranker. Moreover, our technique does
not require an explicit re-ranking step, does not rely on learning user
profiles from long term search behavior, and does not involve complex modeling
of query-item-user features. Our approach captures item co-click propensity
using lightweight item embeddings. We experimentally show that our technique
significantly outperforms a generic ranker in terms of Mean Reciprocal Rank
(MRR). We also provide anecdotal evidence for the semantic similarity captured
by the item embeddings on the eBay search engine.
Attentive Spatio-Temporal Representation Learning for Diving Classification
Authors: Gagan Kanojia, Sudhakar Kumawat, Shanmuganathan Raman
Date: 2019-04-30 00:00:00
Tasks: Classification, General Classification, Representation Learning

Competitive diving is a well recognized aquatic sport in which a person dives
from a platform or a springboard into the water. Based on the acrobatics
performed during the dive, diving is classified into a finite set of action
classes which are standardized by FINA. In this work, we propose an attention
guided LSTM-based neural network architecture for the task of diving
classification. The network takes the frames of a diving video as input and
determines its class. We evaluate the performance of the proposed model on a
recently introduced competitive diving dataset, Diving48. It contains over
18000 video clips which covers 48 classes of diving. The proposed model
outperforms the classification accuracy of the state-of-the-art models in both
2D and 3D frameworks by 11.54% and 4.24%, respectively. We show that the
network is able to localize the diver in the video frames during the dive
without being trained with such a supervision.
Appearance and Pose-Conditioned Human Image Generation using Deformable GANs
Authors: Aliaksandr Siarohin, Stéphane Lathuilière, Enver Sangineto, Nicu Sebe
Date: 2019-04-30 00:00:00
Tasks: Data Augmentation, Generative Adversarial Network, Image Generation, Person Re-Identification

In this paper, we address the problem of generating person images conditioned on both pose and appearance information. Specifically, given an image xa of a person and a target pose P(xb), extracted from a different image xb, we synthesize a new image of that person in pose P(xb), while preserving the visual details in xa. In order to deal with pixel-to-pixel misalignments caused by the pose differences between P(xa) and P(xb), we introduce deformable skip connections in the generator of our Generative Adversarial Network. Moreover, a nearest-neighbour loss is proposed instead of the common L1 and L2 losses in order to match the details of the generated image with the target image. Quantitative and qualitative results, using common datasets and protocols recently proposed for this task, show that our approach is competitive with respect to the state of the art. Moreover, we conduct an extensive evaluation using off-the-shell person re-identification (Re-ID) systems trained with person-generation based augmented data, which is one of the main important applications for this task. Our experiments show that our Deformable GANs can significantly boost the Re-ID accuracy and are even better than data-augmentation methods specifically trained using Re-ID losses.
Cross Domain Knowledge Learning with Dual-branch Adversarial Network for Vehicle Re-identification
Authors: Jinjia Peng, Huibing Wang, Xianping Fu
Date: 2019-04-30 00:00:00
Tasks: Domain Adaptation, Image-to-Image Translation, Vehicle Re-Identification

The widespread popularization of vehicles has facilitated all people's life
during the last decades. However, the emergence of a large number of vehicles
poses the critical but challenging problem of vehicle re-identification (reID).
Till now, for most vehicle reID algorithms, both the training and testing
processes are conducted on the same annotated datasets under supervision.
However, even a well-trained model will still cause fateful performance drop
due to the severe domain bias between the trained dataset and the real-world
scenes.
  To address this problem, this paper proposes a domain adaptation framework
for vehicle reID (DAVR), which narrows the cross-domain bias by fully
exploiting the labeled data from the source domain to adapt the target domain.
DAVR develops an image-to-image translation network named Dual-branch
Adversarial Network (DAN), which could promote the images from the source
domain (well-labeled) to learn the style of target domain (unlabeled) without
any annotation and preserve identity information from source domain. Then the
generated images are employed to train the vehicle reID model by a proposed
attention-based feature learning model with more reasonable styles. Through the
proposed framework, the well-trained reID model has better domain adaptation
ability for various scenes in real-world situations. Comprehensive experimental
results have demonstrated that our proposed DAVR can achieve excellent
performances on both VehicleID dataset and VeRi-776 dataset.
Forget the Learning Rate, Decay Loss
Authors: Jiakai Wei
Date: 2019-04-27 00:00:00
Tasks: General Classification, image-classification, Image Classification, Semantic Segmentation

In the usual deep neural network optimization process, the learning rate is
the most important hyper parameter, which greatly affects the final convergence
effect. The purpose of learning rate is to control the stepsize and gradually
reduce the impact of noise on the network. In this paper, we will use a fixed
learning rate with method of decaying loss to control the magnitude of the
update. We used Image classification, Semantic segmentation, and GANs to verify
this method. Experiments show that the loss decay strategy can greatly improve
the performance of the model
sCAKE: Semantic Connectivity Aware Keyword Extraction
Authors: Swagata Duari, Vasudha Bhatnagar
Date: 2018-11-27 00:00:00
Tasks: graph construction, Keyword Extraction

Keyword Extraction is an important task in several text analysis endeavors.
In this paper, we present a critical discussion of the issues and challenges
ingraph-based keyword extraction methods, along with comprehensive empirical
analysis. We propose a parameterless method for constructing graph of text that
captures the contextual relation between words. A novel word scoring method is
also proposed based on the connection between concepts. We demonstrate that
both proposals are individually superior to those followed by the
state-of-the-art graph-based keyword extraction algorithms. Combination of the
proposed graph construction and scoring methods leads to a novel, parameterless
keyword extraction method (sCAKE) based on semantic connectivity of words in
the document.
  Motivated by limited availability of NLP tools for several languages, we also
design and present a language-agnostic keyword extraction (LAKE) method. We
eliminate the need of NLP tools by using a statistical filter to identify
candidate keywords before constructing the graph. We show that the resulting
method is a competent solution for extracting keywords from documents
oflanguages lacking sophisticated NLP support.
Feature-Weighted Linear Stacking
Authors: Joseph Sill, Gabor Takacs, Lester Mackey, David Lin
Date: 2009-11-03 00:00:00
Tasks: Collaborative Filtering

Ensemble methods, such as stacking, are designed to boost predictive accuracy
by blending the predictions of multiple machine learning models. Recent work
has shown that the use of meta-features, additional inputs describing each
example in a dataset, can boost the performance of ensemble methods, but the
greatest reported gains have come from nonlinear procedures requiring
significant tuning and training time. Here, we present a linear technique,
Feature-Weighted Linear Stacking (FWLS), that incorporates meta-features for
improved accuracy while retaining the well-known virtues of linear regression
regarding speed, stability, and interpretability. FWLS combines model
predictions linearly using coefficients that are themselves linear functions of
meta-features. This technique was a key facet of the solution of the second
place team in the recently concluded Netflix Prize competition. Significant
increases in accuracy over standard linear stacking are demonstrated on the
Netflix Prize collaborative filtering dataset.
Recombinator-k-means: An evolutionary algorithm that exploits k-means++ for recombination
Authors: Carlo Baldassi
Date: 2019-05-01 00:00:00
Tasks: Clustering

We introduce an evolutionary algorithm called recombinator-$k$-means for optimizing the highly non-convex kmeans problem. Its defining feature is that its crossover step involves all the members of the current generation, stochastically recombining them with a repurposed variant of the $k$-means++ seeding algorithm. The recombination also uses a reweighting mechanism that realizes a progressively sharper stochastic selection policy and ensures that the population eventually coalesces into a single solution. We compare this scheme with state-of-the-art alternative, a more standard genetic algorithm with deterministic pairwise-nearest-neighbor crossover and an elitist selection policy, of which we also provide an augmented and efficient implementation. Extensive tests on large and challenging datasets (both synthetic and real-word) show that for fixed population sizes recombinator-$k$-means is generally superior in terms of the optimization objective, at the cost of a more expensive crossover step. When adjusting the population sizes of the two algorithms to match their running times, we find that for short times the (augmented) pairwise-nearest-neighbor method is always superior, while at longer times recombinator-$k$-means will match it and, on the most difficult examples, take over. We conclude that the reweighted whole-population recombination is more costly, but generally better at escaping local minima. Moreover, it is algorithmically simpler and more general (it could be applied even to $k$-medians or $k$-medoids, for example). Our implementations are publicly available at \href{https://github.com/carlobaldassi/RecombinatorKMeans.jl}{https://github.com/carlobaldassi/RecombinatorKMeans.jl}.
RetinaFace: Single-stage Dense Face Localisation in the Wild
Authors: Jiankang Deng, Jia Guo, Yuxiang Zhou, Jinke Yu, Irene Kotsia, Stefanos Zafeiriou
Date: 2019-05-02 00:00:00
Tasks: Face Detection, Face Verification, Multi-Task Learning

Face Analysis Project on MXNet
26ms Inference Time for ResNet-50: Towards Real-Time Execution of all DNNs on Smartphone
Authors: Wei Niu, Xiaolong Ma, Yanzhi Wang, Bin Ren
Date: 2019-05-02 00:00:00
Tasks: All, Model Compression

With the rapid emergence of a spectrum of high-end mobile devices, many
applications that required desktop-level computation capability formerly can
now run on these devices without any problem. However, without a careful
optimization, executing Deep Neural Networks (a key building block of the
real-time video stream processing that is the foundation of many popular
applications) is still challenging, specifically, if an extremely low latency
or high accuracy inference is needed. This work presents CADNN, a programming
framework to efficiently execute DNN on mobile devices with the help of
advanced model compression (sparsity) and a set of thorough architecture-aware
optimization. The evaluation result demonstrates that CADNN outperforms all the
state-of-the-art dense DNN execution frameworks like TensorFlow Lite and TVM.
Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines
Authors: Ben Mildenhall, Pratul P. Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, Abhishek Kar
Date: 2019-05-02 00:00:00
Tasks: Novel View Synthesis

We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration. Previous approaches either require intractably dense view sampling or provide little to no guidance for how users should sample views of a scene to reliably render high-quality novel views. Instead, we propose an algorithm for view synthesis from an irregular grid of sampled views that first expands each sampled view into a local light field via a multiplane image (MPI) scene representation, then renders novel views by blending adjacent local light fields. We extend traditional plenoptic sampling theory to derive a bound that specifies precisely how densely users should sample views of a given scene when using our algorithm. In practice, we apply this bound to capture and render views of real world scenes that achieve the perceptual quality of Nyquist rate view sampling while using up to 4000x fewer views. We demonstrate our approach's practicality with an augmented reality smartphone app that guides users to capture input images of a scene and viewers that enable realtime virtual exploration on desktop and mobile platforms.
A tutorial on recursive models for analyzing and predicting path choice behavior
Authors: Maëlle Zimmermann, Emma Frejinger
Date: 2019-05-02 00:00:00
Tasks: Discrete Choice Models, Reinforcement Learning

The problem at the heart of this tutorial consists in modeling the path choice behavior of network users. This problem has been extensively studied in transportation science, where it is known as the route choice problem. In this literature, individuals' choice of paths are typically predicted using discrete choice models. This article is a tutorial on a specific category of discrete choice models called recursive, and it makes three main contributions: First, for the purpose of assisting future research on route choice, we provide a comprehensive background on the problem, linking it to different fields including inverse optimization and inverse reinforcement learning. Second, we formally introduce the problem and the recursive modeling idea along with an overview of existing models, their properties and applications. Third, we extensively analyze illustrative examples from different angles so that a novice reader can gain intuition on the problem and the advantages provided by recursive models in comparison to path-based ones.
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
Authors: Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong
Date: 2019-05-02 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Adversarial Defense, GPU

Deep learning achieves state-of-the-art results in many tasks in computer vision and natural language processing. However, recent works have shown that deep networks can be vulnerable to adversarial perturbations, which raised a serious robustness issue of deep networks. Adversarial training, typically formulated as a robust optimization problem, is an effective way of improving the robustness of deep networks. A major drawback of existing adversarial training algorithms is the computational overhead of the generation of adversarial examples, typically far greater than that of the network training. This leads to the unbearable overall computational cost of adversarial training. In this paper, we show that adversarial training can be cast as a discrete time differential game. Through analyzing the Pontryagin's Maximal Principle (PMP) of the problem, we observe that the adversary update is only coupled with the parameters of the first layer of the network. This inspires us to restrict most of the forward and back propagation within the first layer of the network during adversary updates. This effectively reduces the total number of full forward and backward propagation to only one for each group of adversary updates. Therefore, we refer to this algorithm YOPO (You Only Propagate Once). Numerical experiments demonstrate that YOPO can achieve comparable defense accuracy with approximately 1/5 ~ 1/4 GPU time of the projected gradient descent (PGD) algorithm. Our codes are available at https://https://github.com/a1600012888/YOPO-You-Only-Propagate-Once.