pwc-archive/papers-with-abstracts huggingface dataset:
Movienet: A Movie Multilayer Network Model using Visual and Textual Semantic Cues
Authors: Youssef Mourchid, Benjamin Renoust, Olivier Roupin, Le Van, Hocine Cherifi, Mohammed El Hassouni
Date: 2019-10-18 00:00:00

Discovering content and stories in movies is one of the most important concepts in multimedia content research studies. Network models have proven to be an efficient choice for this purpose. When an audience watches a movie, they usually compare the characters and the relationships between them. For this reason, most of the models developed so far are based on social networks analysis. They focus essentially on the characters at play. By analyzing characters' interactions, we can obtain a broad picture of the narration's content. Other works have proposed to exploit semantic elements such as scenes, dialogues, etc. However, they are always captured from a single facet. Motivated by these limitations, we introduce in this work a multilayer network model to capture the narration of a movie based on its script, its subtitles, and the movie content. After introducing the model and the extraction process from the raw data, we perform a comparative analysis of the whole 6-movie cycle of the Star Wars saga. Results demonstrate the effectiveness of the proposed framework for video content representation and analysis.
Science and Technology Advance through Surprise
Authors: Feng Shi, James Evans
Date: 2019-10-18 00:00:00

Breakthrough discoveries and inventions involve unexpected combinations of contents including problems, methods, and natural entities, and also diverse contexts such as journals, subfields, and conferences. Drawing on data from tens of millions of research papers, patents, and researchers, we construct models that predict next year's content and context combinations with an AUC of 95% based on embeddings constructed from high-dimensional stochastic block models, where the improbability of new combinations itself predicts up to 50% of the likelihood that they will gain outsized citations and major awards. Most of these breakthroughs occur when problems in one field are unexpectedly solved by researchers from a distant other. These findings demonstrate the critical role of surprise in advance, and enable evaluation of scientific institutions ranging from education and peer review to awards in supporting it.
PyTorchPipe: a framework for rapid prototyping of pipelines combining language and vision
Authors: Tomasz Kornuta
Date: 2019-10-18 00:00:00

Access to vast amounts of data along with affordable computational power stimulated the reincarnation of neural networks. The progress could not be achieved without adequate software tools, lowering the entry bar for the next generations of researchers and developers. The paper introduces PyTorchPipe (PTP), a framework built on top of PyTorch. Answering the recent needs and trends in machine learning, PTP facilitates building and training of complex, multi-modal models combining language and vision (but is not limited to those two modalities). At its core, PTP employs a component-oriented approach and relies on the concept of a pipeline, defined as a directed acyclic graph of loosely coupled components. A user defines a pipeline using yaml-based (thus human-readable) configuration files, whereas PTP provides generic workers for their loading, training, and testing using all the computational power (CPUs and GPUs) that is available to the user. The paper covers the main concepts of PyTorchPipe, discusses its key features and briefly presents the currently implemented tasks, models and components.
ELSA: A Throughput-Optimized Design of an LSTM Accelerator for Energy-Constrained Devices
Authors: Elham Azari, Sarma Vrudhula
Date: 2019-10-19 00:00:00
Tasks: Language Modeling, Language Modelling

The next significant step in the evolution and proliferation of artificial intelligence technology will be the integration of neural network (NN) models within embedded and mobile systems. This calls for the design of compact, energy efficient NN models in silicon. In this paper, we present a scalable ASIC design of an LSTM accelerator named ELSA, that is suitable for energy-constrained devices. It includes several architectural innovations to achieve small area and high energy efficiency. To reduce the area and power consumption of the overall design, the compute-intensive units of ELSA employ approximate multiplications and still achieve high performance and accuracy. The performance is further improved through efficient synchronization of the elastic pipeline stages to maximize the utilization. The paper also includes a performance model of ELSA, as a function of the hidden nodes and time steps, permitting its use for the evaluation of any LSTM application. ELSA was implemented in RTL and was synthesized and placed and routed in 65nm technology. Its functionality is demonstrated for language modeling-a common application of LSTM. ELSA is compared against a baseline implementation of an LSTM accelerator with standard functional units and without any of the architectural innovations of ELSA. The paper demonstrates that ELSA can achieve significant improvements in power, area and energy-efficiency when compared to the baseline design and several ASIC implementations reported in the literature, making it suitable for use in embedded systems and real-time applications.
Self-Adaptive Network Pruning
Authors: Jinting Chen, Zhaocheng Zhu, Cheng Li, Yuming Zhao
Date: 2019-10-20 00:00:00
Tasks: Network Pruning

Deep convolutional neural networks have been proved successful on a wide range of tasks, yet they are still hindered by their large computation cost in many industrial scenarios. In this paper, we propose to reduce such cost for CNNs through a self-adaptive network pruning method (SANP). Our method introduces a general Saliency-and-Pruning Module (SPM) for each convolutional layer, which learns to predict saliency scores and applies pruning for each channel. Given a total computation budget, SANP adaptively determines the pruning strategy with respect to each layer and each sample, such that the average computation cost meets the budget. This design allows SANP to be more efficient in computation, as well as more robust to datasets and backbones. Extensive experiments on 2 datasets and 3 backbones show that SANP surpasses state-of-the-art methods in both classification accuracy and pruning rate.
Interpreting Basis Path Set in Neural Networks
Authors: Juanping Zhu, Qi Meng, Wei Chen, Zhi-Ming Ma
Date: 2019-10-18 00:00:00

Based on basis path set, G-SGD algorithm significantly outperforms conventional SGD algorithm in optimizing neural networks. However, how the inner mechanism of basis paths work remains mysterious. From the aspect of graph theory, this paper defines basis path, investigates structure properties of basis paths in regular fully connected neural network and interprets the graph representation of basis path set. Moreover, we propose hierarchical algorithm HBPS to find basis path set B in fully connected neural network by decomposing the network into several independent and parallel substructures. Algorithm HBPS demands that there doesn't exist shared edges between any two independent substructure paths.
Towards Learning Cross-Modal Perception-Trace Models
Authors: Achim Rettinger, Viktoria Bogdanova, Philipp Niemann
Date: 2019-10-18 00:00:00
Tasks: Knowledge Graphs, Link Prediction, Representation Learning

Representation learning is a key element of state-of-the-art deep learning approaches. It enables to transform raw data into structured vector space embeddings. Such embeddings are able to capture the distributional semantics of their context, e.g. by word windows on natural language sentences, graph walks on knowledge graphs or convolutions on images. So far, this context is manually defined, resulting in heuristics which are solely optimized for computational performance on certain tasks like link-prediction. However, such heuristic models of context are fundamentally different to how humans capture information. For instance, when reading a multi-modal webpage (i) humans do not perceive all parts of a document equally: Some words and parts of images are skipped, others are revisited several times which makes the perception trace highly non-sequential; (ii) humans construct meaning from a document's content by shifting their attention between text and image, among other things, guided by layout and design elements. In this paper we empirically investigate the difference between human perception and context heuristics of basic embedding models. We conduct eye tracking experiments to capture the underlying characteristics of human perception of media documents containing a mixture of text and images. Based on that, we devise a prototypical computational perception-trace model, called CMPM. We evaluate empirically how CMPM can improve a basic skip-gram embedding approach. Our results suggest, that even with a basic human-inspired computational perception model, there is a huge potential for improving embeddings since such a model does inherently capture multiple modalities, as well as layout and design elements.
Anderson Acceleration of Proximal Gradient Methods
Authors: Vien V. Mai, Mikael Johansson
Date: 2019-10-18 00:00:00
Proceeding: ICML 2020 1

Anderson acceleration is a well-established and simple technique for speeding up fixed-point computations with countless applications. Previous studies of Anderson acceleration in optimization have only been able to provide convergence guarantees for unconstrained and smooth problems. This work introduces novel methods for adapting Anderson acceleration to (non-smooth and constrained) proximal gradient algorithms. Under some technical conditions, we extend the existing local convergence results of Anderson acceleration for smooth fixed-point mappings to the proposed scheme. We also prove analytically that it is not, in general, possible to guarantee global convergence of native Anderson acceleration. We therefore propose a simple scheme for stabilization that combines the global worst-case guarantees of proximal gradient methods with the local adaptation and practical speed-up of Anderson acceleration.
Real-Time Lip Sync for Live 2D Animation
Authors: Deepali Aneja, Wilmot Li
Date: 2019-10-19 00:00:00
Tasks: Constrained Lip-synchronization, Data Augmentation

The emergence of commercial tools for real-time performance-based 2D animation has enabled 2D characters to appear on live broadcasts and streaming platforms. A key requirement for live animation is fast and accurate lip sync that allows characters to respond naturally to other actors or the audience through the voice of a human performer. In this work, we present a deep learning based interactive system that automatically generates live lip sync for layered 2D characters using a Long Short Term Memory (LSTM) model. Our system takes streaming audio as input and produces viseme sequences with less than 200ms of latency (including processing time). Our contributions include specific design decisions for our feature definition and LSTM configuration that provide a small but useful amount of lookahead to produce accurate lip sync. We also describe a data augmentation procedure that allows us to achieve good results with a very small amount of hand-animated training data (13-20 minutes). Extensive human judgement experiments show that our results are preferred over several competing methods, including those that only support offline (non-live) processing. Video summary and supplementary results at GitHub link: https://github.com/deepalianeja/CharacterLipSync2D
Microservices based Framework to Support Interoperable IoT Applications for Enhanced Data Analytics
Authors: Sajjad Ali, Muhammad Aslam Jarwar, Ilyoung Chong
Date: 2019-10-19 00:00:00
Tasks: Management

Internet of things is growing with a large number of diverse objects which generate billions of data streams by sensing, actuating and communicating. Management of heterogeneous IoT objects with existing approaches and processing of myriads of data from these objects using monolithic services have become major challenges in developing effective IoT applications. The heterogeneity can be resolved by providing interoperability with semantic virtualization of objects. Moreover, monolithic services can be substituted with modular microservices. This article presents an architecture that enables the development of IoT applications using semantically interoperable microservices and virtual objects. The proposed framework supports analytic features with knowledge-driven and data-driven techniques to provision intelligent services on top of interoperable microservices in Web Objects enabled IoT environment. The knowledge-driven aspects are supported with reasoning on semantic ontology models and the data-driven aspects are realized with machine learning pipeline. The development of service functionalities is supported with microservices to enhance modularity and reusability. To evaluate the proposed framework a proof of concept implementation with a use case is discussed.
Convex Reconstruction of Structured Matrix Signals from Linear Measurements (I): Theoretical Results
Authors: Yuan Tian
Date: 2019-10-19 00:00:00
Tasks: Compressive Sensing

We investigate the problem of reconstructing n-by-n structured matrix signal X via convex programming, where each column xj is a vector of s-sparsity and all columns have the same l1-norm. The regularizer in use is matrix norm |||X|||1=maxj|xj|1.The contribution in this paper has two parts. The first part is about conditions for stability and robustness in signal reconstruction via solving the convex programming from noise-free or noisy measurements.We establish uniform sufficient conditions which are very close to necessary conditions and non-uniform conditions are also discussed. Similar as the traditional compressive sensing theory for reconstructing vector signals, a related RIP condition is established. In addition, stronger conditions are investigated to guarantee the reconstructed signal's support stability, sign stability and approximation-error robustness. The second part is to establish upper and lower bounds on number of measurements for robust reconstruction in noise. We take the convex geometric approach in random measurement setting and one of the critical ingredients in this approach is to estimate the related widths bounds in case of Gaussian and non-Gaussian distributions. These bounds are explicitly controlled by signal's structural parameters r and s which determine matrix signal's column-wise sparsity and l1-column-flatness respectively.
LEt-SNE: A Hybrid Approach To Data Embedding and Visualization of Hyperspectral Imagery
Authors: Megh Shukla, Biplab Banerjee, Krishna Mohan Buddhiraju
Date: 2019-10-19 00:00:00
Tasks: Clustering, Dimensionality Reduction

Hyperspectral Imagery (and Remote Sensing in general) captured from UAVs or satellites are highly voluminous in nature due to the large spatial extent and wavelengths captured by them. Since analyzing these images requires a huge amount of computational time and power, various dimensionality reduction techniques have been used for feature reduction. Some popular techniques among these falter when applied to Hyperspectral Imagery due to the famed curse of dimensionality. In this paper, we propose a novel approach, LEt-SNE, which combines graph based algorithms like t-SNE and Laplacian Eigenmaps into a model parameterized by a shallow feed forward network. We introduce a new term, Compression Factor, that enables our method to combat the curse of dimensionality. The proposed algorithm is suitable for manifold visualization and sample clustering with labelled or unlabelled data. We demonstrate that our method is competitive with current state-of-the-art methods on hyperspectral remote sensing datasets in public domain.
Data classification by quantum radial basis function networks
Authors: Changpeng Shao
Date: 2019-10-19 00:00:00
Tasks: Binary Classification, Classification, General Classification

Radial basis function (RBF) network is a third layered neural network that is widely used in function approximation and data classification. Here we propose a quantum model of the RBF network. Similar to the classical case, we still use the radial basis functions as the activation functions. Quantum linear algebraic techniques and coherent states can be applied to implement these functions. Differently, we define the state of the weight as a tensor product of single-qubit states. This gives a simple approach to implement the quantum RBF network in the quantum circuits. Theoretically, we prove that the training is almost quadratic faster than the classical one. Numerically, we demonstrate that the quantum RBF network can solve binary classification problems as good as the classical RBF network. While the time used for training is much shorter.
Opinion shaping in social networks using reinforcement learning
Authors: Vivek Borkar, Alexandre Reiffers-Masson
Date: 2019-10-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we study how to shape opinions in social networks when the matrix of interactions is unknown. We consider classical opinion dynamics with some stubborn agents and the possibility of continuously influencing the opinions of a few selected agents, albeit under resource constraints. We map the opinion dynamics to a value iteration scheme for policy evaluation for a specific stochastic shortest path problem. This leads to a representation of the opinion vector as an approximate value function for a stochastic shortest path problem with some non-classical constraints. We suggest two possible ways of influencing agents. One leads to a convex optimization problem and the other to a non-convex one. Firstly, for both problems, we propose two different online two-time scale reinforcement learning schemes that converge to the optimal solution of each problem. Secondly, we suggest stochastic gradient descent schemes and compare these classes of algorithms with the two-time scale reinforcement learning schemes. Thirdly, we also derive another algorithm designed to tackle the curse of dimensionality one faces when all agents are observed. Numerical studies are provided to illustrate the convergence and efficiency of our algorithms.
NormGrad: Finding the Pixels that Matter for Training
Authors: Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, Hakan Bilen, Andrea Vedaldi
Date: 2019-10-19 00:00:00
Tasks: Meta-Learning

The different families of saliency methods, either based on contrastive signals, closed-form formulas mixing gradients with activations or on perturbation masks, all focus on which parts of an image are responsible for the model's inference. In this paper, we are rather interested by the locations of an image that contribute to the model's training. First, we propose a principled attribution method that we extract from the summation formula used to compute the gradient of the weights for a 1x1 convolutional layer. The resulting formula is fast to compute and can used throughout the network, allowing us to efficiently produce fined-grained importance maps. We will show how to extend it in order to compute saliency maps at any targeted point within the network. Secondly, to make the attribution really specific to the training of the model, we introduce a meta-learning approach for saliency methods by considering an inner optimisation step within the loss. This way, we do not aim at identifying the parts of an image that contribute to the model's output but rather the locations that are responsible for the good training of the model on this image. Conversely, we also show that a similar meta-learning approach can be used to extract the adversarial locations which can lead to the degradation of the model.
ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image Compression
Authors: Li-Heng Chen, Christos G. Bampis, Zhi Li, Andrey Norkin, Alan C. Bovik
Date: 2019-10-19 00:00:00
Tasks: Image Compression

The use of $\ell_p$ $(p=1,2)$ norms has largely dominated the measurement of loss in neural networks due to their simplicity and analytical properties. However, when used to assess the loss of visual information, these simple norms are not very consistent with human perception. Here, we describe a different "proximal" approach to optimize image analysis networks against quantitative perceptual models. Specifically, we construct a proxy network, broadly termed ProxIQA, which mimics the perceptual model while serving as a loss layer of the network. We experimentally demonstrate how this optimization framework can be applied to train an end-to-end optimized image compression network. By building on top of an existing deep image compression model, we are able to demonstrate a bitrate reduction of as much as $31\%$ over MSE optimization, given a specified perceptual quality (VMAF) level.
Image Restoration Using Deep Regulated Convolutional Networks
Authors: Peng Liu, Xiaoxiao Zhou, Junyi Yang, El Basha Mohammad D, Ruogu Fang
Date: 2019-10-19 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration, Super-Resolution

While the depth of convolutional neural networks has attracted substantial attention in the deep learning research, the width of these networks has recently received greater interest. The width of networks, defined as the size of the receptive fields and the density of the channels, has demonstrated crucial importance in low-level vision tasks such as image denoising and restoration. However, the limited generalization ability, due to the increased width of networks, creates a bottleneck in designing wider networks. In this paper, we propose the Deep Regulated Convolutional Network (RC-Net), a deep network composed of regulated sub-network blocks cascaded by skip-connections, to overcome this bottleneck. Specifically, the Regulated Convolution block (RC-block), featured by a combination of large and small convolution filters, balances the effectiveness of prominent feature extraction and the generalization ability of the network. RC-Nets have several compelling advantages: they embrace diversified features through large-small filter combinations, alleviate the hazy boundary and blurred details in image denoising and super-resolution problems, and stabilize the learning process. Our proposed RC-Nets outperform state-of-the-art approaches with significant performance gains in various image restoration tasks while demonstrating promising generalization ability. The code is available at https://github.com/cswin/RC-Nets.
KRNET: Image Denoising with Kernel Regulation Network
Authors: Peng Liu, Xiaoxiao Zhou, Junyiyang Li, El Basha Mohammad D, Ruogu Fang
Date: 2019-10-20 00:00:00
Tasks: Color Image Denoising, Denoising, Image Denoising

One popular strategy for image denoising is to design a generalized regularization term that is capable of exploring the implicit prior underlying data observation. Convolutional neural networks (CNN) have shown the powerful capability to learn image prior information through a stack of layers defined by a combination of kernels (filters) on the input. However, existing CNN-based methods mainly focus on synthetic gray-scale images. These methods still exhibit low performance when tackling multi-channel color image denoising. In this paper, we optimize CNN regularization capability by developing a kernel regulation module. In particular, we propose a kernel regulation network-block, referred to as KR-block, by integrating the merits of both large and small kernels, that can effectively estimate features in solving image denoising. We build a deep CNN-based denoiser, referred to as KRNET, via concatenating multiple KR-blocks. We evaluate KRNET on additive white Gaussian noise (AWGN), multi-channel (MC) noise, and realistic noise, where KRNET obtains significant performance gains over state-of-the-art methods across a wide spectrum of noise levels.