pwc-archive/papers-with-abstracts huggingface dataset:
A Novel Generalized Artificial Neural Network for Mining Two-Class Datasets
Authors: Wei-Chang Yeh
Date: 2019-10-23 00:00:00
Tasks: ARC, Attribute, Vocal Bursts Valence Prediction

A novel general neural network (GNN) is proposed for two-class data mining in this study. In a GNN, each attribute in the dataset is treated as a node, with each pair of nodes being connected by an arc. The reliability is of each arc, which is similar to the weight in artificial neural network and must be solved using simplified swarm optimization (SSO), is constant. After the node reliability is made the transformed value of the related attribute, the approximate reliability of each GNN instance is calculated based on the proposed intelligent Monte Carlo simulation (iMCS). This approximate GNN reliability is then compared with a given threshold to predict each instance. The proposed iMCS-SSO is used to repeat the procedure and train the GNN, such that the predicted class values match the actual class values as much as possible. To evaluate the classification performance of the proposed GNN, experiments were performed on five well-known benchmark datasets. The computational results compared favorably with those obtained using support vector machines.
Divide-and-Conquer Adversarial Learning for High-Resolution Image and Video Enhancement
Authors: Zhiwu Huang, Danda Pani Paudel, Guanju Li, Jiqing Wu, Radu Timofte, Luc van Gool
Date: 2019-10-23 00:00:00
Tasks: Generative Adversarial Network, Video Enhancement

This paper introduces a divide-and-conquer inspired adversarial learning (DACAL) approach for photo enhancement. The key idea is to decompose the photo enhancement process into hierarchically multiple sub-problems, which can be better conquered from bottom to up. On the top level, we propose a perception-based division to learn additive and multiplicative components, required to translate a low-quality image or video into its high-quality counterpart. On the intermediate level, we use a frequency-based division with generative adversarial network (GAN) to weakly supervise the photo enhancement process. On the lower level, we design a dimension-based division that enables the GAN model to better approximates the distribution distance on multiple independent one-dimensional data to train the GAN model. While considering all three hierarchies, we develop multiscale and recurrent training approaches to optimize the image and video enhancement process in a weakly-supervised manner. Both quantitative and qualitative results clearly demonstrate that the proposed DACAL achieves the state-of-the-art performance for high-resolution image and video enhancement.
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning
Authors: Anis Elgabli, Jihong Park, Amrit S. Bedi, Chaouki Ben Issaid, Mehdi Bennis, Vaneet Aggarwal
Date: 2019-10-23 00:00:00
Tasks: BIG-bench Machine Learning, image-classification, Image Classification, Quantization

In this article, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM). To reduce the number of communication links, every worker in Q-GADMM communicates only with two neighbors, while updating its model via the group alternating direction method of multipliers (GADMM). Moreover, each worker transmits the quantized difference between its current model and its previously quantized model, thereby decreasing the communication payload size. However, due to the lack of centralized entity in decentralized ML, the spatial sparsity and payload compression may incur error propagation, hindering model training convergence. To overcome this, we develop a novel stochastic quantization method to adaptively adjust model quantization levels and their probabilities, while proving the convergence of Q-GADMM for convex objective functions. Furthermore, to demonstrate the feasibility of Q-GADMM for non-convex and stochastic problems, we propose quantized stochastic GADMM (Q-SGADMM) that incorporates deep neural network architectures and stochastic sampling. Simulation results corroborate that Q-GADMM significantly outperforms GADMM in terms of communication efficiency while achieving the same accuracy and convergence speed for a linear regression task. Similarly, for an image classification task using DNN, Q-SGADMM achieves significantly less total communication cost with identical accuracy and convergence speed compared to its counterpart without quantization, i.e., stochastic GADMM (SGADMM).
CNN-based Analog CSI Feedback in FDD MIMO-OFDM Systems
Authors: Mahdi Boloursaz Mashhadi, Qianqian Yang, Deniz Gunduz
Date: 2019-10-23 00:00:00
Tasks: Diversity, Quantization

Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to better utilize the available spatial diversity and multiplexing gains. However, in a frequency division duplex (FDD) massive MIMO system, CSI feedback overhead degrades the overall spectral efficiency. Convolutional neural network (CNN)-based CSI feedback compression schemes has received a lot of attention recently due to significant improvements in compression efficiency; however, they still require reliable feedback links to convey the compressed CSI information to the BS. Instead, we propose here a CNN-based analog feedback scheme, called AnalogDeepCMC, which directly maps the downlink CSI to uplink channel input. Corresponding noisy channel outputs are used by another CNN to reconstruct the DL channel estimate. Not only the proposed outperforms existing digital CSI feedback schemes in terms of the achievable downlink rate, but also simplifies the operation as it does not require explicit quantization, coding and modulation, and provides a low-latency alternative particularly in rapidly changing MIMO channels, where the CSI needs to be estimated and fed back periodically.
Identification of primary angle-closure on AS-OCT images with Convolutional Neural Networks
Authors: Chenglang Yuan, Cheng Bian, Hongjian Kang, Shu Liang, Kai Ma, Yefeng Zheng
Date: 2019-10-23 00:00:00
Tasks: General Classification

Primary angle-closure disease (PACD) is a severe retinal disease, which might cause irreversible vision loss. In clinic, accurate identification of angle-closure and localization of the scleral spur's position on anterior segment optical coherence tomography (AS-OCT) is essential for the diagnosis of PACD. However, manual delineation might confine in low accuracy and low efficiency. In this paper, we propose an efficient and accurate end-to-end architecture for angle-closure classification and scleral spur localization. Specifically, we utilize a revised ResNet152 as our backbone to improve the accuracy of the angle-closure identification. For scleral spur localization, we adopt EfficientNet as encoder because of its powerful feature extraction potential. By combining the skip-connect module and pyramid pooling module, the network is able to collect semantic cues in feature maps from multiple dimensions and scales. Afterward, we propose a novel keypoint registration loss to constrain the model's attention to the intensity and location of the scleral spur area. Several experiments are extensively conducted to evaluate our method on the angle-closure glaucoma evaluation (AGE) Challenge dataset. The results show that our proposed architecture ranks the first place of the classification task on the test dataset and achieves the average Euclidean distance error of 12.00 pixels in the scleral spur localization task.
BanditRank: Learning to Rank Using Contextual Bandits
Authors: Phanideep Gampa, Sumio Fujita
Date: 2019-10-23 00:00:00
Tasks: Information Retrieval, Learning-To-Rank, Multi-Armed Bandits, Question Answering, Reinforcement Learning, Retrieval

We propose an extensible deep learning method that uses reinforcement learning to train neural networks for offline ranking in information retrieval (IR). We call our method BanditRank as it treats ranking as a contextual bandit problem. In the domain of learning to rank for IR, current deep learning models are trained on objective functions different from the measures they are evaluated on. Since most evaluation measures are discrete quantities, they cannot be leveraged by directly using gradient descent algorithms without an approximation. BanditRank bridges this gap by directly optimizing a task-specific measure, such as mean average precision (MAP), using gradient descent. Specifically, a contextual bandit whose action is to rank input documents is trained using a policy gradient algorithm to directly maximize the reward. The reward can be a single measure, such as MAP, or a combination of several measures. The notion of ranking is also inherent in BanditRank, similar to the current \textit{listwise} approaches. To evaluate the effectiveness of BanditRank, we conducted a series of experiments on datasets related to three different tasks, i.e., web search, community, and factoid question answering. We found that it performs better than state-of-the-art methods when applied on the question answering datasets. On the web search dataset, we found that BanditRank performed better than four strong listwise baselines including LambdaMART, AdaRank, ListNet and Coordinate Ascent.
Controlling the Output Length of Neural Machine Translation
Authors: Surafel Melaku Lakew, Mattia Di Gangi, Marcello Federico
Date: 2019-10-23 00:00:00
Proceeding: EMNLP (IWSLT) 2019 11
Tasks: Machine Translation, NMT, Translation

The recent advances introduced by neural machine translation (NMT) are rapidly expanding the application fields of machine translation, as well as reshaping the quality level to be targeted. In particular, if translations have to fit some given layout, quality should not only be measured in terms of adequacy and fluency, but also length. Exemplary cases are the translation of document files, subtitles, and scripts for dubbing, where the output length should ideally be as close as possible to the length of the input text. This paper addresses for the first time, to the best of our knowledge, the problem of controlling the output length in NMT. We investigate two methods for biasing the output length with a transformer architecture: i) conditioning the output to a given target-source length-ratio class and ii) enriching the transformer positional embedding with length information. Our experiments show that both methods can induce the network to generate shorter translations, as well as acquiring interpretable linguistic skills.
INTEL-TAU: A Color Constancy Dataset
Authors: Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis, Jarno Nikkanen, Moncef Gabbouj
Date: 2019-10-23 00:00:00
Tasks: Color Constancy, Few-Shot Camera-Adaptive Color Constancy, Image Declipping, Multi-target regression

In this paper, we describe a new large dataset for illumination estimation. This dataset, called INTEL-TAU, contains 7022 images in total, which makes it the largest available high-resolution dataset for illumination estimation research. The variety of scenes captured using three different camera models, namely Canon 5DSR, Nikon D810, and Sony IMX135, makes the dataset appropriate for evaluating the camera and scene invariance of the different illumination estimation techniques. Privacy masking is done for sensitive information, e.g., faces. Thus, the dataset is coherent with the new General Data Protection Regulation (GDPR). Furthermore, the effect of color shading for mobile images can be evaluated with INTEL-TAU dataset, as both corrected and uncorrected versions of the raw data are provided. Furthermore, this paper benchmarks several color constancy approaches on the proposed dataset.
Filterbank design for end-to-end speech separation
Authors: Manuel Pariente, Samuele Cornell, Antoine Deleforge, Emmanuel Vincent
Date: 2019-10-23 00:00:00
Tasks: Speaker Recognition, Speech Separation

Single-channel speech separation has recently made great progress thanks to learned filterbanks as used in ConvTasNet. In parallel, parameterized filterbanks have been proposed for speaker recognition where only center frequencies and bandwidths are learned. In this work, we extend real-valued learned and parameterized filterbanks into complex-valued analytic filterbanks and define a set of corresponding representations and masking strategies. We evaluate these filterbanks on a newly released noisy speech separation dataset (WHAM). The results show that the proposed analytic learned filterbank consistently outperforms the real-valued filterbank of ConvTasNet. Also, we validate the use of parameterized filterbanks and show that complex-valued representations and masks are beneficial in all conditions. Finally, we show that the STFT achieves its best performance for 2ms windows.
Random 2.5D U-net for Fully 3D Segmentation
Authors: Christoph Angermann, Markus Haltmeier
Date: 2019-10-23 00:00:00
Tasks: GPU, Segmentation

Convolutional neural networks are state-of-the-art for various segmentation tasks. While for 2D images these networks are also computationally efficient, 3D convolutions have huge storage requirements and therefore, end-to-end training is limited by GPU memory and data size. To overcome this issue, we introduce a network structure for volumetric data without 3D convolution layers. The main idea is to include projections from different directions to transform the volumetric data to a sequence of images, where each image contains information of the full data. We then apply 2D convolutions to these projection images and lift them again to volumetric data using a trainable reconstruction algorithm. The proposed architecture can be applied end-to-end to very large data volumes without cropping or sliding-window techniques. For a tested sparse binary segmentation task, it outperforms already known standard approaches and is more resistant to generation of artefacts.
Efficient Decoupled Neural Architecture Search by Structure and Operation Sampling
Authors: Heung-Chang Lee, Do-Guk Kim, Bohyung Han
Date: 2019-10-23 00:00:00
Tasks: Neural Architecture Search, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a novel neural architecture search algorithm via reinforcement learning by decoupling structure and operation search processes. Our approach samples candidate models from the multinomial distribution on the policy vectors defined on the two search spaces independently. The proposed technique improves the efficiency of architecture search process significantly compared to the conventional methods based on reinforcement learning with the RNN controllers while achieving competitive accuracy and model size in target tasks. Our policy vectors are easily interpretable throughout the training procedure, which allows to analyze the search progress and the discovered architectures; the black-box characteristics of the RNN controllers hamper understanding training progress in terms of policy parameter updates. Our experiments demonstrate outstanding performance compared to the state-of-the-art methods with a fraction of search cost.
RTOP: A Conceptual and Computational Framework for General Intelligence
Authors: Shilpesh Garg
Date: 2019-10-23 00:00:00

A novel general intelligence model is proposed with three types of learning. A unified sequence of the foreground percept trace and the command trace translates into direct and time-hop observation paths to form the basis of Raw learning. Raw learning includes the formation of image-image associations, which lead to the perception of temporal and spatial relationships among objects and object parts; and the formation of image-audio associations, which serve as the building blocks of language. Offline identification of similar segments in the observation paths and their subsequent reduction into a common segment through merging of memory nodes leads to Generalized learning. Generalization includes the formation of interpolated sensory nodes for robust and generic matching, the formation of sensory properties nodes for specific matching and superimposition, and the formation of group nodes for simpler logic pathways. Online superimposition of memory nodes across multiple predictions, primarily the superimposition of images on the internal projection canvas, gives rise to Innovative learning and thought. The learning of actions happens the same way as raw learning while the action determination happens through the utility model built into the raw learnings, the utility function being the pleasure and pain of the physical senses.
Speech-XLNet: Unsupervised Acoustic Model Pretraining For Self-Attention Networks
Authors: Xingchen Song, Guangsen Wang, Zhiyong Wu, Yiheng Huang, Dan Su, Dong Yu, Helen Meng
Date: 2019-10-23 00:00:00
Tasks: Representation Learning, Speech Representation Learning

Self-attention network (SAN) can benefit significantly from the bi-directional representation learning through unsupervised pretraining paradigms such as BERT and XLNet. In this paper, we present an XLNet-like pretraining scheme "Speech-XLNet" for unsupervised acoustic model pretraining to learn speech representations with SAN. The pretrained SAN is finetuned under the hybrid SAN/HMM framework. We conjecture that by shuffling the speech frame orders, the permutation in Speech-XLNet serves as a strong regularizer to encourage the SAN to make inferences by focusing on global structures through its attention weights. In addition, Speech-XLNet also allows the model to explore the bi-directional contexts for effective speech representation learning. Experiments on TIMIT and WSJ demonstrate that Speech-XLNet greatly improves the SAN/HMM performance in terms of both convergence speed and recognition accuracy compared to the one trained from randomly initialized weights. Our best systems achieve a relative improvement of 11.9% and 8.3% on the TIMIT and WSJ tasks respectively. In particular, the best system achieves a phone error rate (PER) of 13.3% on the TIMIT test set, which to our best knowledge, is the lowest PER obtained from a single system.
Stabilising priors for robust Bayesian deep learning
Authors: Felix McGregor, Arnu Pretorius, Johan du Preez, Steve Kroon
Date: 2019-10-23 00:00:00
Tasks: Deep Learning, Variational Inference

Bayesian neural networks (BNNs) have developed into useful tools for probabilistic modelling due to recent advances in variational inference enabling large scale BNNs. However, BNNs remain brittle and hard to train, especially: (1) when using deep architectures consisting of many hidden layers and (2) in situations with large weight variances. We use signal propagation theory to quantify these challenges and propose self-stabilising priors. This is achieved by a reformulation of the ELBO to allow the prior to influence network signal propagation. Then, we develop a stabilising prior, where the distributional parameters of the prior are adjusted before each forward pass to ensure stability of the propagating signal. This stabilised signal propagation leads to improved convergence and robustness making it possible to train deeper networks and in more noisy settings.
Online Synthesis for Runtime Enforcement of Safety in Multi-Agent Systems
Authors: Dhananjay Raju, Suda Bharadwaj, Ufuk Topcu
Date: 2019-10-23 00:00:00
Tasks: Collision Avoidance, Pathfinder

A shield is attached to a system to guarantee safety by correcting the system's behavior at runtime. Existing methods that employ design-time synthesis of shields do not scale to multi-agent systems. Moreover, such shields are typically implemented in a centralized manner, requiring global information on the state of all agents in the system. We address these limitations through a new approach where the shields are synthesized at runtime and do not require global information. There is a shield onboard every agent, which can only modify the behavior of the corresponding agent. In this approach, which is fundamentally decentralized, the shield on every agent has two components: a pathfinder that corrects the behavior of the agent and an ordering mechanism that dynamically modifies the priority of the agent. The current priority determines if the shield uses the pathfinder to modify behavior of the agent. We derive an upper bound on the maximum deviation for any agent from its original behavior. We prove that the worst-case synthesis time is quadratic in the number of agents at runtime as opposed to exponential at design-time for existing methods. We test the performance of the decentralized, runtime shield synthesis approach on a collision-avoidance problem. For 50 agents in a 50x50 grid, the synthesis at runtime requires a few seconds per agent whenever a potential collision is detected. In contrast, the centralized design-time synthesis of shields for a similar setting is intractable beyond 4 agents in a 5x5 grid.
Network2Vec Learning Node Representation Based on Space Mapping in Networks
Authors: Huang Zhenhua, Wang Zhenyu, Zhang Rui, Zhao Yangyang, Xie Xiaohui, Sharad Mehrotra
Date: 2019-10-23 00:00:00
Tasks: General Classification, Link Prediction, Network Embedding, Node Classification

Complex networks represented as node adjacency matrices constrains the application of machine learning and parallel algorithms. To address this limitation, network embedding (i.e., graph representation) has been intensively studied to learn a fixed-length vector for each node in an embedding space, where the node properties in the original graph are preserved. Existing methods mainly focus on learning embedding vectors to preserve nodes proximity, i.e., nodes next to each other in the graph space should also be closed in the embedding space, but do not enforce algebraic statistical properties to be shared between the embedding space and graph space. In this work, we propose a lightweight model, entitled Network2Vec, to learn network embedding on the base of semantic distance mapping between the graph space and embedding space. The model builds a bridge between the two spaces leveraging the property of group homomorphism. Experiments on different learning tasks, including node classification, link prediction, and community visualization, demonstrate the effectiveness and efficiency of the new embedding method, which improves the state-of-the-art model by 19% in node classification and 7% in link prediction tasks at most. In addition, our method is significantly faster, consuming only a fraction of the time used by some famous methods.