pwc-archive/papers-with-abstracts huggingface dataset:
Multi-object Tracking via End-to-end Tracklet Searching and Ranking
Authors: Tao Hu, Lichao Huang, Han Shen
Date: 2020-03-04 00:00:00
Tasks: Multi-Object Tracking, Multiple Object Tracking, Object Tracking

Recent works in multiple object tracking use sequence model to calculate the similarity score between the detections and the previous tracklets. However, the forced exposure to ground-truth in the training stage leads to the training-inference discrepancy problem, i.e., exposure bias, where association error could accumulate in the inference and make the trajectories drift. In this paper, we propose a novel method for optimizing tracklet consistency, which directly takes the prediction errors into account by introducing an online, end-to-end tracklet search training process. Notably, our methods directly optimize the whole tracklet score instead of pairwise affinity. With sequence model as appearance encoders of tracklet, our tracker achieves remarkable performance gain from conventional tracklet association baseline. Our methods have also achieved state-of-the-art in MOT15~17 challenge benchmarks using public detection and online settings.
Real-time Federated Evolutionary Neural Architecture Search
Authors: Hangyu Zhu, Yaochu Jin
Date: 2020-03-04 00:00:00
Tasks: BIG-bench Machine Learning, Federated Learning, Neural Architecture Search

Federated learning is a distributed machine learning approach to privacy preservation and two major technical challenges prevent a wider application of federated learning. One is that federated learning raises high demands on communication, since a large number of model parameters must be transmitted between the server and the clients. The other challenge is that training large machine learning models such as deep neural networks in federated learning requires a large amount of computational resources, which may be unrealistic for edge devices such as mobile phones. The problem becomes worse when deep neural architecture search is to be carried out in federated learning. To address the above challenges, we propose an evolutionary approach to real-time federated neural architecture search that not only optimize the model performance but also reduces the local payload. During the search, a double-sampling technique is introduced, in which for each individual, a randomly sampled sub-model of a master model is transmitted to a number of randomly sampled clients for training without reinitialization. This way, we effectively reduce computational and communication costs required for evolutionary optimization and avoid big performance fluctuations of the local models, making the proposed framework well suited for real-time federated neural architecture search.
EPINE: Enhanced Proximity Information Network Embedding
Authors: Luoyi Zhang, Ming Xu
Date: 2020-03-04 00:00:00
Tasks: Link Prediction, Network Embedding, Node Classification

Unsupervised homogeneous network embedding (NE) represents every vertex of networks into a low-dimensional vector and meanwhile preserves the network information. Adjacency matrices retain most of the network information, and directly charactrize the first-order proximity. In this work, we devote to mining valuable information in adjacency matrices at a deeper level. Under the same objective, many NE methods calculate high-order proximity by the powers of adjacency matrices, which is not accurate and well-designed enough. Instead, we propose to redefine high-order proximity in a more intuitive manner. Besides, we design a novel algorithm for calculation, which alleviates the scalability problem in the field of accurate calculation for high-order proximity. Comprehensive experiments on real-world network datasets demonstrate the effectiveness of our method in downstream machine learning tasks such as network reconstruction, link prediction and node classification.
Phase transitions in a decentralized graph-based approach to human language
Authors: Javier Vera, Felipe Urbina, Wenceslao Palma
Date: 2020-03-04 00:00:00

Zipf's law establishes a scaling behavior for word-frequencies in large text corpora. The appearance of Zipfian properties in human language has been previously explained as an optimization problem for the interests of speakers and hearers. On the other hand, human-like vocabularies can be viewed as bipartite graphs. The aim here is double: within a bipartite-graph approach to human vocabularies, to propose a decentralized language game model for the formation of Zipfian properties. To do this, we define a language game, in which a population of artificial agents is involved in idealized linguistic interactions. Numerical simulations show the appearance of a phase transition from an initially disordered state to three possible phases for language formation. Our results suggest that Zipfian properties in language seem to arise partly from decentralized linguistic interactions between agents endowed with bipartite word-meaning mappings.
Hybrid modeling: Applications in real-time diagnosis
Authors: Ion Matei, Johan de Kleer, Alexander Feldman, Rahul Rai, Souma Chowdhury
Date: 2020-03-04 00:00:00
Tasks: BIG-bench Machine Learning, Fault Diagnosis

Reduced-order models that accurately abstract high fidelity models and enable faster simulation is vital for real-time, model-based diagnosis applications. In this paper, we outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models to generate reduced-order models from high fidelity models. We are using such models for real-time diagnosis applications. Specifically, we have developed machine learning inspired representations to generate reduced order component models that preserve, in part, the physical interpretation of the original high fidelity component models. To ensure the accuracy, scalability and numerical stability of the learning algorithms when training the reduced-order models we use optimization platforms featuring automatic differentiation. Training data is generated by simulating the high-fidelity model. We showcase our approach in the context of fault diagnosis of a rail switch system. Three new model abstractions whose complexities are two orders of magnitude smaller than the complexity of the high fidelity model, both in the number of equations and simulation time are shown. The numerical experiments and results demonstrate the efficacy of the proposed hybrid modeling approach.
Deep Learning Approach to Diabetic Retinopathy Detection
Authors: Borys Tymchenko, Philip Marchenko, Dmitry Spodarets
Date: 2020-03-03 00:00:00
Tasks: Deep Learning, Diabetic Retinopathy Detection, Specificity, Transfer Learning

Diabetic retinopathy is one of the most threatening complications of diabetes that leads to permanent blindness if left untreated. One of the essential challenges is early detection, which is very important for treatment success. Unfortunately, the exact identification of the diabetic retinopathy stage is notoriously tricky and requires expert human interpretation of fundus images. Simplification of the detection step is crucial and can help millions of people. Convolutional neural networks (CNN) have been successfully applied in many adjacent subjects, and for diagnosis of diabetic retinopathy itself. However, the high cost of big labeled datasets, as well as inconsistency between different doctors, impede the performance of these methods. In this paper, we propose an automatic deep-learning-based method for stage detection of diabetic retinopathy by single photography of the human fundus. Additionally, we propose the multistage approach to transfer learning, which makes use of similar datasets with different labeling. The presented method can be used as a screening method for early detection of diabetic retinopathy with sensitivity and specificity of 0.99 and is ranked 54 of 2943 competing methods (quadratic weighted kappa score of 0.925466) on APTOS 2019 Blindness Detection Dataset (13000 images).
Deep Learning in Memristive Nanowire Networks
Authors: Jack D. Kendall, Ross D. Pantone, Juan C. Nino
Date: 2020-03-03 00:00:00
Tasks: Deep Learning

Analog crossbar architectures for accelerating neural network training and inference have made tremendous progress over the past several years. These architectures are ideal for dense layers with fewer than roughly a thousand neurons. However, for large sparse layers, crossbar architectures are highly inefficient. A new hardware architecture, dubbed the MN3 (Memristive Nanowire Neural Network), was recently described as an efficient architecture for simulating very wide, sparse neural network layers, on the order of millions of neurons per layer. The MN3 utilizes a high-density memristive nanowire mesh to efficiently connect large numbers of silicon neurons with modifiable weights. Here, in order to explore the MN3's ability to function as a deep neural network, we describe one algorithm for training deep MN3 models and benchmark simulations of the architecture on two deep learning tasks. We utilize a simple piecewise linear memristor model, since we seek to demonstrate that training is, in principle, possible for randomized nanowire architectures. In future work, we intend on utilizing more realistic memristor models, and we will adapt the presented algorithm appropriately. We show that the MN3 is capable of performing composition, gradient propagation, and weight updates, which together allow it to function as a deep neural network. We show that a simulated multilayer perceptron (MLP), built from MN3 networks, can obtain a 1.61% error rate on the popular MNIST dataset, comparable to equivalently sized software-based network. This work represents, to the authors' knowledge, the first randomized nanowire architecture capable of reproducing the backpropagation algorithm.
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks
Authors: Juno V. Saraiva, Iran M. Braga Jr., Victor F. Monteiro, F. Rafael M. Lima, Tarcisio F. Maciel, Walter C. Freitas Jr., F. Rodrigo P. Cavalcanti
Date: 2020-03-03 00:00:00
Tasks: Deep Reinforcement Learning, Distributed Optimization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this article, we study a Radio Resource Allocation (RRA) that was formulated as a non-convex optimization problem whose main aim is to maximize the spectral efficiency subject to satisfaction guarantees in multiservice wireless systems. This problem has already been previously investigated in the literature and efficient heuristics have been proposed. However, in order to assess the performance of Machine Learning (ML) algorithms when solving optimization problems in the context of RRA, we revisit that problem and propose a solution based on a Reinforcement Learning (RL) framework. Specifically, a distributed optimization method based on multi-agent deep RL is developed, where each agent makes its decisions to find a policy by interacting with the local environment, until reaching convergence. Thus, this article focuses on an application of RL and our main proposal consists in a new deep RL based approach to jointly deal with RRA, satisfaction guarantees and Quality of Service (QoS) constraints in multiservice celular networks. Lastly, through computational simulations we compare the state-of-art solutions of the literature with our proposal and we show a near optimal performance of the latter in terms of throughput and outage rate.
Watch and learn -- a generalized approach for transferrable learning in deep neural networks via physical principles
Authors: Kyle Sprague, Juan Carrasquilla, Steve Whitelam, Isaac Tamblyn
Date: 2020-03-03 00:00:00
Tasks: Transfer Learning

Transfer learning refers to the use of knowledge gained while solving a machine learning task and applying it to the solution of a closely related problem. Such an approach has enabled scientific breakthroughs in computer vision and natural language processing where the weights learned in state-of-the-art models can be used to initialize models for other tasks which dramatically improve their performance and save computational time. Here we demonstrate an unsupervised learning approach augmented with basic physical principles that achieves fully transferrable learning for problems in statistical physics across different physical regimes. By coupling a sequence model based on a recurrent neural network to an extensive deep neural network, we are able to learn the equilibrium probability distributions and inter-particle interaction models of classical statistical mechanical systems. Our approach, distribution-consistent learning, DCL, is a general strategy that works for a variety of canonical statistical mechanical models (Ising and Potts) as well as disordered (spin-glass) interaction potentials. Using data collected from a single set of observation conditions, DCL successfully extrapolates across all temperatures, thermodynamic phases, and can be applied to different length-scales. This constitutes a fully transferrable physics-based learning in a generalizable approach.
Learning in the Sky: An Efficient 3D Placement of UAVs
Authors: Atefeh Hajijamali Arani, M. Mahdi Azari, William Melek, Safieddin Safavi-Naeini
Date: 2020-03-02 00:00:00

Deployment of unmanned aerial vehicles (UAVs) as aerial base stations can deliver a fast and flexible solution for serving varying traffic demand. In order to adequately benefit of UAVs deployment, their efficient placement is of utmost importance, and requires to intelligently adapt to the environment changes. In this paper, we propose a learning-based mechanism for the three-dimensional deployment of UAVs assisting terrestrial cellular networks in the downlink. The problem is modeled as a non-cooperative game among UAVs in satisfaction form. To solve the game, we utilize a low complexity algorithm, in which unsatisfied UAVs update their locations based on a learning algorithm. Simulation results reveal that the proposed UAV placement algorithm yields significant performance gains up to about 52% and 74% in terms of throughput and the number of dropped users, respectively, compared to an optimized baseline algorithm.
Learning-Based Link Scheduling in Millimeter-wave Multi-connectivity Scenarios
Authors: Cristian Tatino, Nikolaos Pappas, Ilaria Malanchini, Lutz Ewe, Di Yuan
Date: 2020-03-02 00:00:00
Tasks: Scheduling

Multi-connectivity is emerging as a promising solution to provide reliable communications and seamless connectivity for the millimeter-wave frequency range. Due to the blockage sensitivity at such high frequencies, connectivity with multiple cells can drastically increase the network performance in terms of throughput and reliability. However, an inefficient link scheduling, i.e., over and under-provisioning of connections, can lead either to high interference and energy consumption or to unsatisfied user's quality of service (QoS) requirements. In this work, we present a learning-based solution that is able to learn and then to predict the optimal link scheduling to satisfy users' QoS requirements while avoiding communication interruptions. Moreover, we compare the proposed approach with two base line methods and the genie-aided link scheduling that assumes perfect channel knowledge. We show that the learning-based solution approaches the optimum and outperforms the base line methods.
PPMC RL Training Algorithm: Rough Terrain Intelligent Robots through Reinforcement Learning
Authors: Tamir Blum, Kazuya Yoshida
Date: 2020-03-02 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Robots can now learn how to make decisions and control themselves, generalizing learned behaviors to unseen scenarios. In particular, AI powered robots show promise in rough environments like the lunar surface, due to the environmental uncertainties. We address this critical generalization aspect for robot locomotion in rough terrain through a training algorithm we have created called the Path Planning and Motion Control (PPMC) Training Algorithm. This algorithm is coupled with any generic reinforcement learning algorithm to teach robots how to respond to user commands and to travel to designated locations on a single neural network. In this paper, we show that the algorithm works independent of the robot structure, demonstrating that it works on a wheeled rover in addition the past results on a quadruped walking robot. Further, we take several big steps towards real world practicality by introducing a rough highly uneven terrain. Critically, we show through experiments that the robot learns to generalize to new rough terrain maps, retaining a 100% success rate. To the best of our knowledge, this is the first paper to introduce a generic training algorithm teaching generalized PPMC in rough environments to any robot, with just the use of reinforcement learning.
Multi-Scale Neural network for EEG Representation Learning in BCI
Authors: Wonjun Ko, Eunjin Jeon, Seungwoo Jeong, Heung-Il Suk
Date: 2020-03-02 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Representation Learning

Recent advances in deep learning have had a methodological and practical impact on brain-computer interface research. Among the various deep network architectures, convolutional neural networks have been well suited for spatio-spectral-temporal electroencephalogram signal representation learning. Most of the existing CNN-based methods described in the literature extract features at a sequential level of abstraction with repetitive nonlinear operations and involve densely connected layers for classification. However, studies in neurophysiology have revealed that EEG signals carry information in different ranges of frequency components. To better reflect these multi-frequency properties in EEGs, we propose a novel deep multi-scale neural network that discovers feature representations in multiple frequency/time ranges and extracts relationships among electrodes, i.e., spatial representations, for subject intention/condition identification. Furthermore, by completely representing EEG signals with spatio-spectral-temporal information, the proposed method can be utilized for diverse paradigms in both active and passive BCIs, contrary to existing methods that are primarily focused on single-paradigm BCIs. To demonstrate the validity of our proposed method, we conducted experiments on various paradigms of active/passive BCI datasets. Our experimental results demonstrated that the proposed method achieved performance improvements when judged against comparable state-of-the-art methods. Additionally, we analyzed the proposed method using different techniques, such as PSD curves and relevance score inspection to validate the multi-scale EEG signal information capturing ability, activation pattern maps for investigating the learned spatial filters, and t-SNE plotting for visualizing represented features. Finally, we also demonstrated our method's application to real-world problems.
Machine Learning for Predictive Deployment of UAVs with Multiple Access
Authors: Linyan Lu, Zhaohui Yang, Mingzhe Chen, Zelin Zang, Mohammad Shikh-Bahaei
Date: 2020-03-02 00:00:00
Tasks: BIG-bench Machine Learning, Traffic Prediction

In this paper, a machine learning based deployment framework of unmanned aerial vehicles (UAVs) is studied. In the considered model, UAVs are deployed as flying base stations (BS) to offload heavy traffic from ground BSs. Due to time-varying traffic distribution, a long short-term memory (LSTM) based prediction algorithm is introduced to predict the future cellular traffic. To predict the user service distribution, a KEG algorithm, which is a joint K-means and expectation maximization (EM) algorithm based on Gaussian mixture model (GMM), is proposed for determining the service area of each UAV. Based on the predicted traffic, the optimal UAV positions are derived and three multi-access techniques are compared so as to minimize the total transmit power. Simulation results show that the proposed method can reduce up to 24\% of the total power consumption compared to the conventional method without traffic prediction. Besides, rate splitting multiple access (RSMA) has the lower required transmit power compared to frequency domain multiple access (FDMA) and time domain multiple access (TDMA).
$MC^2RAM$: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian Inference
Authors: Priyesh Shukla, Ahish Shylendra, Theja Tulabandhula, Amit Ranjan Trivedi
Date: 2020-02-28 00:00:00
Tasks: Bayesian Inference

This work discusses the implementation of Markov Chain Monte Carlo (MCMC) sampling from an arbitrary Gaussian mixture model (GMM) within SRAM. We show a novel architecture of SRAM by embedding it with random number generators (RNGs), digital-to-analog converters (DACs), and analog-to-digital converters (ADCs) so that SRAM arrays can be used for high performance Metropolis-Hastings (MH) algorithm-based MCMC sampling. Most of the expensive computations are performed within the SRAM and can be parallelized for high speed sampling. Our iterative compute flow minimizes data movement during sampling. We characterize power-performance trade-off of our design by simulating on 45 nm CMOS technology. For a two-dimensional, two mixture GMM, the implementation consumes ~ 91 micro-Watts power per sampling iteration and produces 500 samples in 2000 clock cycles on an average at 1 GHz clock frequency. Our study highlights interesting insights on how low-level hardware non-idealities can affect high-level sampling characteristics, and recommends ways to optimally operate SRAM within area/power constraints for high performance sampling.
SentenceMIM: A Latent Variable Language Model
Authors: Micha Livne, Kevin Swersky, David J. Fleet
Date: 2020-02-18 00:00:00
Tasks: Language Modeling, Language Modelling, model, Question Answering, Transfer Learning

SentenceMIM is a probabilistic auto-encoder for language data, trained with Mutual Information Machine (MIM) learning to provide a fixed length representation of variable length language observations (i.e., similar to VAE). Previous attempts to learn VAEs for language data faced challenges due to posterior collapse. MIM learning encourages high mutual information between observations and latent variables, and is robust against posterior collapse. As such, it learns informative representations whose dimension can be an order of magnitude higher than existing language VAEs. Importantly, the SentenceMIM loss has no hyper-parameters, simplifying optimization. We compare sentenceMIM with VAE, and AE on multiple datasets. SentenceMIM yields excellent reconstruction, comparable to AEs, with a rich structured latent space, comparable to VAEs. The structured latent representation is demonstrated with interpolation between sentences of different lengths. We demonstrate the versatility of sentenceMIM by utilizing a trained model for question-answering and transfer learning, without fine-tuning, outperforming VAE and AE with similar architectures.