pwc-archive/papers-with-abstracts huggingface dataset:
Vox-Surf: Voxel-based Implicit Surface Representation
Authors: Hai Li, Xingrui Yang, Hongjia Zhai, Yuqian Liu, Hujun Bao, Guofeng Zhang
Date: 2022-08-21 00:00:00
Tasks: valid

Virtual content creation and interaction play an important role in modern 3D applications such as AR and VR. Recovering detailed 3D models from real scenes can significantly expand the scope of its applications and has been studied for decades in the computer vision and computer graphics community. We propose Vox-Surf, a voxel-based implicit surface representation. Our Vox-Surf divides the space into finite bounded voxels. Each voxel stores geometry and appearance information in its corner vertices. Vox-Surf is suitable for almost any scenario thanks to sparsity inherited from voxel representation and can be easily trained from multiple view images. We leverage the progressive training procedure to extract important voxels gradually for further optimization so that only valid voxels are preserved, which greatly reduces the number of sampling points and increases rendering speed.The fine voxels can also be considered as the bounding volume for collision detection.The experiments show that Vox-Surf representation can learn delicate surface details and accurate color with less memory and faster rendering speed than other methods.We also show that Vox-Surf can be more practical in scene editing and AR applications.
Memristive Computing for Efficient Inference on Resource Constrained Devices
Authors: Venkatesh Rammamoorthy, Geng Zhao, Bharathi Reddy, Ming-Yang Lin
Date: 2022-08-21 00:00:00
Tasks: Deep Learning

The advent of deep learning has resulted in a number of applications which have transformed the landscape of the research area in which it has been applied. However, with an increase in popularity, the complexity of classical deep neural networks has increased over the years. As a result, this has leads to considerable problems during deployment on devices with space and time constraints. In this work, we perform a review of the present advancements in non-volatile memory and how the use of resistive RAM memory, particularly memristors, can help to progress the state of research in deep learning. In other words, we wish to present an ideology that advances in the field of memristive technology can greatly influence and impact deep learning inference on edge devices.
A Stochastic Variance Reduced Gradient using Barzilai-Borwein Techniques as Second Order Information
Authors: Hardik Tankaria, Nobuo Yamashita
Date: 2022-08-23 00:00:00

In this paper, we consider to improve the stochastic variance reduce gradient (SVRG) method via incorporating the curvature information of the objective function. We propose to reduce the variance of stochastic gradients using the computationally efficient Barzilai-Borwein (BB) method by incorporating it into the SVRG. We also incorporate a BB-step size as its variant. We prove its linear convergence theorem that works not only for the proposed method but also for the other existing variants of SVRG with second-order information. We conduct the numerical experiments on the benchmark datasets and show that the proposed method with constant step size performs better than the existing variance reduced methods for some test problems.
Multi-Modal Representation Learning with Self-Adaptive Threshold for Commodity Verification
Authors: Chenchen Han, Heng Jia
Date: 2022-08-23 00:00:00
Tasks: Representation Learning

In this paper, we propose a method to identify identical commodities. In e-commerce scenarios, commodities are usually described by both images and text. By definition, identical commodities are those that have identical key attributes and are cognitively identical to consumers. There are two main challenges: 1) The extraction and fusion of multi-modal representation. 2) The ability to verify identical commodities by comparing the similarity between representations and a threshold. To address the above problems, we propose an end-to-end multi-modal representation learning method with self-adaptive threshold. We use a dual-stream network to extract multi-modal commodity embeddings and threshold embeddings separately and then concatenate them to obtain commodity representation. Our method is able to adaptively adjust the threshold according to different commodities while maintaining the indexability of the commodity representation space. We experimentally validate the advantages of self-adaptive threshold and the effectiveness of multimodal representation fusion. Besides, our method achieves third place with an F1 score of 0.8936 on the second task of the CCKS-2022 Knowledge Graph Evaluation for Digital Commerce Competition. Code and pretrained models are available at https://github.com/hanchenchen/CCKS2022-track2-solution.
Large-Scale Traffic Congestion Prediction based on Multimodal Fusion and Representation Mapping
Authors: Bodong Zhou, Jiahui Liu, Songyi Cui, Yaping Zhao
Date: 2022-08-23 00:00:00

With the progress of the urbanisation process, the urban transportation system is extremely critical to the development of cities and the quality of life of the citizens. Among them, it is one of the most important tasks to judge traffic congestion by analysing the congestion factors. Recently, various traditional and machine-learning-based models have been introduced for predicting traffic congestion. However, these models are either poorly aggregated for massive congestion factors or fail to make accurate predictions for every precise location in large-scale space. To alleviate these problems, a novel end-to-end framework based on convolutional neural networks is proposed in this paper. With learning representations, the framework proposes a novel multimodal fusion module and a novel representation mapping module to achieve traffic congestion predictions on arbitrary query locations on a large-scale map, combined with various global reference information. The proposed framework achieves significant results and efficient inference on real-world large-scale datasets.
Exponential concentration in quantum kernel methods
Authors: Supanut Thanasilp, Samson Wang, M. Cerezo, Zoë Holmes
Date: 2022-08-23 00:00:00
Tasks: Quantum Machine Learning

Kernel methods in Quantum Machine Learning (QML) have recently gained significant attention as a potential candidate for achieving a quantum advantage in data analysis. Among other attractive properties, when training a kernel-based model one is guaranteed to find the optimal model's parameters due to the convexity of the training landscape. However, this is based on the assumption that the quantum kernel can be efficiently obtained from quantum hardware. In this work we study the performance of quantum kernel models from the perspective of the resources needed to accurately estimate kernel values. We show that, under certain conditions, values of quantum kernels over different input data can be exponentially concentrated (in the number of qubits) towards some fixed value. Thus on training with a polynomial number of measurements, one ends up with a trivial model where the predictions on unseen inputs are independent of the input data. We identify four sources that can lead to concentration including: expressivity of data embedding, global measurements, entanglement and noise. For each source, an associated concentration bound of quantum kernels is analytically derived. Lastly, we show that when dealing with classical data, training a parametrized data embedding with a kernel alignment method is also susceptible to exponential concentration. Our results are verified through numerical simulations for several QML tasks. Altogether, we provide guidelines indicating that certain features should be avoided to ensure the efficient evaluation of quantum kernels and so the performance of quantum kernel methods.
Enhancement Encoding: A Novel Imbalanced Classification Approach via Encoding the Training Labels
Authors: Jia-Chen Zhao
Date: 2022-08-23 00:00:00
Tasks: imbalanced classification

Class imbalance, which is also called long-tailed distribution, is a common problem in classification tasks based on machine learning. If it happens, the minority data will be overwhelmed by the majority, which presents quite a challenge for data science. To address the class imbalance problem, researchers have proposed lots of methods: some people make the data set balanced (SMOTE), some others refine the loss function (Focal Loss), and even someone has noticed the value of labels influences class-imbalanced learning (Yang and Xu. Rethinking the value of labels for improving class-imbalanced learning. In NeurIPS 2020), but no one changes the way to encode the labels of data yet. Nowadays, the most prevailing technique to encode labels is the one-hot encoding due to its nice performance in the general situation. However, it is not a good choice for imbalanced data, because the classifier will treat majority and minority samples equally. In this paper, we innovatively propose the enhancement encoding technique, which is specially designed for the imbalanced classification. The enhancement encoding combines re-weighting and cost-sensitiveness, which can reflect the difference between hard and easy (or minority and majority) classes. To reduce the number of validation samples and the computation cost, we also replace the confusion matrix with a novel soft-confusion matrix which works better with a small validation set. In the experiments, we evaluate the enhancement encoding with three different types of loss. And the results show that enhancement encoding is very effective to improve the performance of the network trained with imbalanced data. Particularly, the performance on minority classes is much better.
Strategic Decision-Making in the Presence of Information Asymmetry: Provably Efficient RL with Algorithmic Instruments
Authors: Mengxin Yu, Zhuoran Yang, Jianqing Fan
Date: 2022-08-23 00:00:00
Tasks: Decision Making, Offline RL, Recommendation Systems, regression, valid

We study offline reinforcement learning under a novel model called strategic MDP, which characterizes the strategic interactions between a principal and a sequence of myopic agents with private types. Due to the bilevel structure and private types, strategic MDP involves information asymmetry between the principal and the agents. We focus on the offline RL problem, where the goal is to learn the optimal policy of the principal concerning a target population of agents based on a pre-collected dataset that consists of historical interactions. The unobserved private types confound such a dataset as they affect both the rewards and observations received by the principal. We propose a novel algorithm, Pessimistic policy Learning with Algorithmic iNstruments (PLAN), which leverages the ideas of instrumental variable regression and the pessimism principle to learn a near-optimal principal's policy in the context of general function approximation. Our algorithm is based on the critical observation that the principal's actions serve as valid instrumental variables. In particular, under a partial coverage assumption on the offline dataset, we prove that PLAN outputs a $1 / \sqrt{K}$-optimal policy with $K$ being the number of collected trajectories. We further apply our framework to some special cases of strategic MDP, including strategic regression, strategic bandit, and noncompliance in recommendation systems.
Link prediction with continuous-time classical and quantum walks
Authors: Mark Goldsmith, Guillermo García-Pérez, Joonas Malmi, Matteo A. C. Rossi, Harto Saarinen, Sabrina Maniscalco
Date: 2022-08-23 00:00:00
Tasks: Link Prediction, Prediction

Protein-protein interaction (PPI) networks consist of the physical and/or functional interactions between the proteins of an organism. Since the biophysical and high-throughput methods used to form PPI networks are expensive, time-consuming, and often contain inaccuracies, the resulting networks are usually incomplete. In order to infer missing interactions in these networks, we propose a novel class of link prediction methods based on continuous-time classical and quantum random walks. In the case of quantum walks, we examine the usage of both the network adjacency and Laplacian matrices for controlling the walk dynamics. We define a score function based on the corresponding transition probabilities and perform tests on four real-world PPI datasets. Our results show that continuous-time classical random walks and quantum walks using the network adjacency matrix can successfully predict missing protein-protein interactions, with performance rivalling the state of the art.
Convex mixed-integer optimization with Frank-Wolfe methods
Authors: Deborah Hendrych, Hannah Troppens, Mathieu Besançon, Sebastian Pokutta
Date: 2022-08-23 00:00:00

Mixed-integer nonlinear optimization encompasses a broad class of problems that present both theoretical and computational challenges. We propose a new type of method to solve these problems based on a branch-and-bound algorithm with convex node relaxations. These relaxations are solved with a Frank-Wolfe algorithm over the convex hull of mixed-integer feasible points instead of the continuous relaxation via calls to a mixed-integer linear solver as the linear minimization oracle. The proposed method computes feasible solutions while working on a single representation of the polyhedral constraints, leveraging the full extent of mixed-integer linear solvers without an outer approximation scheme and can exploit inexact solutions of node subproblems.
Learning linear modules in a dynamic network with missing node observations
Authors: Karthik R. Ramaswamy, Giulio Bottegal, Paul M. J. Van den Hof
Date: 2022-08-23 00:00:00
Tasks: Data Augmentation, Gaussian Processes

In order to identify a system (module) embedded in a dynamic network, one has to formulate a multiple-input estimation problem that necessitates certain nodes to be measured and included as predictor inputs. However, some of these nodes may not be measurable in many practical cases due to sensor selection and placement issues. This may result in biased estimates of the target module. Furthermore, the identification problem associated with the multiple-input structure may require determining a large number of parameters that are not of particular interest to the experimenter, with increased computational complexity in large-sized networks. In this paper, we tackle these problems by using a data augmentation strategy that allows us to reconstruct the missing node measurements and increase the accuracy of the estimated target module. To this end, we develop a system identification method using regularized kernel-based methods coupled with approximate inference methods. Keeping a parametric model for the module of interest, we model the other modules as Gaussian Processes (GP) with a kernel given by the so-called stable spline kernel. An Empirical Bayes (EB) approach is used to estimate the parameters of the target module. The related optimization problem is solved using an Expectation-Maximization (EM) method, where we employ a Markov-chain Monte Carlo (MCMC) technique to reconstruct the unknown missing node information and the network dynamics. Numerical simulations on dynamic network examples illustrate the potentials of the developed method.
Application of federated learning techniques for arrhythmia classification using 12-lead ECG signals
Authors: Daniel Mauricio Jimenez Gutierrez, Hafiz Muuhammad Hassan, Lorella Landi, Andrea Vitaletti, Ioannis Chatzigiannakis
Date: 2022-08-23 00:00:00
Tasks: Decision Making, Electrocardiography (ECG), Feature Engineering, Federated Learning, Privacy Preserving

Artificial Intelligence-based (AI) analysis of large, curated medical datasets is promising for providing early detection, faster diagnosis, and more effective treatment using low-power Electrocardiography (ECG) monitoring devices information. However, accessing sensitive medical data from diverse sources is highly restricted since improper use, unsafe storage, or data leakage could violate a person's privacy. This work uses a Federated Learning (FL) privacy-preserving methodology to train AI models over heterogeneous sets of high-definition ECG from 12-lead sensor arrays collected from six heterogeneous sources. We evaluated the capacity of the resulting models to achieve equivalent performance compared to state-of-the-art models trained in a Centralized Learning (CL) fashion. Moreover, we assessed the performance of our solution over Independent and Identical distributed (IID) and non-IID federated data. Our methodology involves machine learning techniques based on Deep Neural Networks and Long-Short-Term Memory models. It has a robust data preprocessing pipeline with feature engineering, selection, and data balancing techniques. Our AI models demonstrated comparable performance to models trained using CL, IID, and non-IID approaches. They showcased advantages in reduced complexity and faster training time, making them well-suited for cloud-edge architectures.
Causal Entropy Optimization
Authors: Nicola Branchini, Virginia Aglietti, Neil Dhir, Theodoros Damoulas
Date: 2022-08-23 00:00:00
Tasks: Bayesian Optimization

We study the problem of globally optimizing the causal effect on a target variable of an unknown causal graph in which interventions can be performed. This problem arises in many areas of science including biology, operations research and healthcare. We propose Causal Entropy Optimization (CEO), a framework that generalizes Causal Bayesian Optimization (CBO) to account for all sources of uncertainty, including the one arising from the causal graph structure. CEO incorporates the causal structure uncertainty both in the surrogate models for the causal effects and in the mechanism used to select interventions via an information-theoretic acquisition function. The resulting algorithm automatically trades-off structure learning and causal effect optimization, while naturally accounting for observation noise. For various synthetic and real-world structural causal models, CEO achieves faster convergence to the global optimum compared with CBO while also learning the graph. Furthermore, our joint approach to structure learning and causal optimization improves upon sequential, structure-learning-first approaches.
Regularized impurity reduction: Accurate decision trees with complexity guarantees
Authors: Guangyi Zhang, Aristides Gionis
Date: 2022-08-23 00:00:00

Decision trees are popular classification models, providing high accuracy and intuitive explanations. However, as the tree size grows the model interpretability deteriorates. Traditional tree-induction algorithms, such as C4.5 and CART, rely on impurity-reduction functions that promote the discriminative power of each split. Thus, although these traditional methods are accurate in practice, there has been no theoretical guarantee that they will produce small trees. In this paper, we justify the use of a general family of impurity functions, including the popular functions of entropy and Gini-index, in scenarios where small trees are desirable, by showing that a simple enhancement can equip them with complexity guarantees. We consider a general setting, where objects to be classified are drawn from an arbitrary probability distribution, classification can be binary or multi-class, and splitting tests are associated with non-uniform costs. As a measure of tree complexity, we adopt the expected cost to classify an object drawn from the input distribution, which, in the uniform-cost case, is the expected number of tests. We propose a tree-induction algorithm that gives a logarithmic approximation guarantee on the tree complexity. This approximation factor is tight up to a constant factor under mild assumptions. The algorithm recursively selects a test that maximizes a greedy criterion defined as a weighted sum of three components. The first two components encourage the selection of tests that improve the balance and the cost-efficiency of the tree, respectively, while the third impurity-reduction component encourages the selection of more discriminative tests. As shown in our empirical evaluation, compared to the original heuristics, the enhanced algorithms strike an excellent balance between predictive accuracy and tree complexity.
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning
Authors: Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert
Date: 2022-08-23 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Multi-Armed Bandits, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Thompson Sampling

Thompson Sampling is one of the most effective methods for contextual bandits and has been generalized to posterior sampling for certain MDP settings. However, existing posterior sampling methods for reinforcement learning are limited by being model-based or lack worst-case theoretical guarantees beyond linear MDPs. This paper proposes a new model-free formulation of posterior sampling that applies to more general episodic reinforcement learning problems with theoretical guarantees. We introduce novel proof techniques to show that under suitable conditions, the worst-case regret of our posterior sampling method matches the best known results of optimization based methods. In the linear MDP setting with dimension, the regret of our algorithm scales linearly with the dimension as compared to a quadratic dependence of the existing posterior sampling-based exploration algorithms.
Transfer Learning Application of Self-supervised Learning in ARPES
Authors: Sandy Adhitia Ekahana, Genta Indra Winata, Y. Soh, Gabriel Aeppli, Radovic Milan, Ming Shi
Date: 2022-08-23 00:00:00
Tasks: Few-Shot Learning, Self-Supervised Learning, Transfer Learning

Recent development in angle-resolved photoemission spectroscopy (ARPES) technique involves spatially resolving samples while maintaining the high-resolution feature of momentum space. This development easily expands the data size and its complexity for data analysis, where one of it is to label similar dispersion cuts and map them spatially. In this work, we demonstrate that the recent development in representational learning (self-supervised learning) model combined with k-means clustering can help automate that part of data analysis and save precious time, albeit with low performance. Finally, we introduce a few-shot learning (k-nearest neighbour or kNN) in representational space where we selectively choose one (k=1) image reference for each known label and subsequently label the rest of the data with respect to the nearest reference image. This last approach demonstrates the strength of the self-supervised learning to automate the image analysis in ARPES in particular and can be generalized into any science data analysis that heavily involves image data.