pwc-archive/papers-with-abstracts huggingface dataset:
Policy-Guided Heuristic Search with Guarantees
Authors: Laurent Orseau, Levi H. S. Lelis
Date: 2021-03-21 00:00:00
Tasks: Heuristic Search, Sokoban

The use of a policy and a heuristic function for guiding search can be quite effective in adversarial problems, as demonstrated by AlphaGo and its successors, which are based on the PUCT search algorithm. While PUCT can also be used to solve single-agent deterministic problems, it lacks guarantees on its search effort and it can be computationally inefficient in practice. Combining the A* algorithm with a learned heuristic function tends to work better in these domains, but A* and its variants do not use a policy. Moreover, the purpose of using A* is to find solutions of minimum cost, while we seek instead to minimize the search loss (e.g., the number of search steps). LevinTS is guided by a policy and provides guarantees on the number of search steps that relate to the quality of the policy, but it does not make use of a heuristic function. In this work we introduce Policy-guided Heuristic Search (PHS), a novel search algorithm that uses both a heuristic function and a policy and has theoretical guarantees on the search loss that relates to both the quality of the heuristic and of the policy. We show empirically on the sliding-tile puzzle, Sokoban, and a puzzle from the commercial game `The Witness' that PHS enables the rapid learning of both a policy and a heuristic function and compares favorably with A*, Weighted A*, Greedy Best-First Search, LevinTS, and PUCT in terms of number of problems solved and search time in all three domains tested.
UCB-based Algorithms for Multinomial Logistic Regression Bandits
Authors: Sanae Amani, Christos Thrampoulidis
Date: 2021-03-21 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: regression

Out of the rich family of generalized linear bandits, perhaps the most well studied ones are logisitc bandits that are used in problems with binary rewards: for instance, when the learner/agent tries to maximize the profit over a user that can select one of two possible outcomes (e.g., `click' vs `no-click'). Despite remarkable recent progress and improved algorithms for logistic bandits, existing works do not address practical situations where the number of outcomes that can be selected by the user is larger than two (e.g., `click', `show me later', `never show again', `no click'). In this paper, we study such an extension. We use multinomial logit (MNL) to model the probability of each one of $K+1\geq 2$ possible outcomes (+1 stands for the `not click' outcome): we assume that for a learner's action $\mathbf{x}_t$, the user selects one of $K+1\geq 2$ outcomes, say outcome $i$, with a multinomial logit (MNL) probabilistic model with corresponding unknown parameter $\bar{\boldsymbol\theta}_{\ast i}$. Each outcome $i$ is also associated with a revenue parameter $\rho_i$ and the goal is to maximize the expected revenue. For this problem, we present MNL-UCB, an upper confidence bound (UCB)-based algorithm, that achieves regret $\tilde{\mathcal{O}}(dK\sqrt{T})$ with small dependency on problem-dependent constants that can otherwise be arbitrarily large and lead to loose regret bounds. We present numerical simulations that corroborate our theoretical results.
The Discovery of Dynamics via Linear Multistep Methods and Deep Learning: Error Estimation
Authors: Qiang Du, Yiqi Gu, Haizhao Yang, Chao Zhou
Date: 2021-03-21 00:00:00

Identifying hidden dynamics from observed data is a significant and challenging task in a wide range of applications. Recently, the combination of linear multistep methods (LMMs) and deep learning has been successfully employed to discover dynamics, whereas a complete convergence analysis of this approach is still under development. In this work, we consider the deep network-based LMMs for the discovery of dynamics. We put forward error estimates for these methods using the approximation property of deep networks. It indicates, for certain families of LMMs, that the $\ell^2$ grid error is bounded by the sum of $O(h^p)$ and the network approximation error, where $h$ is the time step size and $p$ is the local truncation error order. Numerical results of several physically relevant examples are provided to demonstrate our theory.
Paying Attention to Activation Maps in Camera Pose Regression
Authors: Yoli Shavit, Ron Ferens, Yosi Keller
Date: 2021-03-21 00:00:00
Tasks: Image Retrieval, Position, regression, Retrieval

Camera pose regression methods apply a single forward pass to the query image to estimate the camera pose. As such, they offer a fast and light-weight alternative to traditional localization schemes based on image retrieval. Pose regression approaches simultaneously learn two regression tasks, aiming to jointly estimate the camera position and orientation using a single embedding vector computed by a convolutional backbone. We propose an attention-based approach for pose regression, where the convolutional activation maps are used as sequential inputs. Transformers are applied to encode the sequential activation maps as latent vectors, used for camera pose regression. This allows us to pay attention to spatially-varying deep features. Using two Transformer heads, we separately focus on the features for camera position and orientation, based on how informative they are per task. Our proposed approach is shown to compare favorably to contemporary pose regressors schemes and achieves state-of-the-art accuracy across multiple outdoor and indoor benchmarks. In particular, to the best of our knowledge, our approach is the only method to attain sub-meter average accuracy across outdoor scenes. We make our code publicly available from here.
Conditional Generative Adversarial Networks for Speed Control in Trajectory Simulation
Authors: Sahib Julka, Vishal Sowrirajan, Joerg Schloetterer, Michael Granitzer
Date: 2021-03-21 00:00:00
Tasks: Trajectory Prediction

Motion behaviour is driven by several factors -- goals, presence and actions of neighbouring agents, social relations, physical and social norms, the environment with its variable characteristics, and further. Most factors are not directly observable and must be modelled from context. Trajectory prediction, is thus a hard problem, and has seen increasing attention from researchers in the recent years. Prediction of motion, in application, must be realistic, diverse and controllable. In spite of increasing focus on multimodal trajectory generation, most methods still lack means for explicitly controlling different modes of the data generation. Further, most endeavours invest heavily in designing special mechanisms to learn the interactions in latent space. We present Conditional Speed GAN (CSG), that allows controlled generation of diverse and socially acceptable trajectories, based on user controlled speed. During prediction, CSG forecasts future speed from latent space and conditions its generation based on it. CSG is comparable to state-of-the-art GAN methods in terms of the benchmark distance metrics, while being simple and useful for simulation and data augmentation for different contexts such as fast or slow paced environments. Additionally, we compare the effect of different aggregation mechanisms and show that a naive approach of concatenation works comparable to its attention and pooling alternatives.
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
Authors: Ali Agha, Kyohei Otsu, Benjamin Morrell, David D. Fan, Rohan Thakker, Angel Santamaria-Navarro, Sung-Kyun Kim, Amanda Bouman, Xianmei Lei, Jeffrey Edlund, Muhammad Fadhil Ginting, Kamak Ebadi, Matthew Anderson, Torkom Pailevanian, Edward Terry, Michael Wolf, Andrea Tagliabue, Tiago Stegun Vaquero, Matteo Palieri, Scott Tepsuporn, Yun Chang, Arash Kalantari, Fernando Chavez, Brett Lopez, Nobuhiro Funabiki, Gregory Miles, Thomas Touma, Alessandro Buscicchio, Jesus Tordesillas, Nikhilesh Alatur, Jeremy Nash, William Walsh, Sunggoo Jung, Hanseob Lee, Christoforos Kanellakis, John Mayo, Scott Harper, Marcel Kaufmann, Anushri Dixit, Gustavo Correa, Carlyn Lee, Jay Gao, Gene Merewether, Jairo Maldonado-Contreras, Gautam Salhotra, Maira Saboia Da Silva, Benjamin Ramtoula, Yuki Kubo, Seyed Fakoorian, Alexander Hatteland, Taeyeon Kim, Tara Bartlett, Alex Stephens, Leon Kim, Chuck Bergh, Eric Heiden, Thomas Lew, Abhishek Cauligi, Tristan Heywood, Andrew Kramer, Henry A. Leopold, Chris Choi, Shreyansh Daftry, Olivier Toupet, Inhwan Wee, Abhishek Thakur, Micah Feras, Giovanni Beltrame, George Nikolakopoulos, David Shim, Luca Carlone, Joel Burdick
Date: 2021-03-21 00:00:00
Tasks: Decision Making, Motion Planning

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved 2nd and 1st place, respectively. We also discuss CoSTAR's demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including: (i) geometric and semantic environment mapping; (ii) a multi-modal positioning system; (iii) traversability analysis and local planning; (iv) global motion planning and exploration behavior; (i) risk-aware mission planning; (vi) networking and decentralized reasoning; and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g. wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.
Learning Multi-Scene Absolute Pose Regression with Transformers
Authors: Yoli Shavit, Ron Ferens, Yosi Keller
Date: 2021-03-21 00:00:00
Proceeding: ICCV 2021 10
Tasks: regression, Visual Localization

Absolute camera pose regressors estimate the position and orientation of a camera from the captured image alone. Typically, a convolutional backbone with a multi-layer perceptron head is trained with images and pose labels to embed a single reference scene at a time. Recently, this scheme was extended for learning multiple scenes by replacing the MLP head with a set of fully connected layers. In this work, we propose to learn multi-scene absolute camera pose regression with Transformers, where encoders are used to aggregate activation maps with self-attention and decoders transform latent features and scenes encoding into candidate pose predictions. This mechanism allows our model to focus on general features that are informative for localization while embedding multiple scenes in parallel. We evaluate our method on commonly benchmarked indoor and outdoor datasets and show that it surpasses both multi-scene and state-of-the-art single-scene absolute pose regressors. We make our code publicly available from https://github.com/yolish/multi-scene-pose-transformer.
Robust Cell-Load Learning with a Small Sample Set
Authors: Daniyal Amir Awan, Renato L. G. Cavalcante, Slawomir Stanczak
Date: 2021-03-21 00:00:00

Learning of the cell-load in radio access networks (RANs) has to be performed within a short time period. Therefore, we propose a learning framework that is robust against uncertainties resulting from the need for learning based on a relatively small training sample set. To this end, we incorporate prior knowledge about the cell-load in the learning framework. For example, an inherent property of the cell-load is that it is monotonic in downlink (data) rates. To obtain additional prior knowledge we first study the feasible rate region, i.e., the set of all vectors of user rates that can be supported by the network. We prove that the feasible rate region is compact. Moreover, we show the existence of a Lipschitz function that maps feasible rate vectors to cell-load vectors. With these results in hand, we present a learning technique that guarantees a minimum approximation error in the worst-case scenario by using prior knowledge and a small training sample set. Simulations in the network simulator NS3 demonstrate that the proposed method exhibits better robustness and accuracy than standard multivariate learning techniques, especially for small training sample sets.
UAV Images Dataset for Moving Object Detection from Moving Cameras
Authors: Ibrahim Delibasoglu
Date: 2021-03-21 00:00:00
Tasks: Motion Detection, Moving Object Detection, Object, object-detection, Object Detection

This paper presents a new high resolution aerial images dataset in which moving objects are labelled manually. It aims to contribute to the evaluation of the moving object detection methods for moving cameras. The problem of recognizing moving objects from aerial images is one of the important issues in computer vision. The biggest problem in the images taken by UAV is that the background is constantly variable due to camera movement. There are various datasets in the literature in which proposed methods for motion detection are evaluated. Prepared dataset consists of challenging images containing small targets compared to other datasets. Two methods in the literature have been tested for the prepared dataset. In addition, a simpler method compared to these methods has been proposed for moving object object in this paper.
Support Vector Regression Parameters Optimization using Golden Sine Algorithm and its application in stock market
Authors: Mohammadreza Ghanbari, Mahdi Goldani
Date: 2021-03-21 00:00:00
Tasks: regression

Support vector machine modeling is a new approach in machine learning for classification showing good performance on forecasting problems of small samples and high dimensions. Later, it promoted to Support Vector Regression (SVR) for regression problems. A big challenge for achieving reliable is the choice of appropriate parameters. Here, a novel Golden sine algorithm (GSA) based SVR is proposed for proper selection of the parameters. For comparison, the performance of the proposed algorithm is compared with eleven other meta-heuristic algorithms on some historical stock prices of technological companies from Yahoo Finance website based on Mean Squared Error and Mean Absolute Percent Error. The results demonstrate that the given algorithm is efficient for tuning the parameters and is indeed competitive in terms of accuracy and computing time.
Set-Theoretic Learning for Detection in Cell-Less C-RAN Systems
Authors: Daniyal Amir Awan, Renato L. G. Cavalcante, Zoran Utkovski, Slawomir Stanczak
Date: 2021-03-21 00:00:00
Tasks: Quantization

Cloud-radio access network (C-RAN) can enable cell-less operation by connecting distributed remote radio heads (RRHs) via fronthaul links to a powerful central unit. In conventional C-RAN, baseband signals are forwarded after quantization/ compression to the central unit for centralized processing to keep the complexity of the RRHs low. However, the limited capacity of the fronthaul is thought to be a significant bottleneck in the ability of C-RAN to support large systems (e.g. massive machine-type communications (mMTC)). Therefore, in contrast to the conventional C-RAN, we propose a learning-based system in which the detection is performed locally at each RRH and only the likelihood information is conveyed to the CU. To this end, we develop a general set-theoretic learningmethod to estimate likelihood functions. The method can be used to extend existing detection methods to the C-RAN setting.
Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach
Authors: Rui Xie, Wei Ye, Jinan Sun, Shikun Zhang
Date: 2021-03-21 00:00:00
Tasks: Code Summarization, Decoder, Informativeness, Multi-Task Learning, Source Code Summarization

Code summaries are brief natural language descriptions of source code pieces. The main purpose of code summarization is to assist developers in understanding code and to reduce documentation workload. In this paper, we design a novel multi-task learning (MTL) approach for code summarization through mining the relationship between method code summaries and method names. More specifically, since a method's name can be considered as a shorter version of its code summary, we first introduce the tasks of generation and informativeness prediction of method names as two auxiliary training objectives for code summarization. A novel two-pass deliberation mechanism is then incorporated into our MTL architecture to generate more consistent intermediate states fed into a summary decoder, especially when informative method names do not exist. To evaluate our deliberation MTL approach, we carried out a large-scale experiment on two existing datasets for Java and Python. The experiment results show that our technique can be easily applied to many state-of-the-art neural models for code summarization and improve their performance. Meanwhile, our approach shows significant superiority when generating summaries for methods with non-informative names.
Traffic Camera Calibration via Vehicle Vanishing Point Detection
Authors: Viktor Kocur, Milan Ftáčnik
Date: 2021-03-21 00:00:00
Tasks: Camera Calibration

In this paper we propose a traffic surveillance camera calibration method based on detection of pairs of vanishing points associated with vehicles in the traffic surveillance footage. To detect the vanishing points we propose a CNN which outputs heatmaps in which the positions of vanishing points are represented using the diamond space parametrization which enables us to detect vanishing points from the whole infinite projective space. From the detected pairs of vanishing points for multiple vehicles in a scene we establish the scene geometry by estimating the focal length of the camera and the orientation of the road plane. We show that our method achieves competitive results on the BrnoCarPark dataset while having fewer requirements than the current state of the art approach.
Responsible AI: Gender bias assessment in emotion recognition
Authors: Artem Domnich, Gholamreza Anbarjafari
Date: 2021-03-21 00:00:00
Tasks: Emotion Recognition, Facial Expression Recognition, Facial Expression Recognition (FER), Fairness

Rapid development of artificial intelligence (AI) systems amplify many concerns in society. These AI algorithms inherit different biases from humans due to mysterious operational flow and because of that it is becoming adverse in usage. As a result, researchers have started to address the issue by investigating deeper in the direction towards Responsible and Explainable AI. Among variety of applications of AI, facial expression recognition might not be the most important one, yet is considered as a valuable part of human-AI interaction. Evolution of facial expression recognition from the feature based methods to deep learning drastically improve quality of such algorithms. This research work aims to study a gender bias in deep learning methods for facial expression recognition by investigating six distinct neural networks, training them, and further analysed on the presence of bias, according to the three definition of fairness. The main outcomes show which models are gender biased, which are not and how gender of subject affects its emotion recognition. More biased neural networks show bigger accuracy gap in emotion recognition between male and female test sets. Furthermore, this trend keeps for true positive and false positive rates. In addition, due to the nature of the research, we can observe which types of emotions are better classified for men and which for women. Since the topic of biases in facial expression recognition is not well studied, a spectrum of continuation of this research is truly extensive, and may comprise detail analysis of state-of-the-art methods, as well as targeting other biases.
A deep learning approach to data-driven model-free pricing and to martingale optimal transport
Authors: Ariel Neufeld, Julian Sester
Date: 2021-03-21 00:00:00

We introduce a novel and highly tractable supervised learning approach based on neural networks that can be applied for the computation of model-free price bounds of, potentially high-dimensional, financial derivatives and for the determination of optimal hedging strategies attaining these bounds. In particular, our methodology allows to train a single neural network offline and then to use it online for the fast determination of model-free price bounds of a whole class of financial derivatives with current market data. We show the applicability of this approach and highlight its accuracy in several examples involving real market data. Further, we show how a neural network can be trained to solve martingale optimal transport problems involving fixed marginal distributions instead of financial market data.
SEMIE: SEMantically Infused Embeddings with Enhanced Interpretability for Domain-specific Small Corpus
Authors: Rishabh Gupta, Rajesh N Rao
Date: 2021-03-21 00:00:00
Tasks: Word Embeddings

Word embeddings are a basic building block of modern NLP pipelines. Efforts have been made to learn rich, efficient, and interpretable embeddings for large generic datasets available in the public domain. However, these embeddings have limited applicability for small corpora from specific domains such as automotive, manufacturing, maintenance and support, etc. In this work, we present a comprehensive notion of interpretability for word embeddings and propose a novel method to generate highly interpretable and efficient embeddings for a domain-specific small corpus. We report the evaluation results of our resulting word embeddings and demonstrate their novel features for enhanced interpretability.
Knowledge Discovery in Surveys using Machine Learning: A Case Study of Women in Entrepreneurship in UAE
Authors: Syed Farhan Ahmad, Amrah Hermayen, Ganga Bhavani
Date: 2021-03-21 00:00:00
Tasks: BIG-bench Machine Learning

Knowledge Discovery plays a very important role in analyzing data and getting insights from them to drive better business decisions. Entrepreneurship in a Knowledge based economy contributes greatly to the development of a country's economy. In this paper, we analyze surveys that were conducted on women in entrepreneurship in UAE. Relevant insights are extracted from the data that can help us to better understand the current landscape of women in entrepreneurship and predict the future as well. The features are analyzed using machine learning to drive better business decisions in the future.