pwc-archive/papers-with-abstracts huggingface dataset:
Visual Hide and Seek
Authors: Boyuan Chen, Shuran Song, Hod Lipson, Carl Vondrick
Date: 2019-10-15 00:00:00
Tasks: Navigate

We train embodied agents to play Visual Hide and Seek where a prey must navigate in a simulated environment in order to avoid capture from a predator. We place a variety of obstacles in the environment for the prey to hide behind, and we only give the agents partial observations of their environment using an egocentric perspective. Although we train the model to play this game from scratch, experiments and visualizations suggest that the agent learns to predict its own visibility in the environment. Furthermore, we quantitatively analyze how agent weaknesses, such as slower speed, effect the learned policy. Our results suggest that, although agent weaknesses make the learning problem more challenging, they also cause more useful features to be learned. Our project website is available at: http://www.cs.columbia.edu/ ~bchen/visualhideseek/.
Adaptive Transfer Learning of Multi-View Time Series Classification
Authors: Donglin Zhan, Shiyu Yi, Dongli Xu, Xiao Yu, Denglin Jiang, Siqi Yu, Haoting Zhang, Wenfang Shangguan, Weihua Zhang
Date: 2019-10-14 00:00:00
Tasks: Classification, Density Estimation, General Classification, Time Series, Time Series Analysis, Time Series Classification, Transfer Learning

Time Series Classification (TSC) has been an important and challenging task in data mining, especially on multivariate time series and multi-view time series data sets. Meanwhile, transfer learning has been widely applied in computer vision and natural language processing applications to improve deep neural network's generalization capabilities. However, very few previous works applied transfer learning framework to time series mining problems. Particularly, the technique of measuring similarities between source domain and target domain based on dynamic representation such as density estimation with importance sampling has never been combined with transfer learning framework. In this paper, we first proposed a general adaptive transfer learning framework for multi-view time series data, which shows strong ability in storing inter-view importance value in the process of knowledge transfer. Next, we represented inter-view importance through some time series similarity measurements and approximated the posterior distribution in latent space for the importance sampling via density estimation techniques. We then computed the matrix norm of sampled importance value, which controls the degree of knowledge transfer in pre-training process. We further evaluated our work, applied it to many other time series classification tasks, and observed that our architecture maintained desirable generalization ability. Finally, we concluded that our framework could be adapted with deep learning techniques to receive significant model performance improvements.
Wasserstein Distance Guided Cross-Domain Learning
Authors: Jie Su
Date: 2019-10-14 00:00:00
Tasks: Domain Adaptation

Domain adaptation aims to generalise a high-performance learner on target domain (non-labelled data) by leveraging the knowledge from source domain (rich labelled data) which comes from a different but related distribution. Assuming the source and target domains data(e.g. images) come from a joint distribution but follow on different marginal distributions, the domain adaptation work aims to infer the joint distribution from the source and target domain to learn the domain invariant features. Therefore, in this study, I extend the existing state-of-the-art approach to solve the domain adaptation problem. In particular, I propose a new approach to infer the joint distribution of images from different distributions, namely Wasserstein Distance Guided Cross-Domain Learning (WDGCDL). WDGCDL applies the Wasserstein distance to estimate the divergence between the source and target distribution which provides good gradient property and promising generalisation bound. Moreover, to tackle the training difficulty of the proposed framework, I propose two different training schemes for stable training. Qualitative results show that this new approach is superior to the existing state-of-the-art methods in the standard domain adaptation benchmark.
Machine Learning and Big Scientific Data
Authors: Tony Hey, Keith Butler, Sam Jackson, Jeyarajan Thiyagalingam
Date: 2019-10-12 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Object Recognition, Protein Folding

This paper reviews some of the challenges posed by the huge growth of experimental data generated by the new generation of large-scale experiments at UK national facilities at the Rutherford Appleton Laboratory site at Harwell near Oxford. Such "Big Scientific Data" comes from the Diamond Light Source and Electron Microscopy Facilities, the ISIS Neutron and Muon Facility, and the UK's Central Laser Facility. Increasingly, scientists are now needing to use advanced machine learning and other AI technologies both to automate parts of the data pipeline and also to help find new scientific discoveries in the analysis of their data. For commercially important applications, such as object recognition, natural language processing and automatic translation, deep learning has made dramatic breakthroughs. Google's DeepMind has now also used deep learning technology to develop their AlphaFold tool to make predictions for protein folding. Remarkably, they have been able to achieve some spectacular results for this specific scientific problem. Can deep learning be similarly transformative for other scientific problems? After a brief review of some initial applications of machine learning at the Rutherford Appleton Laboratory, we focus on challenges and opportunities for AI in advancing materials science. Finally, we discuss the importance of developing some realistic machine learning benchmarks using Big Scientific Data coming from a number of different scientific domains. We conclude with some initial examples of our "SciML" benchmark suite and of the research challenges these benchmarks will enable.
Combining Machine Learning Models using combo Library
Authors: Yue Zhao, Xuejian Wang, Cheng Cheng, Xueying Ding
Date: 2019-09-21 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Clustering, Ensemble Learning

Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications. To facilitate this process, we propose and implement an easy-to-use Python toolkit, combo, to aggregate models and scores under various scenarios, including classification, clustering, and anomaly detection. In a nutshell, combo provides a unified and consistent way to combine both raw and pretrained models from popular machine learning libraries, e.g., scikit-learn, XGBoost, and LightGBM. With accessibility and robustness in mind, combo is designed with detailed documentation, interactive examples, continuous integration, code coverage, and maintainability check; it can be installed easily through Python Package Index (PyPI) or https://github.com/yzhao062/combo.
Baseline wander and power line interference removal from ECG signals using eigenvalue decomposition
Authors: Rishi Raj Sharma，Ram Bilas Pachori
Date: 2018-06-01 00:00:00
Proceeding: Elsevier 2018 6

In this paper, a novel method is proposed for baseline wander (BW) and power line interference (PLI)
removal from electrocardiogram (ECG) signals. The proposed methodology is based on the eigenvalue
decomposition of the Hankel matrix. It has been observed that the end-point eigenvalues of the Hankel
matrix formed using noisy ECG signals are correlated with BW and PLI components. We have proposed
a methodology to remove BW and PLI noise by eliminating eigenvalues corresponding to noisy components. The proposed concept uses one-step process for removing both BW and PLI noise simultaneously.
The proposed method has been compared with other existing methods using performance measure
parameters namely output signal to noise ratio (SNRout), and percent root mean square difference (PRD).
Simulation results validate the better performance of the proposed method than compared methods at
different noise levels. The proposed method is suitable for preprocessing of ECG signals.
Optimizing Query Evaluations using Reinforcement Learning for Web Search
Authors: Corby Rosset, Damien Jose, Gargi Ghosh, Bhaskar Mitra, Saurabh Tiwary
Date: 2018-04-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In web search, typically a candidate generation step selects a small set of documents---from collections containing as many as billions of web pages---that are subsequently ranked and pruned before being presented to the user. In Bing, the candidate generation involves scanning the index using statically designed match plans that prescribe sequences of different match criteria and stopping conditions. In this work, we pose match planning as a reinforcement learning task and observe up to 20% reduction in index blocks accessed, with small or no degradation in the quality of the candidate sets.
Reinforced Imitation Learning from Observations
Authors: Konrad Zolna, Negar Rostamzadeh, Yoshua Bengio, Sungjin Ahn, Pedro O. Pinheiro
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Imitation Learning, Reinforcement Learning

Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse. In this paper, we consider a challenging setting where an agent has access to a sparse reward function and state-only expert observations. We propose a method which gradually balances between the imitation learning cost and the reinforcement learning objective. Built upon an existing imitation learning method, our approach works with state-only observations. We show, through navigation scenarios, that (i) an agent is able to efficiently leverage sparse rewards to outperform standard state-only imitation learning, (ii) it can learn a policy even when learner's actions are different from the expert, and (iii) the performance of the agent is not bounded by that of the expert due to the optimized usage of sparse rewards.
Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms
Authors: Lihong Li, Wei Chu, John Langford, Xuanhui Wang
Date: 2010-03-31 00:00:00
Tasks: News Recommendation, Recommendation Systems

Contextual bandit algorithms have become popular for online recommendation systems such as Digg, Yahoo! Buzz, and news recommendation in general. \emph{Offline} evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging due to their "partial-label" nature. Common practice is to create a simulator which simulates the online environment for the problem at hand and then run an algorithm against this simulator. However, creating simulator itself is often difficult and modeling bias is usually unavoidably introduced. In this paper, we introduce a \emph{replay} methodology for contextual bandit algorithm evaluation. Different from simulator-based approaches, our method is completely data-driven and very easy to adapt to different applications. More importantly, our method can provide provably unbiased evaluations. Our empirical results on a large-scale news article recommendation dataset collected from Yahoo! Front Page conform well with our theoretical results. Furthermore, comparisons between our offline replay and online bucket evaluation of several contextual bandit algorithms show accuracy and effectiveness of our offline evaluation method.
Image Segmentation Based on Watershed and Edge Detection Techniques
Authors: Nassir Salman Computer Science Department, Zarqa Private University, Jordan
Date: 2005-02-13 00:00:00
Proceeding: Conference 2005 2
Tasks: Clustering, Edge Detection, Image Segmentation, Segmentation, Semantic Segmentation

A combination of K-means, watershed segmentation method, and Difference In Strength (DIS) map was used to
perform image segmentation and edge detection tasks. We obtained an initial segmentation based on K-means clustering
technique. Starting from this, we used two techniques; the first is watershed technique with new merging procedures based on
mean intensity value to segment the image regions and to detect their boundaries. The second is edge strength technique to
obtain an accurate edge maps of our images without using watershed method. In this paper: We solved the problem of
undesirable oversegmentation results produced by the watershed algorithm, when used directly with raw data images. Also,
the edge maps we obtained have no broken lines on entire image and the final edge detection result is one closed boundary per
actual region in the image.
A hybrid method of Exponential Smoothing and Recurrent Neural Networks for time series forecasting
Authors: Slawek Smyl
Date: 2019-07-18 00:00:00
Proceeding: International Journal of Forecasting 2019 7
Tasks: Graph Neural Network, Time Series, Time Series Analysis, Time Series Forecasting

This paper presents the winning submission of the M4 forecasting competition. The submission utilizes a Dynamic Computational Graph Neural Network system that enables mixing of a standard Exponential Smoothing model with advanced Long Short Term Memory networks into a common framework. The result is a hybrid and hierarchical forecasting method.
Keywords: Forecasting competitions, M4, Dynamic Computational Graphs, Automatic Differentiation, Long Short Term Memory (LSTM) networks, Exponential Smoothing
Knowledge-aware Autoencoders for Explainable Recommender Sytems
Authors: Vito Bellini, Angelo Schiavone, Tommaso Di Noia, Azzurra Ragone, Eugenio Di Sciascio
Date: 2018-07-17 00:00:00
Tasks: Diversity, Recommendation Systems

Recommender Systems have been widely used to help users in finding what they are looking for thus tackling the information overload problem. After several years of research and industrial findings looking after better algorithms to improve accuracy and diversity metrics, explanation services for recommendation are gaining momentum as a tool to provide a human-understandable feedback to results computed, in most of the cases, by black-box machine learning techniques. As a matter of fact, explanations may guarantee users satisfaction, trust, and loyalty in a system. In this paper, we evaluate how different information encoded in a Knowledge Graph are perceived by users when they are adopted to show them an explanation. More precisely, we compare how the use of categorical information, factual one or a mixture of them both in building explanations, affect explanatory criteria for a recommender system. Experimental results are validated through an A/B testing platform which uses a recommendation engine based on a Semantics-Aware Autoencoder to build users profiles which are in turn exploited to compute recommendation lists and to provide an explanation.
Artificial Neural Networks for Beginners
Authors: Carlos Gershenson
Date: 2003-08-20 00:00:00

The scope of this teaching package is to make a brief induction to Artificial Neural Networks (ANNs) for people who have no previous knowledge of them. We first make a brief introduction to models of networks, for then describing in general terms ANNs. As an application, we explain the backpropagation algorithm, since it is widely used and many other algorithms are derived from it. The user should know algebra and the handling of functions and vectors. Differential calculus is recommendable, but not necessary. The contents of this package should be understood by people with high school education. It would be useful for people who are just curious about what are ANNs, or for people who want to become familiar with them, so when they study them more fully, they will already have clear notions of ANNs. Also, people who only want to apply the backpropagation algorithm without a detailed and formal explanation of it will find this material useful. This work should not be seen as "Nets for dummies", but of course it is not a treatise. Much of the formality is skipped for the sake of simplicity. Detailed explanations and demonstrations can be found in the referred readings. The included exercises complement the understanding of the theory. The on-line resources are highly recommended for extending this brief induction.
5G mmWave Cooperative Positioning and Mapping using Multi-Model PHD Filter and Map Fusion
Authors: Hyowon Kim, Karl Granström, Lin Gao, Giorgio Battistelli, Sunwoo Kim, Henk Wymeersch
Date: 2019-08-26 00:00:00

5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station (BS) and vehicles are equipped with large antenna arrays. However, radio-based positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. Building such a map is challenging, due to the inherent data association uncertainty, missed detection, and clutter. We propose a new method for cooperative vehicle positioning and mapping of the radio environment in order to address these challenges. The proposed method comprises a multi-model probability hypothesis density (PHD) filter and a map fusion routine, which is able to consider different types of objects and different fields of views (FoVs). Simulation results demonstrate that the proposed method handles the aforementioned challenges, and improves the vehicle positioning and mapping performance.
Many Faces of Feature Importance: Comparing Built-in and Post-hoc Feature Importance in Text Classification
Authors: Vivian Lai, Jon Z. Cai, Chenhao Tan
Date: 2019-10-18 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Feature Importance, General Classification, text-classification, Text Classification

Feature importance is commonly used to explain machine predictions. While feature importance can be derived from a machine learning model with a variety of methods, the consistency of feature importance via different methods remains understudied. In this work, we systematically compare feature importance from built-in mechanisms in a model such as attention values and post-hoc methods that approximate model behavior such as LIME. Using text classification as a testbed, we find that 1) no matter which method we use, important features from traditional models such as SVM and XGBoost are more similar with each other, than with deep learning models; 2) post-hoc methods tend to generate more similar important features for two models than built-in methods. We further demonstrate how such similarity varies across instances. Notably, important features do not always resemble each other better when two models agree on the predicted label than when they disagree.
Masked Gradient-Based Causal Structure Learning
Authors: Ignavier Ng, Shengyu Zhu, Zhuangyan Fang, Haoyang Li, Zhitang Chen, Jun Wang
Date: 2019-10-18 00:00:00
Tasks: Causal Discovery, Causal Inference

This paper studies the problem of learning causal structures from observational data. We reformulate the Structural Equation Model (SEM) with additive noises in a form parameterized by binary graph adjacency matrix and show that, if the original SEM is identifiable, then the binary adjacency matrix can be identified up to super-graphs of the true causal graph under mild conditions. We then utilize the reformulated SEM to develop a causal structure learning method that can be efficiently trained using gradient-based optimization, by leveraging a smooth characterization on acyclicity and the Gumbel-Softmax approach to approximate the binary adjacency matrix. It is found that the obtained entries are typically near zero or one and can be easily thresholded to identify the edges. We conduct experiments on synthetic and real datasets to validate the effectiveness of the proposed method, and show that it readily includes different smooth model functions and achieves a much improved performance on most datasets considered.
A flexible integer linear programming formulation for scheduling clinician on-call service in hospitals
Authors: David Landsman, Huiting Ma, Jesse Knight, Kevin Gough, Sharmistha Mishra
Date: 2019-10-18 00:00:00
Tasks: Scheduling

Scheduling of personnel in a hospital environment is vital to improving the service provided to patients and balancing the workload assigned to clinicians. Many approaches have been tried and successfully applied to generate efficient schedules in such settings. However, due to the computational complexity of the scheduling problem in general, most approaches resort to heuristics to find a non-optimal solution in a reasonable amount of time. We designed an integer linear programming formulation to find an optimal schedule in a clinical division of a hospital. Our formulation mitigates issues related to computational complexity by minimizing the set of constraints, yet retains sufficient flexibility so that it can be adapted to a variety of clinical divisions. We then conducted a case study for our approach using data from the Infectious Diseases division at St. Michael's Hospital in Toronto, Canada. We analyzed and compared the results of our approach to manually-created schedules at the hospital, and found improved adherence to departmental constraints and clinician preferences. We used simulated data to examine the sensitivity of the runtime of our linear program for various parameters and observed reassuring results, signifying the practicality and generalizability of our approach in different real-world scenarios.
MARTHE: Scheduling the Learning Rate Via Online Hypergradients
Authors: Michele Donini, Luca Franceschi, Massimiliano Pontil, Orchid Majumder, Paolo Frasconi
Date: 2019-10-18 00:00:00
Tasks: Hyperparameter Optimization, Scheduling

We study the problem of fitting task-specific learning rate schedules from the perspective of hyperparameter optimization, aiming at good generalization. We describe the structure of the gradient of a validation error w.r.t. the learning rate schedule -- the hypergradient. Based on this, we introduce MARTHE, a novel online algorithm guided by cheap approximations of the hypergradient that uses past information from the optimization trajectory to simulate future behaviour. It interpolates between two recent techniques, RTHO (Franceschi et al., 2017) and HD (Baydin et al. 2018), and is able to produce learning rate schedules that are more stable leading to models that generalize better.
Fast Local Planning and Mapping in Unknown Off-Road Terrain
Authors: Timothy Overbye, Srikanth Saripalli
Date: 2019-10-18 00:00:00

In this paper, we present a fast, on-line mapping and planning solution for operation in unknown, off-road, environments. We combine obstacle detection along with a terrain gradient map to make simple and adaptable cost map. This map can be created and updated at 10 Hz. An A* planner finds optimal paths over the map. Finally, we take multiple samples over the control input space and do a kinematic forward simulation to generated feasible trajectories. Then the most optimal trajectory, as determined by the cost map and proximity to A* path, is chosen and sent to the controller. Our method allows real time operation at rates of 30 Hz. We demonstrate the efficiency of our method in various off-road terrain at high speed.