pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Distillation for Decentralized Learning from Heterogeneous Clients
Authors: Jiaxin Ma, Ryo Yonetani, Zahid Iqbal
Date: 2020-08-18 00:00:00
Tasks: Federated Learning

This paper addresses the problem of decentralized learning to achieve a high-performance global model by asking a group of clients to share local models pre-trained with their own data resources. We are particularly interested in a specific case where both the client model architectures and data distributions are diverse, which makes it nontrivial to adopt conventional approaches such as Federated Learning and network co-distillation. To this end, we propose a new decentralized learning method called Decentralized Learning via Adaptive Distillation (DLAD). Given a collection of client models and a large number of unlabeled distillation samples, the proposed DLAD 1) aggregates the outputs of the client models while adaptively emphasizing those with higher confidence in given distillation samples and 2) trains the global model to imitate the aggregated outputs. Our extensive experimental evaluation on multiple public datasets (MNIST, CIFAR-10, and CINIC-10) demonstrates the effectiveness of the proposed method.
Describing Unseen Videos via Multi-Modal Cooperative Dialog Agents
Authors: Ye Zhu, Yu Wu, Yi Yang, Yan Yan
Date: 2020-08-18 00:00:00
Proceeding: ECCV 2020 8
Tasks: Video Description

With the arising concerns for the AI systems provided with direct access to abundant sensitive information, researchers seek to develop more reliable AI with implicit information sources. To this end, in this paper, we introduce a new task called video description via two multi-modal cooperative dialog agents, whose ultimate goal is for one conversational agent to describe an unseen video based on the dialog and two static frames. Specifically, one of the intelligent agents - Q-BOT - is given two static frames from the beginning and the end of the video, as well as a finite number of opportunities to ask relevant natural language questions before describing the unseen video. A-BOT, the other agent who has already seen the entire video, assists Q-BOT to accomplish the goal by providing answers to those questions. We propose a QA-Cooperative Network with a dynamic dialog history update learning mechanism to transfer knowledge from A-BOT to Q-BOT, thus helping Q-BOT to better describe the video. Extensive experiments demonstrate that Q-BOT can effectively learn to describe an unseen video by the proposed model and the cooperative learning method, achieving the promising performance where Q-BOT is given the full ground truth history dialog.
Motion Capture from Internet Videos
Authors: Junting Dong, Qing Shuai, Yuanqing Zhang, Xian Liu, Xiaowei Zhou, Hujun Bao
Date: 2020-08-18 00:00:00
Proceeding: ECCV 2020 8
Tasks: Pose Estimation

Recent advances in image-based human pose estimation make it possible to capture 3D human motion from a single RGB video. However, the inherent depth ambiguity and self-occlusion in a single view prohibit the recovery of as high-quality motion as multi-view reconstruction. While multi-view videos are not common, the videos of a celebrity performing a specific action are usually abundant on the Internet. Even if these videos were recorded at different time instances, they would encode the same motion characteristics of the person. Therefore, we propose to capture human motion by jointly analyzing these Internet videos instead of using single videos separately. However, this new task poses many new challenges that cannot be addressed by existing methods, as the videos are unsynchronized, the camera viewpoints are unknown, the background scenes are different, and the human motions are not exactly the same among videos. To address these challenges, we propose a novel optimization-based framework and experimentally demonstrate its ability to recover much more precise and detailed motion from multiple videos, compared against monocular motion capture methods.
Feature Products Yield Efficient Networks
Authors: Philipp Grüning, Thomas Martinetz, Erhardt Barth
Date: 2020-08-18 00:00:00

We introduce Feature-Product networks (FP-nets) as a novel deep-network architecture based on a new building block inspired by principles of biological vision. For each input feature map, a so-called FP-block learns two different filters, the outputs of which are then multiplied. Such FP-blocks are inspired by models of end-stopped neurons, which are common in cortical areas V1 and especially in V2. Convolutional neural networks can be transformed into parameter-efficient FP-nets by substituting conventional blocks of regular convolutions with FP-blocks. In this way, we create several novel FP-nets based on state-of-the-art networks and evaluate them on the Cifar-10 and ImageNet challenges. We show that the use of FP-blocks reduces the number of parameters significantly without decreasing generalization capability. Since so far heuristics and search algorithms have been used to find more efficient networks, it seems remarkable that we can obtain even more efficient networks based on a novel bio-inspired design principle.
Visibility-aware Multi-view Stereo Network
Authors: Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang
Date: 2020-08-18 00:00:00
Tasks: 3D Reconstruction, Depth Estimation, Point Clouds

Learning-based multi-view stereo (MVS) methods have demonstrated promising results. However, very few existing networks explicitly take the pixel-wise visibility into consideration, resulting in erroneous cost aggregation from occluded pixels. In this paper, we explicitly infer and integrate the pixel-wise occlusion information in the MVS network via the matching uncertainty estimation. The pair-wise uncertainty map is jointly inferred with the pair-wise depth map, which is further used as weighting guidance during the multi-view cost volume fusion. As such, the adverse influence of occluded pixels is suppressed in the cost fusion. The proposed framework Vis-MVSNet significantly improves depth accuracies in the scenes with severe occlusion. Extensive experiments are performed on DTU, BlendedMVS, and Tanks and Temples datasets to justify the effectiveness of the proposed framework.
Linear Disentangled Representations and Unsupervised Action Estimation
Authors: Matthew Painter, Jonathon Hare, Adam Prugel-Bennett
Date: 2020-08-18 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Disentanglement, Representation Learning

Disentangled representation learning has seen a surge in interest over recent times, generally focusing on new models which optimise one of many disparate disentanglement metrics. Symmetry Based Disentangled Representation learning introduced a robust mathematical framework that defined precisely what is meant by a "linear disentangled representation". This framework determined that such representations would depend on a particular decomposition of the symmetry group acting on the data, showing that actions would manifest through irreducible group representations acting on independent representational subspaces. Caselles-Dupre et al [2019] subsequently proposed the first model to induce and demonstrate a linear disentangled representation in a VAE model. In this work we empirically show that linear disentangled representations are not generally present in standard VAE models and that they instead require altering the loss landscape to induce them. We proceed to show that such representations are a desirable property with regard to classical disentanglement metrics. Finally we propose a method to induce irreducible representations which forgoes the need for labelled action sequences, as was required by prior work. We explore a number of properties of this method, including the ability to learn from action sequences without knowledge of intermediate states and robustness under visual noise. We also demonstrate that it can successfully learn 4 independent symmetries directly from pixels.
Inductive logic programming at 30: a new introduction
Authors: Andrew Cropper, Sebastijan Dumančić
Date: 2020-08-18 00:00:00
Tasks: BIG-bench Machine Learning, Inductive logic programming

Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we provide a new introduction to the field. We introduce the necessary logical notation and the main learning settings; describe the building blocks of an ILP system; compare several systems on several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol); highlight key application areas; and, finally, summarise current limitations and directions for future research.
Glancing Transformer for Non-Autoregressive Neural Machine Translation
Authors: Lihua Qian, Hao Zhou, Yu Bao, Mingxuan Wang, Lin Qiu, Wei-Nan Zhang, Yong Yu, Lei LI
Date: 2020-08-18 00:00:00
Proceeding: ACL 2021 5
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

Recent work on non-autoregressive neural machine translation (NAT) aims at improving the efficiency by parallel decoding without sacrificing the quality. However, existing NAT methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup. We propose the Glancing Language Model (GLM), a method to learn word interdependency for single-pass parallel generation models. With GLM, we develop Glancing Transformer (GLAT) for machine translation. With only single-pass parallel decoding, GLAT is able to generate high-quality translation with 8-15 times speedup. Experiments on multiple WMT language directions show that GLAT outperforms all previous single pass non-autoregressive methods, and is nearly comparable to Transformer, reducing the gap to 0.25-0.9 BLEU points.
Bayesian geoacoustic inversion using mixture density network
Authors: Guoli Wu, Jingya Zhang, Junqiang Song
Date: 2020-08-18 00:00:00
Tasks: Bayesian Inference

Bayesian geoacoustic inversion problems are conventionally solved by Markov chain Monte Carlo methods or its variants, which are computationally expensive. This paper extends the classic Bayesian geoacoustic inversion framework by deriving important geoacoustic statistics of Bayesian geoacoustic inversion from the multidimensional posterior probability density (PPD) using the mixture density network (MDN) theory. These statistics make it convenient to train the network directly on the whole parameter space and get the multidimensional PPD of model parameters. The present approach provides a much more efficient way to solve geoacoustic inversion problems in Bayesian inference framework. The network is trained on a simulated dataset of surface-wave dispersion curves with shear-wave velocities as labels and tested on both synthetic and real data cases. The results show that the network gives reliable predictions and has good generalization performance on unseen data. Once trained, the network can rapidly (within seconds) give a fully probabilistic solution which is comparable to Monte Carlo methods. It provides an promising approach for real-time inversion.
Person image generation with semantic attention network for person re-identification
Authors: Meichen Liu, Kejun Wang, Juihang Ji, Shuzhi Sam Ge
Date: 2020-08-18 00:00:00
Tasks: Image Generation, Person Re-Identification, Pose-Guided Image Generation, Semantic Parsing

Pose variation is one of the key factors which prevents the network from learning a robust person re-identification (Re-ID) model. To address this issue, we propose a novel person pose-guided image generation method, which is called the semantic attention network. The network consists of several semantic attention blocks, where each block attends to preserve and update the pose code and the clothing textures. The introduction of the binary segmentation mask and the semantic parsing is important for seamlessly stitching foreground and background in the pose-guided image generation. Compared with other methods, our network can characterize better body shape and keep clothing attributes, simultaneously. Our synthesized image can obtain better appearance and shape consistency related to the original image. Experimental results show that our approach is competitive with respect to both quantitative and qualitative results on Market-1501 and DeepFashion. Furthermore, we conduct extensive evaluations by using person re-identification (Re-ID) systems trained with the pose-transferred person based augmented data. The experiment shows that our approach can significantly enhance the person Re-ID accuracy.
Modeling, Visualization, and Analysis of African Innovation Performance
Authors: Muhammad Omer, Moayad El-Amin, Ammar Nasr, Rami Ahmed
Date: 2020-08-18 00:00:00
Tasks: BIG-bench Machine Learning

In this paper we discuss the concepts and emergence of Innovation Performance, and how to quantify it, primarily working with data from the Global Innovation Index, with emphasis on the African Innovation Performance. We briefly overview existing literature on using machine learning for modeling innovation performance, and use simple machine learning techniques, to analyze and predict the "Mobile App Creation Indicator" from the Global Innovation Index, by using insights from the stack-overflow developers survey. Also, we build and compare models to predict the Innovation Output Sub-index, also from the Global Innovation Index.
COVID-SEE: Scientific Evidence Explorer for COVID-19 Related Research
Authors: Karin Verspoor, Simon Šuster, Yulia Otmakhova, Shevon Mendis, Zenan Zhai, Biaoyan Fang, Jey Han Lau, Timothy Baldwin, Antonio Jimeno Yepes, David Martinez
Date: 2020-08-18 00:00:00
Tasks: Articles

We present COVID-SEE, a system for medical literature discovery based on the concept of information exploration, which builds on several distinct text analysis and natural language processing methods to structure and organise information in publications, and augments search by providing a visual overview supporting exploration of a collection to identify key articles of interest. We developed this system over COVID-19 literature to help medical professionals and researchers explore the literature evidence, and improve findability of relevant information. COVID-SEE is available at http://covid-see.com.
Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement Learning
Authors: Wenshuai Zhao, Jorge Peña Queralta, Li Qingqing, Tomi Westerlund
Date: 2020-08-18 00:00:00
Tasks: Deep Reinforcement Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Current research directions in deep reinforcement learning include bridging the simulation-reality gap, improving sample efficiency of experiences in distributed multi-agent reinforcement learning, together with the development of robust methods against adversarial agents in distributed learning, among many others. In this work, we are particularly interested in analyzing how multi-agent reinforcement learning can bridge the gap to reality in distributed multi-robot systems where the operation of the different robots is not necessarily homogeneous. These variations can happen due to sensing mismatches, inherent errors in terms of calibration of the mechanical joints, or simple differences in accuracy. While our results are simulation-based, we introduce the effect of sensing, calibration, and accuracy mismatches in distributed reinforcement learning with proximal policy optimization (PPO). We discuss on how both the different types of perturbances and how the number of agents experiencing those perturbances affect the collaborative learning effort. The simulations are carried out using a Kuka arm model in the Bullet physics engine. This is, to the best of our knowledge, the first work exploring the limitations of PPO in multi-robot systems when considering that different robots might be exposed to different environments where their sensors or actuators have induced errors. With the conclusions of this work, we set the initial point for future work on designing and developing methods to achieve robust reinforcement learning on the presence of real-world perturbances that might differ within a multi-robot system.
S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
Authors: Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, Ji-Rong Wen
Date: 2020-08-18 00:00:00
Tasks: Attribute, Self-Supervised Learning, Sequential Recommendation

Recently, significant progress has been made in sequential recommendation with deep learning. Existing neural sequential recommendation models usually rely on the item prediction loss to learn model parameters or data representations. However, the model trained with this loss is prone to suffer from data sparsity problem. Since it overemphasizes the final performance, the association or fusion between context data and sequence data has not been well captured and utilized for sequential recommendation. To tackle this problem, we propose the model S^3-Rec, which stands for Self-Supervised learning for Sequential Recommendation, based on the self-attentive neural architecture. The main idea of our approach is to utilize the intrinsic data correlation to derive self-supervision signals and enhance the data representations via pre-training methods for improving sequential recommendation. For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence by utilizing the mutual information maximization (MIM) principle. MIM provides a unified way to characterize the correlation between different types of data, which is particularly suitable in our scenario. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods, especially when only limited training data is available. Besides, we extend our self-supervised learning method to other recommendation models, which also improve their performance.
Self-supervised Sparse to Dense Motion Segmentation
Authors: Amirhossein Kardoost, Kalun Ho, Peter Ochs, Margret Keuper
Date: 2020-08-18 00:00:00
Tasks: Motion Segmentation, Segmentation

Observable motion in videos can give rise to the definition of objects moving with respect to the scene. The task of segmenting such moving objects is referred to as motion segmentation and is usually tackled either by aggregating motion information in long, sparse point trajectories, or by directly producing per frame dense segmentations relying on large amounts of training data. In this paper, we propose a self supervised method to learn the densification of sparse motion segmentations from single video frames. While previous approaches towards motion segmentation build upon pre-training on large surrogate datasets and use dense motion information as an essential cue for the pixelwise segmentation, our model does not require pre-training and operates at test time on single frames. It can be trained in a sequence specific way to produce high quality dense segmentations from sparse and noisy input. We evaluate our method on the well-known motion segmentation datasets FBMS59 and DAVIS16.
Multi-Modal Trajectory Prediction of NBA Players
Authors: Sandro Hauri, Nemanja Djuric, Vladan Radosavljevic, Slobodan Vucetic
Date: 2020-08-18 00:00:00
Tasks: Decision Making, Prediction, Trajectory Prediction

National Basketball Association (NBA) players are highly motivated and skilled experts that solve complex decision making problems at every time point during a game. As a step towards understanding how players make their decisions, we focus on their movement trajectories during games. We propose a method that captures the multi-modal behavior of players, where they might consider multiple trajectories and select the most advantageous one. The method is built on an LSTM-based architecture predicting multiple trajectories and their probabilities, trained by a multi-modal loss function that updates the best trajectories. Experiments on large, fine-grained NBA tracking data show that the proposed method outperforms the state-of-the-art. In addition, the results indicate that the approach generates more realistic trajectories and that it can learn individual playing styles of specific players.
A Formally Robust Time Series Distance Metric
Authors: Maximilian Toller, Bernhard C. Geiger, Roman Kern
Date: 2020-08-18 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis, Time Series Classification

Distance-based classification is among the most competitive classification methods for time series data. The most critical component of distance-based classification is the selected distance function. Past research has proposed various different distance metrics or measures dedicated to particular aspects of real-world time series data, yet there is an important aspect that has not been considered so far: Robustness against arbitrary data contamination. In this work, we propose a novel distance metric that is robust against arbitrarily "bad" contamination and has a worst-case computational complexity of $\mathcal{O}(n\log n)$. We formally argue why our proposed metric is robust, and demonstrate in an empirical evaluation that the metric yields competitive classification accuracy when applied in k-Nearest Neighbor time series classification.
The Relational Data Borg is Learning
Authors: Dan Olteanu
Date: 2020-08-18 00:00:00

This paper overviews an approach that addresses machine learning over relational data as a database problem. This is justified by two observations. First, the input to the learning task is commonly the result of a feature extraction query over the relational data. Second, the learning task requires the computation of group-by aggregates. This approach has been already investigated for a number of supervised and unsupervised learning tasks, including: ridge linear regression, factorisation machines, support vector machines, decision trees, principal component analysis, and k-means; and also for linear algebra over data matrices. The main message of this work is that the runtime performance of machine learning can be dramatically boosted by a toolbox of techniques that exploit the knowledge of the underlying data. This includes theoretical development on the algebraic, combinatorial, and statistical structure of relational data processing and systems development on code specialisation, low-level computation sharing, and parallelisation. These techniques aim at lowering both the complexity and the constant factors of the learning time. This work is the outcome of extensive collaboration of the author with colleagues from RelationalAI, in particular Mahmoud Abo Khamis, Molham Aref, Hung Ngo, and XuanLong Nguyen, and from the FDB research project, in particular Ahmet Kara, Milos Nikolic, Maximilian Schleich, Amir Shaikhha, Jakub Zavodny, and Haozhe Zhang. The author would also like to thank the members of the FDB project for the figures and examples used in this paper. The author is grateful for support from industry: Amazon Web Services, Google, Infor, LogicBlox, Microsoft Azure, RelationalAI; and from the funding agencies EPSRC and ERC. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 682588.