pwc-archive/papers-with-abstracts huggingface dataset:
Towards Abstract Relational Learning in Human Robot Interaction
Authors: Mohamadreza Faridghasemnia, Daniele Nardi, Alessandro Saffiotti
Date: 2020-11-20 00:00:00
Tasks: Attribute, Relational Reasoning

Humans have a rich representation of the entities in their environment. Entities are described by their attributes, and entities that share attributes are often semantically related. For example, if two books have "Natural Language Processing" as the value of their `title' attribute, we can expect that their `topic' attribute will also be equal, namely, "NLP". Humans tend to generalize such observations, and infer sufficient conditions under which the `topic' attribute of any entity is "NLP". If robots need to interact successfully with humans, they need to represent entities, attributes, and generalizations in a similar way. This ends in a contextualized cognitive agent that can adapt its understanding, where context provides sufficient conditions for a correct understanding. In this work, we address the problem of how to obtain these representations through human-robot interaction. We integrate visual perception and natural language input to incrementally build a semantic model of the world, and then use inductive reasoning to infer logical rules that capture generic semantic relations, true in this model. These relations can be used to enrich the human-robot interaction, to populate a knowledge base with inferred facts, or to remove uncertainty in the robot's sensory inputs.
Logically Consistent Loss for Visual Question Answering
Authors: Anh-Cat Le-Ngo, Truyen Tran, Santu Rana, Sunil Gupta, Svetha Venkatesh
Date: 2020-11-19 00:00:00
Tasks: Multi-Task Learning, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Given an image, a back-ground knowledge, and a set of questions about an object, human learners answer the questions very consistently regardless of question forms and semantic tasks. The current advancement in neural-network based Visual Question Answering (VQA), despite their impressive performance, cannot ensure such consistency due to identically distribution (i.i.d.) assumption. We propose a new model-agnostic logic constraint to tackle this issue by formulating a logically consistent loss in the multi-task learning framework as well as a data organisation called family-batch and hybrid-batch. To demonstrate usefulness of this proposal, we train and evaluate MAC-net based VQA machines with and without the proposed logically consistent loss and the proposed data organization. The experiments confirm that the proposed loss formulae and introduction of hybrid-batch leads to more consistency as well as better performance. Though the proposed approach is tested with MAC-net, it can be utilised in any other QA methods whenever the logical consistency between answers exist.
The Fundamental Principles of Reproducibility
Authors: Odd Erik Gundersen
Date: 2020-11-19 00:00:00
Tasks: BIG-bench Machine Learning

Reproducibility is a confused terminology. In this paper, I take a fundamental view on reproducibility rooted in the scientific method. The scientific method is analysed and characterised in order to develop the terminology required to define reproducibility. Further, the literature on reproducibility and replication is surveyed, and experiments are modeled as tasks and problem solving methods. Machine learning is used to exemplify the described approach. Based on the analysis, reproducibility is defined and three different types of reproducibility as well as four degrees of reproducibility are specified.
Batteries, camera, action! Learning a semantic control space for expressive robot cinematography
Authors: Rogerio Bonatti, Arthur Bucker, Sebastian Scherer, Mustafa Mukadam, Jessica Hodgins
Date: 2020-11-19 00:00:00

Aerial vehicles are revolutionizing the way film-makers can capture shots of actors by composing novel aerial and dynamic viewpoints. However, despite great advancements in autonomous flight technology, generating expressive camera behaviors is still a challenge and requires non-technical users to edit a large number of unintuitive control parameters. In this work, we develop a data-driven framework that enables editing of these complex camera positioning parameters in a semantic space (e.g. calm, enjoyable, establishing). First, we generate a database of video clips with a diverse range of shots in a photo-realistic simulator, and use hundreds of participants in a crowd-sourcing framework to obtain scores for a set of semantic descriptors for each clip. Next, we analyze correlations between descriptors and build a semantic control space based on cinematography guidelines and human perception studies. Finally, we learn a generative model that can map a set of desired semantic video descriptors into low-level camera trajectory parameters. We evaluate our system by demonstrating that our model successfully generates shots that are rated by participants as having the expected degrees of expression for each descriptor. We also show that our models generalize to different scenes in both simulation and real-world experiments. Data and video found at: https://sites.google.com/view/robotcam.
Provable Multi-Objective Reinforcement Learning with Generative Models
Authors: Dongruo Zhou, Jiahao Chen, Quanquan Gu
Date: 2020-11-19 00:00:00
Tasks: Multi-Objective Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Multi-objective reinforcement learning (MORL) is an extension of ordinary, single-objective reinforcement learning (RL) that is applicable to many real-world tasks where multiple objectives exist without known relative costs. We study the problem of single policy MORL, which learns an optimal policy given the preference of objectives. Existing methods require strong assumptions such as exact knowledge of the multi-objective Markov decision process, and are analyzed in the limit of infinite data and time. We propose a new algorithm called model-based envelop value iteration (EVI), which generalizes the enveloped multi-objective $Q$-learning algorithm in Yang et al., 2019. Our method can learn a near-optimal value function with polynomial sample complexity and linear convergence speed. To the best of our knowledge, this is the first finite-sample analysis of MORL algorithms.
Collaborative Storytelling with Large-scale Neural Language Models
Authors: Eric Nichols, Leo Gao, Randy Gomez
Date: 2020-11-20 00:00:00
Tasks: Language Modeling, Language Modelling

Storytelling plays a central role in human socializing and entertainment. However, much of the research on automatic storytelling generation assumes that stories will be generated by an agent without any human interaction. In this paper, we introduce the task of collaborative storytelling, where an artificial intelligence agent and a person collaborate to create a unique story by taking turns adding to it. We present a collaborative storytelling system which works with a human storyteller to create a story by generating new utterances based on the story so far. We constructed the storytelling system by tuning a publicly-available large scale language model on a dataset of writing prompts and their accompanying fictional works. We identify generating sufficiently human-like utterances to be an important technical issue and propose a sample-and-rank approach to improve utterance quality. Quantitative evaluation shows that our approach outperforms a baseline, and we present qualitative evaluation of our system's capabilities.
Certified Monotonic Neural Networks
Authors: Xingchao Liu, Xing Han, Na Zhang, Qiang Liu
Date: 2020-11-20 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Fairness

Learning monotonic models with respect to a subset of the inputs is a desirable feature to effectively address the fairness, interpretability, and generalization issues in practice. Existing methods for learning monotonic neural networks either require specifically designed model structures to ensure monotonicity, which can be too restrictive/complicated, or enforce monotonicity by adjusting the learning process, which cannot provably guarantee the learned model is monotonic on selected features. In this work, we propose to certify the monotonicity of the general piece-wise linear neural networks by solving a mixed integer linear programming problem.This provides a new general approach for learning monotonic neural networks with arbitrary model structures. Our method allows us to train neural networks with heuristic monotonicity regularizations, and we can gradually increase the regularization magnitude until the learned network is certified monotonic. Compared to prior works, our approach does not require human-designed constraints on the weight space and also yields more accurate approximation. Empirical studies on various datasets demonstrate the efficiency of our approach over the state-of-the-art methods, such as Deep Lattice Networks.
Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat
Authors: Xiang Fang, Yuchong Hu, Pan Zhou, Dapeng Oliver Wu
Date: 2020-11-20 00:00:00
Tasks: Clustering, Incomplete multi-view clustering, Multi-view Subspace Clustering

Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous works assume that all views have the same incompleteness, i.e., balanced incompleteness. However, different views often have distinct incompleteness, i.e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views). The unbalanced incompleteness prevents us from directly using the previous methods for clustering. In this paper, inspired by the effective biological evolution theory, we design the novel scheme of view evolution to cluster strong and weak views. Moreover, we propose an Unbalanced Incomplete Multi-view Clustering method (UIMC), which is the first effective method based on view evolution for unbalanced incomplete multi-view clustering. Compared with previous methods, UIMC has two unique advantages: 1) it proposes weighted multi-view subspace clustering to integrate these unbalanced incomplete views, which effectively solves the unbalanced incomplete multi-view problem; 2) it designs the low-rank and robust representation to recover the data, which diminishes the impact of the incompleteness and noises. Extensive experimental results demonstrate that UIMC improves the clustering performance by up to 40% on three evaluation metrics over other state-of-the-art methods.
Learning Synthetic to Real Transfer for Localization and Navigational Tasks
Authors: Maxime Pietrantoni, Boris Chidlovskii, Tomi Silander
Date: 2020-11-20 00:00:00
Tasks: Autonomous Navigation, Image Retrieval, Navigate, Retrieval

Autonomous navigation consists in an agent being able to navigate without human intervention or supervision, it affects both high level planning and low level control. Navigation is at the crossroad of multiple disciplines, it combines notions of computer vision, robotics and control. This work aimed at creating, in a simulation, a navigation pipeline whose transfer to the real world could be done with as few efforts as possible. Given the limited time and the wide range of problematic to be tackled, absolute navigation performances while important was not the main objective. The emphasis was rather put on studying the sim2real gap which is one the major bottlenecks of modern robotics and autonomous navigation. To design the navigation pipeline four main challenges arise; environment, localization, navigation and planning. The iGibson simulator is picked for its photo-realistic textures and physics engine. A topological approach to tackle space representation was picked over metric approaches because they generalize better to new environments and are less sensitive to change of conditions. The navigation pipeline is decomposed as a localization module, a planning module and a local navigation module. These modules utilize three different networks, an image representation extractor, a passage detector and a local policy. The laters are trained on specifically tailored tasks with some associated datasets created for those specific tasks. Localization is the ability for the agent to localize itself against a specific space representation. It must be reliable, repeatable and robust to a wide variety of transformations. Localization is tackled as an image retrieval task using a deep neural network trained on an auxiliary task as a feature descriptor extractor. The local policy is trained with behavioral cloning from expert trajectories gathered with ROS navigation stack.
Recovery-to-Efficiency: A New Robustness Concept for Multi-objective Optimization under Uncertainty
Authors: El-Ghazali Talbi, Raca Todosijevic
Date: 2020-11-20 00:00:00

This paper presents a new robustness concept for uncertain multi-objective optimization problems. More precisely, in the paper the so-called recovery-to-efficiency robustness concept is proposed and investigated. Several approaches for generating recovery-to-efficiency robust sets in the context of multi-objective optimization are proposed as well. An extensive experimental analysis is performed to disclose differences among robust sets obtained using different concepts as well as to deduce some interesting observations. For testing purposes, instances from the bi-objective knapsack problem are considered.
PSD2 Explainable AI Model for Credit Scoring
Authors: Neus Llop Torrent, Giorgio Visani, Enrico Bagli
Date: 2020-11-20 00:00:00
Tasks: model

The aim of this project is to develop and test advanced analytical methods to improve the prediction accuracy of Credit Risk Models, preserving at the same time the model interpretability. In particular, the project focuses on applying an explainable machine learning model to bank-related databases. The input data were obtained from open data. Over the total proven models, CatBoost has shown the highest performance. The algorithm implementation produces a GINI of 0.68 after tuning the hyper-parameters. SHAP package is used to provide a global and local interpretation of the model predictions to formulate a human-comprehensive approach to understanding the decision-maker algorithm. The 20 most important features are selected using the Shapley values to present a full human-understandable model that reveals how the attributes of an individual are related to its model prediction.
Born Identity Network: Multi-way Counterfactual Map Generation to Explain a Classifier's Decision
Authors: Kwanseok Oh, Jee Seok Yoon, Heung-Il Suk
Date: 2020-11-20 00:00:00
Tasks: counterfactual

There exists an apparent negative correlation between performance and interpretability of deep learning models. In an effort to reduce this negative correlation, we propose a Born Identity Network (BIN), which is a post-hoc approach for producing multi-way counterfactual maps. A counterfactual map transforms an input sample to be conditioned and classified as a target label, which is similar to how humans process knowledge through counterfactual thinking. For example, a counterfactual map can localize hypothetical abnormalities from a normal brain image that may cause it to be diagnosed with a disease. Specifically, our proposed BIN consists of two core components: Counterfactual Map Generator and Target Attribution Network. The Counterfactual Map Generator is a variation of conditional GAN which can synthesize a counterfactual map conditioned on an arbitrary target label. The Target Attribution Network provides adequate assistance for generating synthesized maps by conditioning a target label into the Counterfactual Map Generator. We have validated our proposed BIN in qualitative and quantitative analysis on MNIST, 3D Shapes, and ADNI datasets, and showed the comprehensibility and fidelity of our method from various ablation studies.
Double Self-weighted Multi-view Clustering via Adaptive View Fusion
Authors: Xiang Fang, Yuchong Hu
Date: 2020-11-20 00:00:00
Tasks: Clustering

Multi-view clustering has been applied in many real-world applications where original data often contain noises. Some graph-based multi-view clustering methods have been proposed to try to reduce the negative influence of noises. However, previous graph-based multi-view clustering methods treat all features equally even if there are redundant features or noises, which is obviously unreasonable. In this paper, we propose a novel multi-view clustering framework Double Self-weighted Multi-view Clustering (DSMC) to overcome the aforementioned deficiency. DSMC performs double self-weighted operations to remove redundant features and noises from each graph, thereby obtaining robust graphs. For the first self-weighted operation, it assigns different weights to different features by introducing an adaptive weight matrix, which can reinforce the role of the important features in the joint representation and make each graph robust. For the second self-weighting operation, it weights different graphs by imposing an adaptive weight factor, which can assign larger weights to more robust graphs. Furthermore, by designing an adaptive multiple graphs fusion, we can fuse the features in the different graphs to integrate these graphs for clustering. Experiments on six real-world datasets demonstrate its advantages over other state-of-the-art multi-view clustering methods.
Bridging Scene Understanding and Task Execution with Flexible Simulation Environments
Authors: Zachary Ravichandran, J. Daniel Griffith, Benjamin Smith, Costas Frost
Date: 2020-11-20 00:00:00
Tasks: Graph Generation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scene Graph Generation, Scene Understanding, Semantic Segmentation

Significant progress has been made in scene understanding which seeks to build 3D, metric and object-oriented representations of the world. Concurrently, reinforcement learning has made impressive strides largely enabled by advances in simulation. Comparatively, there has been less focus in simulation for perception algorithms. Simulation is becoming increasingly vital as sophisticated perception approaches such as metric-semantic mapping or 3D dynamic scene graph generation require precise 3D, 2D, and inertial information in an interactive environment. To that end, we present TESSE (Task Execution with Semantic Segmentation Environments), an open source simulator for developing scene understanding and task execution algorithms. TESSE has been used to develop state-of-the-art solutions for metric-semantic mapping and 3D dynamic scene graph generation. Additionally, TESSE served as the platform for the GOSEEK Challenge at the International Conference of Robotics and Automation (ICRA) 2020, an object search competition with an emphasis on reinforcement learning. Code for TESSE is available at https://github.com/MIT-TESSE.
Implementing the Cognition Level for Industry 4.0 by integrating Augmented Reality and Manufacturing Execution Systems
Authors: Alfonso Di Pace, Giuseppe Fenza, Mariacristina Gallo, Vincenzo Loia, Aldo Meglio, Francesco Orciuoli
Date: 2020-11-18 00:00:00

In the current industrial practices, the exponential growth in terms of availability and affordability of sensors, data acquisition systems, and computer networks forces factories to move toward implementing high integrating Cyber-Physical Systems (CPS) with production, logistics, and services. This transforms today's factories into Industry 4.0 factories with significant economic potential. Industry 4.0, also known as the fourth Industrial Revolution, levers on the integration of cyber technologies, the Internet of Things, and Services. This paper proposes an Augmented Reality (AR)-based system that creates a Cognition Level that integrates existent Manufacturing Execution Systems (MES) to CPS. The idea is to highlight the opportunities offered by AR technologies to CPS by describing an application scenario. The system, analyzed in a real factory, shows its capacity to integrate physical and digital worlds strongly. Furthermore, the conducted survey (based on the Situation Awareness Global Assessment Technique method) reveals significant advantages in terms of production monitoring, progress, and workers' Situation Awareness in general.
Graph Signal Recovery Using Restricted Boltzmann Machines
Authors: Ankith Mohan, Aiichiro Nakano, Emilio Ferrara
Date: 2020-11-20 00:00:00
Tasks: BIG-bench Machine Learning, Denoising

We propose a model-agnostic pipeline to recover graph signals from an expert system by exploiting the content addressable memory property of restricted Boltzmann machine and the representational ability of a neural network. The proposed pipeline requires the deep neural network that is trained on a downward machine learning task with clean data, data which is free from any form of corruption or incompletion. We show that denoising the representations learned by the deep neural networks is usually more effective than denoising the data itself. Although this pipeline can deal with noise in any dataset, it is particularly effective for graph-structured datasets.
Efficient Consensus Model based on Proximal Gradient Method applied to Convolutional Sparse Problems
Authors: Gustavo Silva, Paul Rodriguez
Date: 2020-11-19 00:00:00
Tasks: Anomaly Detection, Dictionary Learning

Convolutional sparse representation (CSR), shift-invariant model for inverse problems, has gained much attention in the fields of signal/image processing, machine learning and computer vision. The most challenging problems in CSR implies the minimization of a composite function of the form $min_x \sum_i f_i(x) + g(x)$, where a direct and low-cost solution can be difficult to achieve. However, it has been reported that semi-distributed formulations such as ADMM consensus can provide important computational benefits. In the present work, we derive and detail a thorough theoretical analysis of an efficient consensus algorithm based on proximal gradient (PG) approach. The effectiveness of the proposed algorithm with respect to its ADMM counterpart is primarily assessed in the classic convolutional dictionary learning problem. Furthermore, our consensus method, which is generically structured, can be used to solve other optimization problems, where a sum of convex functions with a regularization term share a single global variable. As an example, the proposed algorithm is also applied to another particular convolutional problem for the anomaly detection task.
Deep Neural Networks using a Single Neuron: Folded-in-Time Architecture using Feedback-Modulated Delay Loops
Authors: Florian Stelzer, André Röhm, Raul Vicente, Ingo Fischer, Serhiy Yanchuk
Date: 2020-11-19 00:00:00

Deep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron's dynamics. By adjusting the feedback-modulation within the loops, we adapt the network's connection weights. These connection weights are determined via a back-propagation algorithm, where both the delay-induced and local network connections must be taken into account. Our approach can fully represent standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.