pwc-archive/papers-with-abstracts huggingface dataset:
FINED: Fast Inference Network for Edge Detection
Authors: Jan Kristanto Wibisono, Hsueh-Ming Hang
Date: 2020-12-15 00:00:00
Tasks: Deep Learning, Edge Detection, Object Detection, Semantic Segmentation

In this paper, we address the design of lightweight deep learning-based edge detection. The deep learning technology offers a significant improvement on the edge detection accuracy. However, typical neural network designs have very high model complexity, which prevents it from practical usage. In contrast, we propose a Fast Inference Network for Edge Detection (FINED), which is a lightweight neural net dedicated to edge detection. By carefully choosing proper components for edge detection purpose, we can achieve the state-of-the-art accuracy in edge detection while significantly reducing its complexity. Another key contribution in increasing the inferencing speed is introducing the training helper concept. The extra subnetworks (training helper) are employed in training but not used in inferencing. It can further reduce the model complexity and yet maintain the same level of accuracy. Our experiments show that our systems outperform all the current edge detectors at about the same model (parameter) size.
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets
Authors: Rui Gong, Dengxin Dai, Yuhua Chen, Wen Li, Luc van Gool
Date: 2020-12-15 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D Semantic Segmentation, Domain Adaptation, image-classification, Image Classification, Image Segmentation, Object Recognition, Segmentation, Semantic Segmentation

One challenge of object recognition is to generalize to new domains, to more classes and/or to new modalities. This necessitates methods to combine and reuse existing datasets that may belong to different domains, have partial annotations, and/or have different data modalities. This paper formulates this as a multi-source domain adaptation and label unification problem, and proposes a novel method for it. Our method consists of a partially-supervised adaptation stage and a fully-supervised adaptation stage. In the former, partial knowledge is transferred from multiple source domains to the target domain and fused therein. Negative transfer between unmatching label spaces is mitigated via three new modules: domain attention, uncertainty maximization and attention-guided adversarial alignment. In the latter, knowledge is transferred in the unified label space after a label completion process with pseudo-labels. Extensive experiments on three different tasks - image classification, 2D semantic image segmentation, and joint 2D-3D semantic segmentation - show that our method outperforms all competing methods significantly.
Keyword-Guided Neural Conversational Model
Authors: Peixiang Zhong, Yong liu, Hao Wang, Chunyan Miao
Date: 2020-12-15 00:00:00
Tasks: Knowledge Graphs, model, Retrieval, Word Embeddings

We study the problem of imposing conversational goals/keywords on open-domain conversational agents, where the agent is required to lead the conversation to a target keyword smoothly and fast. Solving this problem enables the application of conversational agents in many real-world scenarios, e.g., recommendation and psychotherapy. The dominant paradigm for tackling this problem is to 1) train a next-turn keyword classifier, and 2) train a keyword-augmented response retrieval model. However, existing approaches in this paradigm have two limitations: 1) the training and evaluation datasets for next-turn keyword classification are directly extracted from conversations without human annotations, thus, they are noisy and have low correlation with human judgements, and 2) during keyword transition, the agents solely rely on the similarities between word embeddings to move closer to the target keyword, which may not reflect how humans converse. In this paper, we assume that human conversations are grounded on commonsense and propose a keyword-guided neural conversational model that can leverage external commonsense knowledge graphs (CKG) for both keyword transition and response retrieval. Automatic evaluations suggest that commonsense improves the performance of both next-turn keyword prediction and keyword-augmented response retrieval. In addition, both self-play and human evaluations show that our model produces responses with smoother keyword transition and reaches the target keyword faster than competitive baselines.
Evolutionary Game Theory Squared: Evolving Agents in Endogenously Evolving Zero-Sum Games
Authors: Stratis Skoulakis, Tanner Fiez, Ryann Sim, Georgios Piliouras, Lillian Ratliff
Date: 2020-12-15 00:00:00

The predominant paradigm in evolutionary game theory and more generally online learning in games is based on a clear distinction between a population of dynamic agents that interact given a fixed, static game. In this paper, we move away from the artificial divide between dynamic agents and static games, to introduce and analyze a large class of competitive settings where both the agents and the games they play evolve strategically over time. We focus on arguably the most archetypal game-theoretic setting -- zero-sum games (as well as network generalizations) -- and the most studied evolutionary learning dynamic -- replicator, the continuous-time analogue of multiplicative weights. Populations of agents compete against each other in a zero-sum competition that itself evolves adversarially to the current population mixture. Remarkably, despite the chaotic coevolution of agents and games, we prove that the system exhibits a number of regularities. First, the system has conservation laws of an information-theoretic flavor that couple the behavior of all agents and games. Secondly, the system is Poincar\'{e} recurrent, with effectively all possible initializations of agents and games lying on recurrent orbits that come arbitrarily close to their initial conditions infinitely often. Thirdly, the time-average agent behavior and utility converge to the Nash equilibrium values of the time-average game. Finally, we provide a polynomial time algorithm to efficiently predict this time-average behavior for any such coevolving network game.
CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts
Authors: Peixiang Zhong, Di Wang, Pengfei Li, Chen Zhang, Hao Wang, Chunyan Miao
Date: 2020-12-15 00:00:00
Tasks: Response Generation

Rationality and emotion are two fundamental elements of humans. Endowing agents with rationality and emotion has been one of the major milestones in AI. However, in the field of conversational AI, most existing models only specialize in one aspect and neglect the other, which often leads to dull or unrelated responses. In this paper, we hypothesize that combining rationality and emotion into conversational agents can improve response quality. To test the hypothesis, we focus on one fundamental aspect of rationality, i.e., commonsense, and propose CARE, a novel model for commonsense-aware emotional response generation. Specifically, we first propose a framework to learn and construct commonsense-aware emotional latent concepts of the response given an input message and a desired emotion. We then propose three methods to collaboratively incorporate the latent concepts into response generation. Experimental results on two large-scale datasets support our hypothesis and show that our model can produce more accurate and commonsense-aware emotional responses and achieve better human ratings than state-of-the-art models that only specialize in one aspect.
Practical Auto-Calibration for Spatial Scene-Understanding from Crowdsourced Dashcamera Videos
Authors: Hemang Chawla, Matti Jukola, Shabbir Marzban, Elahe Arani, Bahram Zonooz
Date: 2020-12-15 00:00:00
Tasks: Autonomous Vehicles, Camera Auto-Calibration, Camera Calibration, Motion Estimation, Scene Understanding

Spatial scene-understanding, including dense depth and ego-motion estimation, is an important problem in computer vision for autonomous vehicles and advanced driver assistance systems. Thus, it is beneficial to design perception modules that can utilize crowdsourced videos collected from arbitrary vehicular onboard or dashboard cameras. However, the intrinsic parameters corresponding to such cameras are often unknown or change over time. Typical manual calibration approaches require objects such as a chessboard or additional scene-specific information. On the other hand, automatic camera calibration does not have such requirements. Yet, the automatic calibration of dashboard cameras is challenging as forward and planar navigation results in critical motion sequences with reconstruction ambiguities. Structure reconstruction of complete visual-sequences that may contain tens of thousands of images is also computationally untenable. Here, we propose a system for practical monocular onboard camera auto-calibration from crowdsourced videos. We show the effectiveness of our proposed system on the KITTI raw, Oxford RobotCar, and the crowdsourced D$^2$-City datasets in varying conditions. Finally, we demonstrate its application for accurate monocular dense depth and ego-motion estimation on uncalibrated videos.
A general framework for a joint calibration of VIX and VXX options
Authors: Martino Grasselli, Andrea Mazzoran, Andrea Pallavicini
Date: 2020-12-15 00:00:00

We analyze the VIX futures market with a focus on the exchange-traded notes written on such contracts, in particular we investigate the VXX notes tracking the short-end part of the futures term structure. Inspired by recent developments in commodity smile modelling, we present a multi-factor stochastic-local volatility model that is able to jointly calibrate plain vanilla options both on VIX futures and VXX notes, thus going beyond the failure of purely stochastic or simply local volatility models. We discuss numerical results on real market data by highlighting the impact of model parameters on implied volatilities.
Fundamental theorem of asset pricing with acceptable risk in markets with frictions
Authors: Maria Arduca, Cosimo Munari
Date: 2020-12-15 00:00:00

We study the range of prices at which a rational agent should contemplate transacting a financial contract outside a given securities market. Trading is subject to nonproportional transaction costs and portfolio constraints and full replication by way of market instruments is not always possible. Rationality is defined in terms of consistency with market prices and acceptable risk thresholds. We obtain a direct and a dual description of market-consistent prices with acceptable risk. The dual characterization requires an appropriate extension of the classical Fundamental Theorem of Asset Pricing where the role of arbitrage opportunities is played by good deals, i.e., costless investment opportunities with acceptable risk-reward tradeoff. In particular, we highlight the importance of scalable good deals, i.e., investment opportunities that are good deals regardless of their volume.
Screening for breakthroughs
Authors: Gregorio Curello, Ludvig Sinander
Date: 2020-11-19 00:00:00

How best to incentivise prompt disclosure? We study this question in a general model in which a technological breakthrough occurs at an uncertain time and is privately observed by an agent, and a principal must incentivise disclosure via her control of a payoff-relevant physical allocation. We uncover a deadline structure of optimal mechanisms: they have a simple deadline form in an important special case, and a graduated deadline structure in general. We apply our results to the design of unemployment insurance schemes.
Cost-Effective Federated Learning Design
Authors: Bing Luo, Xiang Li, Shiqiang Wang, Jianwei Huang, Leandros Tassiulas
Date: 2020-12-15 00:00:00
Tasks: Federated Learning

Federated learning (FL) is a distributed learning paradigm that enables a large number of devices to collaboratively learn a model without sharing their raw data. Despite its practical efficiency and effectiveness, the iterative on-device learning process incurs a considerable cost in terms of learning time and energy consumption, which depends crucially on the number of selected clients and the number of local iterations in each training round. In this paper, we analyze how to design adaptive FL that optimally chooses these essential control variables to minimize the total cost while ensuring convergence. Theoretically, we analytically establish the relationship between the total cost and the control variables with the convergence upper bound. To efficiently solve the cost minimization problem, we develop a low-cost sampling-based algorithm to learn the convergence related unknown parameters. We derive important solution properties that effectively identify the design principles for different metric preferences. Practically, we evaluate our theoretical results both in a simulated environment and on a hardware prototype. Experimental evidence verifies our derived properties and demonstrates that our proposed solution achieves near-optimal performance for various datasets, different machine learning models, and heterogeneous system settings.
Masksembles for Uncertainty Estimation
Authors: Nikita Durasov, Timur Bagautdinov, Pierre Baque, Pascal Fua
Date: 2020-12-15 00:00:00
Proceeding: CVPR 2021 1
Tasks: Classifier calibration, Ensemble Learning, Out-of-Distribution Detection, Robust classification, Uncertainty Quantification

Deep neural networks have amply demonstrated their prowess but estimating the reliability of their predictions remains challenging. Deep Ensembles are widely considered as being one of the best methods for generating uncertainty estimates but are very expensive to train and evaluate. MC-Dropout is another popular alternative, which is less expensive, but also less reliable. Our central intuition is that there is a continuous spectrum of ensemble-like models of which MC-Dropout and Deep Ensembles are extreme examples. The first uses an effectively infinite number of highly correlated models while the second relies on a finite number of independent models. To combine the benefits of both, we introduce Masksembles. Instead of randomly dropping parts of the network as in MC-dropout, Masksemble relies on a fixed number of binary masks, which are parameterized in a way that allows to change correlations between individual models. Namely, by controlling the overlap between the masks and their density one can choose the optimal configuration for the task at hand. This leads to a simple and easy to implement method with performance on par with Ensembles at a fraction of the cost. We experimentally validate Masksembles on two widely used datasets, CIFAR10 and ImageNet.
39 Hints to Facilitate the Use of Semantics for Data on Agriculture and Nutrition
Authors: Caterina Caracciolo, Sophie Aubin, Clement Jonquet, Emna Amdouni, Romain David, Leyla Garcia, Brandon Whitehead, Catherine Roussey, Armando Stellato, Ferdinando Villa
Date: 2020-12-15 00:00:00
Tasks: Nutrition

In this paper, we report on the outputs and adoption of the Agrisemantics Working Group of the Research Data Alliance (RDA), consisting of a set of recommendations to facilitate the adoption of semantic technologies and methods for the purpose of data interoperability in the field of agriculture and nutrition. From 2016 to 2019, the group gathered researchers and practitioners at the crossing point between information technology and agricultural science, to study all aspects in the life cycle of semantic resources: conceptualization, edition, sharing, standardization, services, alignment, long term support. First, the working group realized a landscape study, a study of the uses of semantics in agrifood, then collected use cases for the exploitation of semantics resources-a generic term to encompass vocabularies, terminologies, thesauri, ontologies. The resulting requirements were synthesized into 39 "hints" for users and developers of semantic resources, and providers of semantic resource services. We believe adopting these recommendations will engage agrifood sciences in a necessary transition to leverage data production, sharing and reuse and the adoption of the FAIR data principles. The paper includes examples of adoption of those requirements, and a discussion of their contribution to the field of data science.
Improved Image Matting via Real-time User Clicks and Uncertainty Estimation
Authors: Tianyi Wei, Dongdong Chen, Wenbo Zhou, Jing Liao, Hanqing Zhao, Weiming Zhang, Nenghai Yu
Date: 2020-12-15 00:00:00
Proceeding: CVPR 2021 1
Tasks: Image Matting

Image matting is a fundamental and challenging problem in computer vision and graphics. Most existing matting methods leverage a user-supplied trimap as an auxiliary input to produce good alpha matte. However, obtaining high-quality trimap itself is arduous, thus restricting the application of these methods. Recently, some trimap-free methods have emerged, however, the matting quality is still far behind the trimap-based methods. The main reason is that, without the trimap guidance in some cases, the target network is ambiguous about which is the foreground target. In fact, choosing the foreground is a subjective procedure and depends on the user's intention. To this end, this paper proposes an improved deep image matting framework which is trimap-free and only needs several user click interactions to eliminate the ambiguity. Moreover, we introduce a new uncertainty estimation module that can predict which parts need polishing and a following local refinement module. Based on the computation budget, users can choose how many local parts to improve with the uncertainty guidance. Quantitative and qualitative results show that our method performs better than existing trimap-free methods and comparably to state-of-the-art trimap-based methods with minimal user effort.
QUARC: Quaternion Multi-Modal Fusion Architecture For Hate Speech Classification
Authors: Deepak Kumar, Nalin Kumar, Subhankar Mishra
Date: 2020-12-15 00:00:00
Tasks: Classification, General Classification

Hate speech, quite common in the age of social media, at times harmless but can also cause mental trauma to someone or even riots in communities. Image of a religious symbol with derogatory comment or video of a man abusing a particular community, all become hate speech with its every modality (such as text, image, and audio) contributing towards it. Models based on a particular modality of hate speech post on social media are not useful, rather, we need models like multi-modal fusion models that consider both image and text while classifying hate speech. Text-image fusion models are heavily parameterized, hence we propose a quaternion neural network-based model having additional fusion components for each pair of modalities. The model is tested on the MMHS150K twitter dataset for hate speech classification. The model shows an almost 75% reduction in parameters and also benefits us in terms of storage space and training time while being at par in terms of performance as compared to its real counterpart.
BiSNN: Training Spiking Neural Networks with Binary Weights via Bayesian Learning
Authors: Hyeryung Jang, Nicolas Skatchkovsky, Osvaldo Simeone
Date: 2020-12-15 00:00:00

Artificial Neural Network (ANN)-based inference on battery-powered devices can be made more energy-efficient by restricting the synaptic weights to be binary, hence eliminating the need to perform multiplications. An alternative, emerging, approach relies on the use of Spiking Neural Networks (SNNs), biologically inspired, dynamic, event-driven models that enhance energy efficiency via the use of binary, sparse, activations. In this paper, an SNN model is introduced that combines the benefits of temporally sparse binary activations and of binary weights. Two learning rules are derived, the first based on the combination of straight-through and surrogate gradient techniques, and the second based on a Bayesian paradigm. Experiments validate the performance loss with respect to full-precision implementations, and demonstrate the advantage of the Bayesian paradigm in terms of accuracy and calibration.
Gegelati: Lightweight Artificial Intelligence through Generic and Evolvable Tangled Program Graphs
Authors: Karol Desnos, Nicolas Sourbier, Pierre-Yves Raumer, Olivier Gesny, Maxime Pelcat
Date: 2020-12-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Tangled Program Graph (TPG) is a reinforcement learning technique based on genetic programming concepts. On state-of-the-art learning environments, TPGs have been shown to offer comparable competence with Deep Neural Networks (DNNs), for a fraction of their computational and storage cost. This lightness of TPGs, both for training and inference, makes them an interesting model to implement Artificial Intelligences (AIs) on embedded systems with limited computational and storage resources. In this paper, we introduce the Gegelati library for TPGs. Besides introducing the general concepts and features of the library, two main contributions are detailed in the paper: 1/ The parallelization of the deterministic training process of TPGs, for supporting heterogeneous Multiprocessor Systems-on-Chips (MPSoCs). 2/ The support for customizable instruction sets and data types within the genetically evolved programs of the TPG model. The scalability of the parallel training process is demonstrated through experiments on architectures ranging from a high-end 24-core processor to a low-power heterogeneous MPSoC. The impact of customizable instructions on the outcome of a training process is demonstrated on a state-of-the-art reinforcement learning environment. CCS Concepts: $\bullet$ Computer systems organization $\rightarrow$ Embedded systems; $\bullet$ Computing methodologies $\rightarrow$ Machine learning.
Approximation of BV functions by neural networks: A regularity theory approach
Authors: Benny Avelin, Vesa Julin
Date: 2020-12-15 00:00:00

In this paper we are concerned with the approximation of functions by single hidden layer neural networks with ReLU activation functions on the unit circle. In particular, we are interested in the case when the number of data-points exceeds the number of nodes. We first study the convergence to equilibrium of the stochastic gradient flow associated with the cost function with a quadratic penalization. Specifically, we prove a Poincar\'e inequality for a penalized version of the cost function with explicit constants that are independent of the data and of the number of nodes. As our penalization biases the weights to be bounded, this leads us to study how well a network with bounded weights can approximate a given function of bounded variation (BV). Our main contribution concerning approximation of BV functions, is a result which we call the localization theorem. Specifically, it states that the expected error of the constrained problem, where the length of the weights are less than $R$, is of order $R^{-1/9}$ with respect to the unconstrained problem (the global optimum). The proof is novel in this topic and is inspired by techniques from regularity theory of elliptic partial differential equations. Finally we quantify the expected value of the global optimum by proving a quantitative version of the universal approximation theorem.
Enhance Multimodal Transformer With External Label And In-Domain Pretrain: Hateful Meme Challenge Winning Solution
Authors: Ron Zhu
Date: 2020-12-15 00:00:00
Tasks: Meme Classification

Hateful meme detection is a new research area recently brought out that requires both visual, linguistic understanding of the meme and some background knowledge to performing well on the task. This technical report summarises the first place solution of the Hateful Meme Detection Challenge 2020, which extending state-of-the-art visual-linguistic transformers to tackle this problem. At the end of the report, we also point out the shortcomings and possible directions for improving the current methodology.