pwc-archive/papers-with-abstracts huggingface dataset:
On Practical Reinforcement Learning: Provable Robustness, Scalability, and Statistical Efficiency
Authors: Thanh Nguyen-Tang
Date: 2022-03-03 00:00:00
Tasks: Offline RL, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This thesis rigorously studies fundamental reinforcement learning (RL) methods in modern practical considerations, including robust RL, distributional RL, and offline RL with neural function approximation. The thesis first prepares the readers with an overall overview of RL and key technical background in statistics and optimization. In each of the settings, the thesis motivates the problems to be studied, reviews the current literature, provides computationally efficient algorithms with provable efficiency guarantees, and concludes with future research directions. The thesis makes fundamental contributions to the three settings above, both algorithmically, theoretically, and empirically, while staying relevant to practical considerations.
Machine learning model to project the impact of Ukraine crisis
Authors: Javad T. Firouzjaee, Pouriya Khaliliyan
Date: 2022-03-03 00:00:00
Tasks: BIG-bench Machine Learning, regression

Russia's attack on Ukraine on Thursday 24 February 2022 hitched financial markets and the increased geopolitical crisis. In this paper, we select some main economic indexes, such as Gold, Oil (WTI), NDAQ, and known currency which are involved in this crisis and try to find the quantitative effect of this war on them. To quantify the war effect, we use the correlation feature and the relationships between these economic indices, create datasets, and compare the results of forecasts with real data. To study war effects, we use Machine Learning Linear Regression. We carry on empirical experiments and perform on these economic indices datasets to evaluate and predict this war tolls and its effects on main economics indexes.
Monitoring Diversity of AI Conferences: Lessons Learnt and Future Challenges in the DivinAI Project
Authors: Isabelle Hupont, Emilia Gomez, Songul Tolan, Lorenzo Porcaro, Ana Freire
Date: 2022-03-03 00:00:00
Tasks: Diversity

DivinAI is an open and collaborative initiative promoted by the European Commission's Joint Research Centre to measure and monitor diversity indicators related to AI conferences, with special focus on gender balance, geographical representation, and presence of academia vs companies. This paper summarizes the main achievements and lessons learnt during the first year of life of the DivinAI project, and proposes a set of recommendations for its further development and maintenance by the AI community.
Optimized cost function for demand response coordination of multiple EV charging stations using reinforcement learning
Authors: Manu Lahariya, Nasrin Sadeghianpourhamami, Chris Develder
Date: 2022-03-03 00:00:00
Tasks: Reinforcement Learning (RL)

Electric vehicle (EV) charging stations represent a substantial load with significant flexibility. The exploitation of that flexibility in demand response (DR) algorithms becomes increasingly important to manage and balance demand and supply in power grids. Model-free DR based on reinforcement learning (RL) is an attractive approach to balance such EV charging load. We build on previous research on RL, based on a Markov decision process (MDP) to simultaneously coordinate multiple charging stations. However, we note that the computationally expensive cost function adopted in the previous research leads to large training times, which limits the feasibility and practicality of the approach. We, therefore, propose an improved cost function that essentially forces the learned control policy to always fulfill any charging demand that does not offer any flexibility. We rigorously compare the newly proposed batch RL fitted Q-iteration implementation with the original (costly) one, using real-world data. Specifically, for the case of load flattening, we compare the two approaches in terms of (i) the processing time to learn the RL-based charging policy, as well as (ii) the overall performance of the policy decisions in terms of meeting the target load for unseen test data. The performance is analyzed for different training periods and varying training sample sizes. In addition to both RL policies performance results, we provide performance bounds in terms of both (i) an optimal all-knowing strategy, and (ii) a simple heuristic spreading individual EV charging uniformly over time
Selective Residual M-Net for Real Image Denoising
Authors: Chi-Mao Fan, Tsung-Jung Liu, Kuan-Hsien Liu
Date: 2022-03-03 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration

Image restoration is a low-level vision task which is to restore degraded images to noise-free images. With the success of deep neural networks, the convolutional neural networks surpass the traditional restoration methods and become the mainstream in the computer vision area. To advance the performanceof denoising algorithms, we propose a blind real image denoising network (SRMNet) by employing a hierarchical architecture improved from U-Net. Specifically, we use a selective kernel with residual block on the hierarchical structure called M-Net to enrich the multi-scale semantic information. Furthermore, our SRMNet has competitive performance results on two synthetic and two real-world noisy datasets in terms of quantitative metrics and visual quality. The source code and pretrained model are available at https://github.com/TentativeGitHub/SRMNet.
Risk-aware Stochastic Shortest Path
Authors: Tobias Meggendorfer
Date: 2022-03-03 00:00:00

We treat the problem of risk-aware control for stochastic shortest path (SSP) on Markov decision processes (MDP). Typically, expectation is considered for SSP, which however is oblivious to the incurred risk. We present an alternative view, instead optimizing conditional value-at-risk (CVaR), an established risk measure. We treat both Markov chains as well as MDP and introduce, through novel insights, two algorithms, based on linear programming and value iteration, respectively. Both algorithms offer precise and provably correct solutions. Evaluation of our prototype implementation shows that risk-aware control is feasible on several moderately sized models.
Early Time-Series Classification Algorithms: An Empirical Comparison
Authors: Charilaos Akasiadis, Evgenios Kladis, Evangelos Michelioudakis, Elias Alevizos, Alexander Artikis
Date: 2022-03-03 00:00:00
Tasks: Classification, Time Series, Time Series Analysis, Time Series Classification

Early Time-Series Classification (ETSC) is the task of predicting the class of incoming time-series by observing as few measurements as possible. Such methods can be employed to obtain classification forecasts in many time-critical applications. However, available techniques are not equally suitable for every problem, since differentiations in the data characteristics can impact algorithm performance in terms of earliness, accuracy, F1-score, and training time. We evaluate six existing ETSC algorithms on publicly available data, as well as on two newly introduced datasets originating from the life sciences and maritime domains. Our goal is to provide a framework for the evaluation and comparison of ETSC algorithms and to obtain intuition on how such approaches perform on real-life applications. The presented framework may also serve as a benchmark for new related techniques.
Nanopublication-Based Semantic Publishing and Reviewing: A Field Study with Formalization Papers
Authors: Cristina-Iulia Bucur, Tobias Kuhn, Davide Ceolin, Jacco van Ossenbruggen
Date: 2022-03-03 00:00:00
Tasks: Articles, Formal Logic

With the rapidly increasing amount of scientific literature,it is getting continuously more difficult for researchers in different disciplines to be updated with the recent findings in their field of study.Processing scientific articles in an automated fashion has been proposed as a solution to this problem,but the accuracy of such processing remains very poor for extraction tasks beyond the basic ones.Few approaches have tried to change how we publish scientific results in the first place,by making articles machine-interpretable by expressing them with formal semantics from the start.In the work presented here,we set out to demonstrate that we can formally publish high-level scientific claims in formal logic,and publish the results in a special issue of an existing journal.We use the concept and technology of nanopublications for this endeavor,and represent not just the submissions and final papers in this RDF-based format,but also the whole process in between,including reviews,responses,and decisions.We do this by performing a field study with what we call formalization papers,which contribute a novel formalization of a previously published claim.We received 15 submissions from 18 authors,who then went through the whole publication process leading to the publication of their contributions in the special issue.Our evaluation shows the technical and practical feasibility of our approach.The participating authors mostly showed high levels of interest and confidence,and mostly experienced the process as not very difficult,despite the technical nature of the current user interfaces.We believe that these results indicate that it is possible to publish scientific results from different fields with machine-interpretable semantics from the start,which in turn opens countless possibilities to radically improve in the future the effectiveness and efficiency of the scientific endeavor as a whole.
Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models
Authors: Zhibo Wang, Xiaowei Dong, Henry Xue, Zhifei Zhang, Weifeng Chiu, Tao Wei, Kui Ren
Date: 2022-03-03 00:00:00
Proceeding: CVPR 2022 1
Tasks: Fairness

Prioritizing fairness is of central importance in artificial intelligence (AI) systems, especially for those societal applications, e.g., hiring systems should recommend applicants equally from different demographic groups, and risk assessment systems must eliminate racism in criminal justice. Existing efforts towards the ethical development of AI systems have leveraged data science to mitigate biases in the training set or introduced fairness principles into the training process. For a deployed AI system, however, it may not allow for retraining or tuning in practice. By contrast, we propose a more flexible approach, i.e., fairness-aware adversarial perturbation (FAAP), which learns to perturb input data to blind deployed models on fairness-related features, e.g., gender and ethnicity. The key advantage is that FAAP does not modify deployed models in terms of parameters and structures. To achieve this, we design a discriminator to distinguish fairness-related attributes based on latent representations from deployed models. Meanwhile, a perturbation generator is trained against the discriminator, such that no fairness-related features could be extracted from perturbed inputs. Exhaustive experimental evaluation demonstrates the effectiveness and superior performance of the proposed FAAP. In addition, FAAP is validated on real-world commercial deployments (inaccessible to model parameters), which shows the transferability of FAAP, foreseeing the potential of black-box adaptation.
DareFightingICE Competition: A Fighting Game Sound Design and AI Competition
Authors: Ibrahim Khan, Thai Van Nguyen, Xincheng Dai, Ruck Thawonmas
Date: 2022-03-03 00:00:00

This paper presents a new competition -- at the 2022 IEEE Conference on Games (CoG) -- called DareFightingICE Competition. The competition has two tracks: a sound design track and an AI track. The game platform for this competition is also called DareFightingICE, a fighting game platform. DareFightingICE is a sound-design-enhanced version of FightingICE, used earlier in a competition at CoG until 2021 to promote artificial intelligence (AI) research in fighting games. In the sound design track, participants compete for the best sound design, given the default sound design of DareFightingICE as a sample, where we define a sound design as a set of sound effects combined with the source code that implements their timing-control algorithm. Participants of the AI track are asked to develop their AI algorithm that controls a character given only sound as the input (blind AI) to fight against their opponent; a sample deep-learning blind AI will be provided by us. Our means to maximize the synergy between the two tracks are also described. This competition serves to come up with effective sound designs for visually impaired players, a group in the gaming community which has been mostly ignored. To the best of our knowledge, DareFightingICE Competition is the first of its kind within and outside of CoG.
Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking
Authors: Jamin Shin, Hangyeol Yu, Hyeongdon Moon, Andrea Madotto, Juneyoung Park
Date: 2022-03-03 00:00:00
Proceeding: Findings (ACL) 2022 5
Tasks: Abstractive Dialogue Summarization, Dialogue State Tracking, Language Modeling, Language Modelling, Multi-domain Dialogue State Tracking

Annotating task-oriented dialogues is notorious for the expensive and difficult data collection process. Few-shot dialogue state tracking (DST) is a realistic solution to this problem. In this paper, we hypothesize that dialogue summaries are essentially unstructured dialogue states; hence, we propose to reformulate dialogue state tracking as a dialogue summarization problem. To elaborate, we train a text-to-text language model with synthetic template-based dialogue summaries, generated by a set of rules from the dialogue states. Then, the dialogue states can be recovered by inversely applying the summary generation rules. We empirically show that our method DS2 outperforms previous works on few-shot DST in MultiWoZ 2.0 and 2.1, in both cross-domain and multi-domain settings. Our method also exhibits vast speedup during both training and inference as it can generate all states at once. Finally, based on our analysis, we discover that the naturalness of the summary templates plays a key role for successful training.
Rethinking the role of normalization and residual blocks for spiking neural networks
Authors: Shin-ichi Ikegawa, Ryuji Saiin, Yoshihide Sawada, Naotake Natori
Date: 2022-03-03 00:00:00

Biologically inspired spiking neural networks (SNNs) are widely used to realize ultralow-power energy consumption. However, deep SNNs are not easy to train due to the excessive firing of spiking neurons in the hidden layers. To tackle this problem, we propose a novel but simple normalization technique called postsynaptic potential normalization. This normalization removes the subtraction term from the standard normalization and uses the second raw moment instead of the variance as the division term. The spike firing can be controlled, enabling the training to proceed appropriating, by conducting this simple normalization to the postsynaptic potential. The experimental results show that SNNs with our normalization outperformed other models using other normalizations. Furthermore, through the pre-activation residual blocks, the proposed model can train with more than 100 layers without other special techniques dedicated to SNNs.
QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition
Authors: Andy T. Liu, Wei Xiao, Henghui Zhu, Dejiao Zhang, Shang-Wen Li, Andrew Arnold
Date: 2022-03-03 00:00:00
Tasks: Few-shot NER, Named Entity Recognition, Named Entity Recognition (NER), Prompt Engineering, Question Answering

Recently, prompt-based learning for pre-trained language models has succeeded in few-shot Named Entity Recognition (NER) by exploiting prompts as task guidance to increase label efficiency. However, previous prompt-based methods for few-shot NER have limitations such as a higher computational complexity, poor zero-shot ability, requiring manual prompt engineering, or lack of prompt robustness. In this work, we address these shortcomings by proposing a new prompt-based learning NER method with Question Answering (QA), called QaNER. Our approach includes 1) a refined strategy for converting NER problems into the QA formulation; 2) NER prompt generation for QA models; 3) prompt-based tuning with QA models on a few annotated NER examples; 4) zero-shot NER by prompting the QA model. Comparing the proposed approach with previous methods, QaNER is faster at inference, insensitive to the prompt quality, and robust to hyper-parameters, as well as demonstrating significantly better low-resource performance and zero-shot capability.
Recent Advances in Vision Transformer: A Survey and Outlook of Recent Work
Authors: Khawar Islam
Date: 2022-03-03 00:00:00

Vision Transformers (ViTs) are becoming more popular and dominating technique for various vision tasks, compare to Convolutional Neural Networks (CNNs). As a demanding technique in computer vision, ViTs have been successfully solved various vision problems while focusing on long-range relationships. In this paper, we begin by introducing the fundamental concepts and background of the self-attention mechanism. Next, we provide a comprehensive overview of recent top-performing ViT methods describing in terms of strength and weakness, computational cost as well as training and testing dataset. We thoroughly compare the performance of various ViT algorithms and most representative CNN methods on popular benchmark datasets. Finally, we explore some limitations with insightful observations and provide further research direction. The project page along with the collections of papers are available at https://github.com/khawar512/ViT-Survey
An Open Challenge for Inductive Link Prediction on Knowledge Graphs
Authors: Mikhail Galkin, Max Berrendorf, Charles Tapley Hoyt
Date: 2022-03-03 00:00:00
Tasks: Graph Representation Learning, Inductive Link Prediction, Knowledge Graphs, Link Prediction, Representation Learning

An emerging trend in representation learning over knowledge graphs (KGs) moves beyond transductive link prediction tasks over a fixed set of known entities in favor of inductive tasks that imply training on one graph and performing inference over a new graph with unseen entities. In inductive setups, node features are often not available and training shallow entity embedding matrices is meaningless as they cannot be used at inference time with unseen entities. Despite the growing interest, there are not enough benchmarks for evaluating inductive representation learning methods. In this work, we introduce ILPC 2022, a novel open challenge on KG inductive link prediction. To this end, we constructed two new datasets based on Wikidata with various sizes of training and inference graphs that are much larger than existing inductive benchmarks. We also provide two strong baselines leveraging recently proposed inductive methods. We hope this challenge helps to streamline community efforts in the inductive graph representation learning area. ILPC 2022 follows best practices on evaluation fairness and reproducibility, and is available at https://github.com/pykeen/ilpc2022.
Deep Q-network using reservoir computing with multi-layered readout
Authors: Toshitaka Matsuki
Date: 2022-03-03 00:00:00
Tasks: Reinforcement Learning (RL), Time Series, Time Series Analysis

Recurrent neural network (RNN) based reinforcement learning (RL) is used for learning context-dependent tasks and has also attracted attention as a method with remarkable learning performance in recent research. However, RNN-based RL has some issues that the learning procedures tend to be more computationally expensive, and training with backpropagation through time (BPTT) is unstable because of vanishing/exploding gradients problem. An approach with replay memory introducing reservoir computing has been proposed, which trains an agent without BPTT and avoids these issues. The basic idea of this approach is that observations from the environment are input to the reservoir network, and both the observation and the reservoir output are stored in the memory. This paper shows that the performance of this method improves by using a multi-layered neural network for the readout layer, which regularly consists of a single linear layer. The experimental results show that using multi-layered readout improves the learning performance of four classical control tasks that require time-series processing.
SPICEprop: Backpropagating Errors Through Memristive Spiking Neural Networks
Authors: Peng Zhou, Jason K. Eshraghian, Dong-Uk Choi, Sung-Mo Kang
Date: 2022-03-02 00:00:00

We present a fully memristive spiking neural network (MSNN) consisting of novel memristive neurons trained using the backpropagation through time (BPTT) learning rule. Gradient descent is applied directly to the memristive integrated-and-fire (MIF) neuron designed using analog SPICE circuit models, which generates distinct depolarization, hyperpolarization, and repolarization voltage waveforms. Synaptic weights are trained by BPTT using the membrane potential of the MIF neuron model and can be processed on memristive crossbars. The natural spiking dynamics of the MIF neuron model are fully differentiable, eliminating the need for gradient approximations that are prevalent in the spiking neural network literature. Despite the added complexity of training directly on SPICE circuit models, we achieve 97.58% accuracy on the MNIST testing dataset and 75.26% on the Fashion-MNIST testing dataset, the highest accuracies among all fully MSNNs.
A Fully Memristive Spiking Neural Network with Unsupervised Learning
Authors: Peng Zhou, Dong-Uk Choi, Jason K. Eshraghian, Sung-Mo Kang
Date: 2022-03-02 00:00:00
Tasks: Multi-class Classification, Retrieval

We present a fully memristive spiking neural network (MSNN) consisting of physically-realizable memristive neurons and memristive synapses to implement an unsupervised Spiking Time Dependent Plasticity (STDP) learning rule. The system is fully memristive in that both neuronal and synaptic dynamics can be realized by using memristors. The neuron is implemented using the SPICE-level memristive integrate-and-fire (MIF) model, which consists of a minimal number of circuit elements necessary to achieve distinct depolarization, hyperpolarization, and repolarization voltage waveforms. The proposed MSNN uniquely implements STDP learning by using cumulative weight changes in memristive synapses from the voltage waveform changes across the synapses, which arise from the presynaptic and postsynaptic spiking voltage signals during the training process. Two types of MSNN architectures are investigated: 1) a biologically plausible memory retrieval system, and 2) a multi-class classification system. Our circuit simulation results verify the MSNN's unsupervised learning efficacy by replicating biological memory retrieval mechanisms, and achieving 97.5% accuracy in a 4-pattern recognition problem in a large scale discriminative MSNN.