pwc-archive/papers-with-abstracts huggingface dataset:
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning
Authors: Jaesik Yoon, Yi-Fu Wu, Heechul Bae, Sungjin Ahn
Date: 2023-02-09 00:00:00
Tasks: Object, Optical Character Recognition (OCR), Out-of-Distribution Generalization, Property Prediction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Systematic Generalization

Unsupervised object-centric representation (OCR) learning has recently drawn attention as a new paradigm of visual representation. This is because of its potential of being an effective pre-training technique for various downstream tasks in terms of sample efficiency, systematic generalization, and reasoning. Although image-based reinforcement learning (RL) is one of the most important and thus frequently mentioned such downstream tasks, the benefit in RL has surprisingly not been investigated systematically thus far. Instead, most of the evaluations have focused on rather indirect metrics such as segmentation quality and object property prediction accuracy. In this paper, we investigate the effectiveness of OCR pre-training for image-based reinforcement learning via empirical experiments. For systematic evaluation, we introduce a simple object-centric visual RL benchmark and conduct experiments to answer questions such as ``Does OCR pre-training improve performance on object-centric tasks?'' and ``Can OCR pre-training help with out-of-distribution generalization?''. Our results provide empirical evidence for valuable insights into the effectiveness of OCR pre-training for RL and the potential limitations of its use in certain scenarios. Additionally, this study also examines the critical aspects of incorporating OCR pre-training in RL, including performance in a visually complex environment and the appropriate pooling layer to aggregate the object representations.
Few-Shot Table-to-Text Generation with Prompt Planning and Knowledge Memorization
Authors: Zhixin Guo, Minyxuan Yan, Jiexing Qi, Jianping Zhou, Ziwei He, Zhouhan Lin, Guanjie Zheng, Xinbing Wang
Date: 2023-02-09 00:00:00
Tasks: Memorization, Table-to-Text Generation, Text Generation

Pre-trained language models (PLM) have achieved remarkable advancement in table-to-text generation tasks. However, the lack of labeled domain-specific knowledge and the topology gap between tabular data and text make it difficult for PLMs to yield faithful text. Low-resource generation likewise faces unique challenges in this domain. Inspired by how humans descript tabular data with prior knowledge, we suggest a new framework: PromptMize, which targets table-to-text generation under few-shot settings. The design of our framework consists of two aspects: a prompt planner and a knowledge adapter. The prompt planner aims to generate a prompt signal that provides instance guidance for PLMs to bridge the topology gap between tabular data and text. Moreover, the knowledge adapter memorizes domain-specific knowledge from the unlabelled corpus to supply essential information during generation. Extensive experiments and analyses are investigated on three open domain few-shot NLG datasets: human, song, and book. Compared with previous state-of-the-art approaches, our model achieves remarkable performance in generating quality as judged by human and automatic evaluations.
Geometry of Score Based Generative Models
Authors: Sandesh Ghimire, Jinyang Liu, Armand Comas, Davin Hill, Aria Masoomi, Octavia Camps, Jennifer Dy
Date: 2023-02-09 00:00:00
Tasks: Bayesian Inference

In this work, we look at Score-based generative models (also called diffusion generative models) from a geometric perspective. From a new view point, we prove that both the forward and backward process of adding noise and generating from noise are Wasserstein gradient flow in the space of probability measures. We are the first to prove this connection. Our understanding of Score-based (and Diffusion) generative models have matured and become more complete by drawing ideas from different fields like Bayesian inference, control theory, stochastic differential equation and Schrodinger bridge. However, many open questions and challenges remain. One problem, for example, is how to decrease the sampling time? We demonstrate that looking from geometric perspective enables us to answer many of these questions and provide new interpretations to some known results. Furthermore, geometric perspective enables us to devise an intuitive geometric solution to the problem of faster sampling. By augmenting traditional score-based generative models with a projection step, we show that we can generate high quality images with significantly fewer sampling-steps.
Simulation-to-reality UAV Fault Diagnosis with Deep Learning
Authors: Wei zhang, Junjie Tong, Fang Liao, Yunfeng Zhang
Date: 2023-02-09 00:00:00
Tasks: Deep Learning, Domain Adaptation, Fault Diagnosis

Accurate diagnosis of propeller faults is crucial for ensuring the safe and efficient operation of quadrotors. Training a fault classifier using simulated data and deploying it on a real quadrotor is a cost-effective and safe approach. However, the simulation-to-reality gap often leads to poor performance of the classifier when applied in real flight. In this work, we propose a deep learning model that addresses this issue by utilizing newly identified features (NIF) as input and utilizing domain adaptation techniques to reduce the simulation-to-reality gap. In addition, we introduce an adjusted simulation model that generates training data that more accurately reflects the behavior of real quadrotors. The experimental results demonstrate that our proposed approach achieves an accuracy of 96\% in detecting propeller faults. To the best of our knowledge, this is the first reliable and efficient method for simulation-to-reality fault diagnosis of quadrotor propellers.
Neural Architecture Search via Two Constant Shared Weights Initialisations
Authors: Ekaterina Gracheva
Date: 2023-02-09 00:00:00
Tasks: Neural Architecture Search

In recent years, zero-cost metrics are gaining ground in neural architecture search (NAS). There metrics allow finding the optimal neural network for a given task faster and with a lesser computational load than conventional NAS methods. Equally important is that they also shed some light on the internal workings of neural architectures. This paper presents a zero-cost metric that highly correlated with the train set accuracy across the NAS-Bench-101, NAS-Bench-201 and NAS-Bench-NLP benchmark datasets. We evaluate a neural achitecture's potential based on the outputs' statistics after two constant shared weights initialisations. For this, we only use an unlabelled mini-batch of data. We observe that the dispersion of the outputs between two initialisations positively correlates with trained accuracy. The correlation further improves when we normalise dispersion by average output magnitude. The resulting metric, epsilon, does not require gradients computation and unbinds the NAS procedure from training hyperparameters, loss metrics and human-labelled data. Our method is easy to integrate within existing NAS algorithms and takes a fraction of a second to evaluate a single network. The code supporting this study can be found on GitHub at https://github.com/egracheva/epsinas.
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning
Authors: Volodymyr Tkachuk, Seyed Alireza Bakhtiari, Johannes Kirschner, Matej Jusup, Ilija Bogunovic, Csaba Szepesvári
Date: 2023-02-08 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A practical challenge in reinforcement learning are combinatorial action spaces that make planning computationally demanding. For example, in cooperative multi-agent reinforcement learning, a potentially large number of agents jointly optimize a global reward function, which leads to a combinatorial blow-up in the action space by the number of agents. As a minimal requirement, we assume access to an argmax oracle that allows to efficiently compute the greedy policy for any Q-function in the model class. Building on recent work in planning with local access to a simulator and linear function approximation, we propose efficient algorithms for this setting that lead to polynomial compute and query complexity in all relevant problem parameters. For the special case where the feature decomposition is additive, we further improve the bounds and extend the results to the kernelized setting with an efficient algorithm.
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints
Authors: Ming Shi, Yingbin Liang, Ness Shroff
Date: 2023-02-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL), Safe Reinforcement Learning

In many applications of Reinforcement Learning (RL), it is critically important that the algorithm performs safely, such that instantaneous hard constraints are satisfied at each step, and unsafe states and actions are avoided. However, existing algorithms for ''safe'' RL are often designed under constraints that either require expected cumulative costs to be bounded or assume all states are safe. Thus, such algorithms could violate instantaneous hard constraints and traverse unsafe states (and actions) in practice. Therefore, in this paper, we develop the first near-optimal safe RL algorithm for episodic Markov Decision Processes with unsafe states and actions under instantaneous hard constraints and the linear mixture model. It not only achieves a regret $\tilde{O}(\frac{d H^3 \sqrt{dK}}{\Delta_c})$ that tightly matches the state-of-the-art regret in the setting with only unsafe actions and nearly matches that in the unconstrained setting, but is also safe at each step, where $d$ is the feature-mapping dimension, $K$ is the number of episodes, $H$ is the number of steps in each episode, and $\Delta_c$ is a safety-related parameter. We also provide a lower bound $\tilde{\Omega}(\max\{dH \sqrt{K}, \frac{H}{\Delta_c^2}\})$, which indicates that the dependency on $\Delta_c$ is necessary. Further, both our algorithm design and regret analysis involve several novel ideas, which may be of independent interest.
MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model
Authors: Huan He, Shifan Zhao, Yuanzhe Xi, Joyce C Ho
Date: 2023-02-08 00:00:00
Tasks: Denoising

Due to patient privacy protection concerns, machine learning research in healthcare has been undeniably slower and limited than in other application domains. High-quality, realistic, synthetic electronic health records (EHRs) can be leveraged to accelerate methodological developments for research purposes while mitigating privacy concerns associated with data sharing. The current state-of-the-art model for synthetic EHR generation is generative adversarial networks, which are notoriously difficult to train and can suffer from mode collapse. Denoising Diffusion Probabilistic Models, a class of generative models inspired by statistical thermodynamics, have recently been shown to generate high-quality synthetic samples in certain domains. It is unknown whether these can generalize to generation of large-scale, high-dimensional EHRs. In this paper, we present a novel generative model based on diffusion models that is the first successful application on electronic health records. Our model proposes a mechanism to perform class-conditional sampling to preserve label information. We also introduce a new sampling strategy to accelerate the inference speed. We empirically show that our model outperforms existing state-of-the-art synthetic EHR generation methods.
CRL+: A Novel Semi-Supervised Deep Active Contrastive Representation Learning-Based Text Classification Model for Insurance Data
Authors: Amir Namavar Jahromi, Ebrahim Pourjafari, Hadis Karimipour, Amit Satpathy, Lovell Hodge
Date: 2023-02-08 00:00:00
Tasks: Active Learning, Representation Learning, text-classification, Text Classification

Financial sector and especially the insurance industry collect vast volumes of text on a daily basis and through multiple channels (their agents, customer care centers, emails, social networks, and web in general). The information collected includes policies, expert and health reports, claims and complaints, results of surveys, and relevant social media posts. It is difficult to effectively extract label, classify, and interpret the essential information from such varied and unstructured material. Therefore, the Insurance Industry is among the ones that can benefit from applying technologies for the intelligent analysis of free text through Natural Language Processing (NLP). In this paper, CRL+, a novel text classification model combining Contrastive Representation Learning (CRL) and Active Learning is proposed to handle the challenge of using semi-supervised learning for text classification. In this method, supervised (CRL) is used to train a RoBERTa transformer model to encode the textual data into a contrastive representation space and then classify using a classification layer. This (CRL)-based transformer model is used as the base model in the proposed Active Learning mechanism to classify all the data in an iterative manner. The proposed model is evaluated using unstructured obituary data with objective to determine the cause of the death from the data. This model is compared with the CRL model and an Active Learning model with the RoBERTa base model. The experiment shows that the proposed method can outperform both methods for this specific task.
Will ChatGPT get you caught? Rethinking of Plagiarism Detection
Authors: Mohammad Khalil, Erkan Er
Date: 2023-02-08 00:00:00

The rise of Artificial Intelligence (AI) technology and its impact on education has been a topic of growing concern in recent years. The new generation AI systems such as chatbots have become more accessible on the Internet and stronger in terms of capabilities. The use of chatbots, particularly ChatGPT, for generating academic essays at schools and colleges has sparked fears among scholars. This study aims to explore the originality of contents produced by one of the most popular AI chatbots, ChatGPT. To this end, two popular plagiarism detection tools were used to evaluate the originality of 50 essays generated by ChatGPT on various topics. Our results manifest that ChatGPT has a great potential to generate sophisticated text outputs without being well caught by the plagiarism check software. In other words, ChatGPT can create content on many topics with high originality as if they were written by someone. These findings align with the recent concerns about students using chatbots for an easy shortcut to success with minimal or no effort. Moreover, ChatGPT was asked to verify if the essays were generated by itself, as an additional measure of plagiarism check, and it showed superior performance compared to the traditional plagiarism-detection tools. The paper discusses the need for institutions to consider appropriate measures to mitigate potential plagiarism issues and advise on the ongoing debate surrounding the impact of AI technology on education. Further implications are discussed in the paper.
Asking for Help: Failure Prediction in Behavioral Cloning through Value Approximation
Authors: Cem Gokmen, Daniel Ho, Mohi Khansari
Date: 2023-02-08 00:00:00
Tasks: Imitation Learning

Recent progress in end-to-end Imitation Learning approaches has shown promising results and generalization capabilities on mobile manipulation tasks. Such models are seeing increasing deployment in real-world settings, where scaling up requires robots to be able to operate with high autonomy, i.e. requiring as little human supervision as possible. In order to avoid the need for one-on-one human supervision, robots need to be able to detect and prevent policy failures ahead of time, and ask for help, allowing a remote operator to supervise multiple robots and help when needed. However, the black-box nature of end-to-end Imitation Learning models such as Behavioral Cloning, as well as the lack of an explicit state-value representation, make it difficult to predict failures. To this end, we introduce Behavioral Cloning Value Approximation (BCVA), an approach to learning a state value function based on and trained jointly with a Behavioral Cloning policy that can be used to predict failures. We demonstrate the effectiveness of BCVA by applying it to the challenging mobile manipulation task of latched-door opening, showing that we can identify failure scenarios with with 86% precision and 81% recall, evaluated on over 2000 real world runs, improving upon the baseline of simple failure classification by 10 percentage-points.
Continuous Learning for Android Malware Detection
Authors: Yizheng Chen, Zhoujie Ding, David Wagner
Date: 2023-02-08 00:00:00
Tasks: Active Learning, Android Malware Detection, Contrastive Learning, Malware Detection

Machine learning methods can detect Android malware with very high accuracy. However, these classifiers have an Achilles heel, concept drift: they rapidly become out of date and ineffective, due to the evolution of malware apps and benign apps. Our research finds that, after training an Android malware classifier on one year's worth of data, the F1 score quickly dropped from 0.99 to 0.76 after 6 months of deployment on new test samples. In this paper, we propose new methods to combat the concept drift problem of Android malware classifiers. Since machine learning technique needs to be continuously deployed, we use active learning: we select new samples for analysts to label, and then add the labeled samples to the training set to retrain the classifier. Our key idea is, similarity-based uncertainty is more robust against concept drift. Therefore, we combine contrastive learning with active learning. We propose a new hierarchical contrastive learning scheme, and a new sample selection technique to continuously train the Android malware classifier. Our evaluation shows that this leads to significant improvements, compared to previously published methods for active learning. Our approach reduces the false negative rate from 14% (for the best baseline) to 9%, while also reducing the false positive rate (from 0.86% to 0.48%). Also, our approach maintains more consistent performance across a seven-year time period than past methods.
Short-Term Memory Convolutions
Authors: Grzegorz Stefański, Krzysztof Arendt, Paweł Daniluk, Bartłomiej Jasik, Artur Szumaczuk
Date: 2023-02-08 00:00:00
Tasks: Acoustic Scene Classification, Scene Classification, Speech Separation, Time Series, Time Series Analysis

The real-time processing of time series signals is a critical issue for many real-life applications. The idea of real-time processing is especially important in audio domain as the human perception of sound is sensitive to any kind of disturbance in perceived signals, especially the lag between auditory and visual modalities. The rise of deep learning (DL) models complicated the landscape of signal processing. Although they often have superior quality compared to standard DSP methods, this advantage is diminished by higher latency. In this work we propose novel method for minimization of inference time latency and memory consumption, called Short-Term Memory Convolution (STMC) and its transposed counterpart. The main advantage of STMC is the low latency comparable to long short-term memory (LSTM) networks. Furthermore, the training of STMC-based models is faster and more stable as the method is based solely on convolutional neural networks (CNNs). In this study we demonstrate an application of this solution to a U-Net model for a speech separation task and GhostNet model in acoustic scene classification (ASC) task. In case of speech separation we achieved a 5-fold reduction in inference time and a 2-fold reduction in latency without affecting the output quality. The inference time for ASC task was up to 4 times faster while preserving the original accuracy.
Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles
Authors: Songyang Han, Shanglin Zhou, Lynn Pepin, Jiangwei Wang, Caiwen Ding, Fei Miao
Date: 2023-02-08 00:00:00
Tasks: Autonomous Vehicles, Multi-agent Reinforcement Learning

The recent advancements in wireless technology enable connected autonomous vehicles (CAVs) to gather data via vehicle-to-vehicle (V2V) communication, such as processed LIDAR and camera data from other vehicles. In this work, we design an integrated information sharing and safe multi-agent reinforcement learning (MARL) framework for CAVs, to take advantage of the extra information when making decisions to improve traffic efficiency and safety. We first use weight pruned convolutional neural networks (CNN) to process the raw image and point cloud LIDAR data locally at each autonomous vehicle, and share CNN-output data with neighboring CAVs. We then design a safe actor-critic algorithm that utilizes both a vehicle's local observation and the information received via V2V communication to explore an efficient behavior planning policy with safety guarantees. Using the CARLA simulator for experiments, we show that our approach improves the CAV system's efficiency in terms of average velocity and comfort under different CAV ratios and different traffic densities. We also show that our approach avoids the execution of unsafe actions and always maintains a safe distance from other vehicles. We construct an obstacle-at-corner scenario to show that the shared vision can help CAVs to observe obstacles earlier and take action to avoid traffic jams.
Learning to Play Stochastic Two-player Perfect-Information Games without Knowledge
Authors: Quentin Cohen-Solal, Tristan Cazenave
Date: 2023-02-08 00:00:00
Tasks: Vocal Bursts Valence Prediction

In this paper, we extend the Descent framework, which enables learning and planning in the context of two-player games with perfect information, to the framework of stochastic games. We propose two ways of doing this, the first way generalizes the search algorithm, i.e. Descent, to stochastic games and the second way approximates stochastic games by deterministic games. We then evaluate them on the game EinStein wurfelt nicht! against state-of-the-art algorithms: Expectiminimax and Polygames (i.e. the Alpha Zero algorithm). It is our generalization of Descent which obtains the best results. The approximation by deterministic games nevertheless obtains good results, presaging that it could give better results in particular contexts.