pwc-archive/papers-with-abstracts huggingface dataset:
GNN-based Passenger Request Prediction
Authors: Aqsa Ashraf Makhdomi, Iqra Altaf Gillani
Date: 2023-01-06 00:00:00
Tasks: Graph Neural Network, Management, Prediction

Passenger request prediction is essential for operations planning, control, and management in ride-sharing platforms. While the demand prediction problem has been studied extensively, the Origin-Destination (OD) flow prediction of passengers has received less attention from the research community. This paper develops a Graph Neural Network framework along with the Attention Mechanism to predict the OD flow of passengers. The proposed framework exploits various linear and non-linear dependencies that arise among requests originating from different locations and captures the repetition pattern and the contextual data of that place. Moreover, the optimal size of the grid cell that covers the road network and preserves the complexity and accuracy of the model is determined. Extensive simulations are conducted to examine the characteristics of our proposed approach and its various components. The results show the superior performance of our proposed model compared to the existing baselines.
Adaptive Pattern Extraction Multi-Task Learning for Multi-Step Conversion Estimations
Authors: Xuewen Tao, Mingming Ha, Xiaobo Guo, Qiongxu Ma, Hongwei Cheng, Wenfang Lin
Date: 2023-01-06 00:00:00
Tasks: Multi-Task Learning, Representation Learning

Multi-task learning (MTL) has been successfully used in many real-world applications, which aims to simultaneously solve multiple tasks with a single model. The general idea of multi-task learning is designing kinds of global parameter sharing mechanism and task-specific feature extractor to improve the performance of all tasks. However, challenge still remains in balancing the trade-off of various tasks since model performance is sensitive to the relationships between them. Less correlated or even conflict tasks will deteriorate the performance by introducing unhelpful or negative information. Therefore, it is important to efficiently exploit and learn fine-grained feature representation corresponding to each task. In this paper, we propose an Adaptive Pattern Extraction Multi-task (APEM) framework, which is adaptive and flexible for large-scale industrial application. APEM is able to fully utilize the feature information by learning the interactions between the input feature fields and extracted corresponding tasks-specific information. We first introduce a DeepAuto Group Transformer module to automatically and efficiently enhance the feature expressivity with a modified set attention mechanism and a Squeeze-and-Excitation operation. Second, explicit Pattern Selector is introduced to further enable selectively feature representation learning by adaptive task-indicator vectors. Empirical evaluations show that APEM outperforms the state-of-the-art MTL methods on public and real-world financial services datasets. More importantly, we explore the online performance of APEM in a real industrial-level recommendation scenario.
TWR-MCAE: A Data Augmentation Method for Through-the-Wall Radar Human Motion Recognition
Authors: Weicheng Gao, Xiaopeng Yang, Xiaodong Qu, Tian Lan
Date: 2023-01-06 00:00:00
Tasks: compressed sensing, Data Augmentation

To solve the problems of reduced accuracy and prolonging convergence time of through-the-wall radar (TWR) human motion due to wall attenuation, multipath effect, and system interference, we propose a multilink auto-encoding neural network (TWR-MCAE) data augmentation method. Specifically, the TWR-MCAE algorithm is jointly constructed by a singular value decomposition (SVD)-based data preprocessing module, an improved coordinate attention module, a compressed sensing learnable iterative shrinkage threshold reconstruction algorithm (LISTA) module, and an adaptive weight module. The data preprocessing module achieves wall clutter, human motion features, and noise subspaces separation. The improved coordinate attention module achieves clutter and noise suppression. The LISTA module achieves human motion feature enhancement. The adaptive weight module learns the weights and fuses the three subspaces. The TWR-MCAE can suppress the low-rank characteristics of wall clutter and enhance the sparsity characteristics in human motion at the same time. It can be linked before the classification step to improve the feature extraction capability without adding other prior knowledge or recollecting more data. Experiments show that the proposed algorithm gets a better peak signal-to-noise ratio (PSNR), which increases the recognition accuracy and speeds up the training process of the back-end classifiers.
Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual Learning
Authors: Vincenzo Lomonaco, Lorenzo Pellegrini, Gabriele Graffieti, Davide Maltoni
Date: 2023-01-06 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Continual Learning, Incremental Learning, Representation Learning

In recent years we have witnessed a renewed interest in machine learning methodologies, especially for deep representation learning, that could overcome basic i.i.d. assumptions and tackle non-stationary environments subject to various distributional shifts or sample selection biases. Within this context, several computational approaches based on architectural priors, regularizers and replay policies have been proposed with different degrees of success depending on the specific scenario in which they were developed and assessed. However, designing comprehensive hybrid solutions that can flexibly and generally be applied with tunable efficiency-effectiveness trade-offs still seems a distant goal. In this paper, we propose "Architect, Regularize and Replay" (ARR), an hybrid generalization of the renowned AR1 algorithm and its variants, that can achieve state-of-the-art results in classic scenarios (e.g. class-incremental learning) but also generalize to arbitrary data streams generated from real-world datasets such as CIFAR-100, CORe50 and ImageNet-1000.
Measuring a Priori Voting Power -- Taking Delegations Seriously
Authors: Rachael Colley, Théo Delemazure, Hugo Gilbert
Date: 2023-01-06 00:00:00

We introduce new power indices to measure the a priori voting power of voters in liquid democracy elections where an underlying network restricts delegations. We argue that our power indices are natural extensions of the standard Penrose-Banzhaf index in simple voting games. We show that computing the criticality of a voter is #P-hard even when voting weights are polynomially-bounded in the size of the instance. However, for specific settings, such as when the underlying network is a bipartite or complete graph, recursive formulas can compute these indices for weighted voting games in pseudo-polynomial time. We highlight their theoretical properties and provide numerical results to illustrate how restricting the possible delegations can alter voters' voting power.
IMKGA-SM: Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling
Authors: Yilin Wen, Biao Luo, Yuqian Zhao
Date: 2023-01-06 00:00:00
Tasks: Link Prediction, Optical Character Recognition, Optical Character Recognition (OCR), Prediction

Multimodal knowledge graph link prediction aims to improve the accuracy and efficiency of link prediction tasks for multimodal data. However, for complex multimodal information and sparse training data, it is usually difficult to achieve interpretability and high accuracy simultaneously for most methods. To address this difficulty, a new model is developed in this paper, namely Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling (IMKGA-SM). First, a multi-modal fine-grained fusion method is proposed, and Vgg16 and Optical Character Recognition (OCR) techniques are adopted to effectively extract text information from images and images. Then, the knowledge graph link prediction task is modelled as an offline reinforcement learning Markov decision model, which is then abstracted into a unified sequence framework. An interactive perception-based reward expectation mechanism and a special causal masking mechanism are designed, which "converts" the query into an inference path. Then, an autoregressive dynamic gradient adjustment mechanism is proposed to alleviate the insufficient problem of multimodal optimization. Finally, two datasets are adopted for experiments, and the popular SOTA baselines are used for comparison. The results show that the developed IMKGA-SM achieves much better performance than SOTA baselines on multimodal link prediction datasets of different sizes.
An Image captioning algorithm based on the Hybrid Deep Learning Technique (CNN+GRU)
Authors: Rana Adnan Ahmad, Muhammad Azhar, Hina Sattar
Date: 2023-01-06 00:00:00
Tasks: Decoder, Image Captioning

Image captioning by the encoder-decoder framework has shown tremendous advancement in the last decade where CNN is mainly used as encoder and LSTM is used as a decoder. Despite such an impressive achievement in terms of accuracy in simple images, it lacks in terms of time complexity and space complexity efficiency. In addition to this, in case of complex images with a lot of information and objects, the performance of this CNN-LSTM pair downgraded exponentially due to the lack of semantic understanding of the scenes presented in the images. Thus, to take these issues into consideration, we present CNN-GRU encoder decode framework for caption-to-image reconstructor to handle the semantic context into consideration as well as the time complexity. By taking the hidden states of the decoder into consideration, the input image and its similar semantic representations is reconstructed and reconstruction scores from a semantic reconstructor are used in conjunction with likelihood during model training to assess the quality of the generated caption. As a result, the decoder receives improved semantic information, enhancing the caption production process. During model testing, combining the reconstruction score and the log-likelihood is also feasible to choose the most appropriate caption. The suggested model outperforms the state-of-the-art LSTM-A5 model for picture captioning in terms of time complexity and accuracy.
You Truly Understand What I Need: Intellectual and Friendly Dialogue Agents grounding Knowledge and Persona
Authors: Jungwoo Lim, Myunghoon Kang, Yuna Hur, SeungWon Jung, Jinsung Kim, Yoonna Jang, Dongyub Lee, Hyesung Ji, Donghoon Shin, Seungryong Kim, Heuiseok Lim
Date: 2023-01-06 00:00:00
Tasks: Hallucination, Language Modeling, Language Modelling, Retrieval, Retrieval-augmented Generation

To build a conversational agent that interacts fluently with humans, previous studies blend knowledge or personal profile into the pre-trained language model. However, the model that considers knowledge and persona at the same time is still limited, leading to hallucination and a passive way of using personas. We propose an effective dialogue agent that grounds external knowledge and persona simultaneously. The agent selects the proper knowledge and persona to use for generating the answers with our candidate scoring implemented with a poly-encoder. Then, our model generates the utterance with lesser hallucination and more engagingness utilizing retrieval augmented generation with knowledge-persona enhanced query. We conduct experiments on the persona-knowledge chat and achieve state-of-the-art performance in grounding and generation tasks on the automatic metrics. Moreover, we validate the answers from the models regarding hallucination and engagingness through human evaluation and qualitative results. We show our retriever's effectiveness in extracting relevant documents compared to the other previous retrievers, along with the comparison of multiple candidate scoring methods. Code is available at https://github.com/dlawjddn803/INFO
ReVoLT: Relational Reasoning and Voronoi Local Graph Planning for Target-driven Navigation
Authors: Junjia Liu, Jianfei Guo, Zehui Meng, Jingtao Xue
Date: 2023-01-06 00:00:00
Tasks: object-detection, Object Detection, Relational Reasoning, Relation Extraction

Embodied AI is an inevitable trend that emphasizes the interaction between intelligent entities and the real world, with broad applications in Robotics, especially target-driven navigation. This task requires the robot to find an object of a certain category efficiently in an unknown domestic environment. Recent works focus on exploiting layout relationships by graph neural networks (GNNs). However, most of them obtain robot actions directly from observations in an end-to-end manner via an incomplete relation graph, which is not interpretable and reliable. We decouple this task and propose ReVoLT, a hierarchical framework: (a) an object detection visual front-end, (b) a high-level reasoner (infers semantic sub-goals), (c) an intermediate-level planner (computes geometrical positions), and (d) a low-level controller (executes actions). ReVoLT operates with a multi-layer semantic-spatial topological graph. The reasoner uses multiform structured relations as priors, which are obtained from combinatorial relation extraction networks composed of unsupervised GraphSAGE, GCN, and GraphRNN-based Region Rollout. The reasoner performs with Upper Confidence Bound for Tree (UCT) to infer semantic sub-goals, accounting for trade-offs between exploitation (depth-first searching) and exploration (regretting). The lightweight intermediate-level planner generates instantaneous spatial sub-goal locations via an online constructed Voronoi local graph. The simulation experiments demonstrate that our framework achieves better performance in the target-driven navigation tasks and generalizes well, which has an 80% improvement compared to the existing state-of-the-art method. The code and result video will be released at https://ventusff.github.io/ReVoLT-website/.
DANLIP: Deep Autoregressive Networks for Locally Interpretable Probabilistic Forecasting
Authors: Ozan Ozyegen, Juyoung Wang, Mucahit Cevik
Date: 2023-01-05 00:00:00
Tasks: Probabilistic Time Series Forecasting, Time Series, Time Series Analysis, Time Series Forecasting

Despite the high performance of neural network-based time series forecasting methods, the inherent challenge in explaining their predictions has limited their applicability in certain application areas. Due to the difficulty in identifying causal relationships between the input and output of such black-box methods, they rarely have been adopted in domains such as legal and medical fields in which the reliability and interpretability of the results can be essential. In this paper, we propose \model, a novel deep learning-based probabilistic time series forecasting architecture that is intrinsically interpretable. We conduct experiments with multiple datasets and performance metrics and empirically show that our model is not only interpretable but also provides comparable performance to state-of-the-art probabilistic time series forecasting methods. Furthermore, we demonstrate that interpreting the parameters of the stochastic processes of interest can provide useful insights into several application areas.
Evidence of behavior consistent with self-interest and altruism in an artificially intelligent agent
Authors: Tim Johnson, Nick Obradovich
Date: 2023-01-05 00:00:00
Tasks: AI Agent

Members of various species engage in altruism--i.e. accepting personal costs to benefit others. Here we present an incentivized experiment to test for altruistic behavior among AI agents consisting of large language models developed by the private company OpenAI. Using real incentives for AI agents that take the form of tokens used to purchase their services, we first examine whether AI agents maximize their payoffs in a non-social decision task in which they select their payoff from a given range. We then place AI agents in a series of dictator games in which they can share resources with a recipient--either another AI agent, the human experimenter, or an anonymous charity, depending on the experimental condition. Here we find that only the most-sophisticated AI agent in the study maximizes its payoffs more often than not in the non-social decision task (it does so in 92% of all trials), and this AI agent also exhibits the most-generous altruistic behavior in the dictator game, resembling humans' rates of sharing with other humans in the game. The agent's altruistic behaviors, moreover, vary by recipient: the AI agent shared substantially less of the endowment with the human experimenter or an anonymous charity than with other AI agents. Our findings provide evidence of behavior consistent with self-interest and altruism in an AI agent. Moreover, our study also offers a novel method for tracking the development of such behaviors in future AI agents.
Extreme Q-Learning: MaxEnt RL without Entropy
Authors: Divyansh Garg, Joey Hejna, Matthieu Geist, Stefano Ermon
Date: 2023-01-05 00:00:00
Tasks: D4RL, Deep Reinforcement Learning, Offline RL, Q-Learning, Reinforcement Learning (RL)

Modern Deep Reinforcement Learning (RL) algorithms require estimates of the maximal Q-value, which are difficult to compute in continuous domains with an infinite number of possible actions. In this work, we introduce a new update rule for online and offline RL which directly models the maximal value using Extreme Value Theory (EVT), drawing inspiration from economics. By doing so, we avoid computing Q-values using out-of-distribution actions which is often a substantial source of error. Our key insight is to introduce an objective that directly estimates the optimal soft-value functions (LogSumExp) in the maximum entropy RL setting without needing to sample from a policy. Using EVT, we derive our \emph{Extreme Q-Learning} framework and consequently online and, for the first time, offline MaxEnt Q-learning algorithms, that do not explicitly require access to a policy or its entropy. Our method obtains consistently strong performance in the D4RL benchmark, outperforming prior works by \emph{10+ points} on the challenging Franka Kitchen tasks while offering moderate improvements over SAC and TD3 on online DM Control tasks. Visualizations and code can be found on our website at https://div99.github.io/XQL/.
Reasoning about Causality in Games
Authors: Lewis Hammond, James Fox, Tom Everitt, Ryan Carey, Alessandro Abate, Michael Wooldridge
Date: 2023-01-05 00:00:00

Causal reasoning and game-theoretic reasoning are fundamental topics in artificial intelligence, among many other disciplines: this paper is concerned with their intersection. Despite their importance, a formal framework that supports both these forms of reasoning has, until now, been lacking. We offer a solution in the form of (structural) causal games, which can be seen as extending Pearl's causal hierarchy to the game-theoretic domain, or as extending Koller and Milch's multi-agent influence diagrams to the causal domain. We then consider three key questions: i) How can the (causal) dependencies in games - either between variables, or between strategies - be modelled in a uniform, principled manner? ii) How may causal queries be computed in causal games, and what assumptions does this require? iii) How do causal games compare to existing formalisms? To address question i), we introduce mechanised games, which encode dependencies between agents' decision rules and the distributions governing the game. In response to question ii), we present definitions of predictions, interventions, and counterfactuals, and discuss the assumptions required for each. Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support. Finally, we highlight possible applications of causal games, aided by an extensive open-source Python library.
Sequentially Controlled Text Generation
Authors: Alexander Spangher, Xinyu Hua, Yao Ming, Nanyun Peng
Date: 2023-01-05 00:00:00
Tasks: Text Generation

While GPT-2 generates sentences that are remarkably human-like, longer documents can ramble and do not follow human-like writing structure. We study the problem of imposing structure on long-range text. We propose a novel controlled text generation task, sequentially controlled text generation, and identify a dataset, NewsDiscourse as a starting point for this task. We develop a sequential controlled text generation pipeline with generation and editing. We test different degrees of structural awareness and show that, in general, more structural awareness results in higher control-accuracy, grammaticality, coherency and topicality, approaching human-level writing performance.
gRoMA: a Tool for Measuring the Global Robustness of Deep Neural Networks
Authors: Natan Levy, Raz Yerushalmi, Guy Katz
Date: 2023-01-05 00:00:00

Deep neural networks (DNNs) are at the forefront of cutting-edge technology, and have been achieving remarkable performance in a variety of complex tasks. Nevertheless, their integration into safety-critical systems, such as in the aerospace or automotive domains, poses a significant challenge due to the threat of adversarial inputs: perturbations in inputs that might cause the DNN to make grievous mistakes. Multiple studies have demonstrated that even modern DNNs are susceptible to adversarial inputs, and this risk must thus be measured and mitigated to allow the deployment of DNNs in critical settings. Here, we present gRoMA (global Robustness Measurement and Assessment), an innovative and scalable tool that implements a probabilistic approach to measure the global categorial robustness of a DNN. Specifically, gRoMA measures the probability of encountering adversarial inputs for a specific output category. Our tool operates on pre-trained, black-box classification DNNs, and generates input samples belonging to an output category of interest. It measures the DNN's susceptibility to adversarial inputs around these inputs, and aggregates the results to infer the overall global categorial robustness of the DNN up to some small bounded statistical error. We evaluate our tool on the popular Densenet DNN model over the CIFAR10 dataset. Our results reveal significant gaps in the robustness of the different output categories. This experiment demonstrates the usefulness and scalability of our approach and its potential for allowing DNNs to be deployed within critical systems of interest.
LostNet: A smart way for lost and find
Authors: Meihua Zhou, Ivan Fung, Li Yang, Nan Wan, Keke Di, Tingting Wang
Date: 2023-01-05 00:00:00

Due to the enormous population growth of cities in recent years, objects are frequently lost and unclaimed on public transportation, in restaurants, or any other public areas. While services like Find My iPhone can easily identify lost electronic devices, more valuable objects cannot be tracked in an intelligent manner, making it impossible for administrators to reclaim a large number of lost and found items in a timely manner. We present a method that significantly reduces the complexity of searching by comparing previous images of lost and recovered things provided by the owner with photos taken when registered lost and found items are received. In this research, we will primarily design a photo matching network by combining the fine-tuning method of MobileNetv2 with CBAM Attention and using the Internet framework to develop an online lost and found image identification system. Our implementation gets a testing accuracy of 96.8% using only 665.12M GLFOPs and 3.5M training parameters. It can recognize practice images and can be run on a regular laptop.