pwc-archive/papers-with-abstracts huggingface dataset:
A Neural-Symbolic Approach Towards Identifying Grammatically Correct Sentences
Authors: Nicos Isaak
Date: 2023-07-16 00:00:00
Tasks: Articles, CoLA, Linguistic Acceptability, Machine Translation, Question Answering, Sentence, Text Summarization, valid

Textual content around us is growing on a daily basis. Numerous articles are being written as we speak on online newspapers, blogs, or social media. Similarly, recent advances in the AI field, like language models or traditional classic AI approaches, are utilizing all the above to improve their learned representation to tackle NLP challenges with human-like accuracy. It is commonly accepted that it is crucial to have access to well-written text from valid sources to tackle challenges like text summarization, question-answering, machine translation, or even pronoun resolution. For instance, to summarize well, one needs to select the most important sentences in order to concatenate them to form the summary. However, what happens if we do not have access to well-formed English sentences or even non-valid sentences? Despite the importance of having access to well-written sentences, figuring out ways to validate them is still an open area of research. To address this problem, we present a simplified way to validate English sentences through a novel neural-symbolic approach. Lately, neural-symbolic approaches have triggered an increasing interest towards tackling various NLP challenges, as they are demonstrating their effectiveness as a central component in various AI systems. Through combining Classic with Modern AI, which involves the blending of grammatical and syntactical rules with language models, we effectively tackle the Corpus of Linguistic Acceptability (COLA), a task that shows whether or not a sequence of words is an English grammatical sentence. Among others, undertaken experiments effectively show that blending symbolic and non-symbolic systems helps the former provide insights about the latter's accuracy results.
Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review
Authors: Chengmin Zhou, Chao Wang, Haseeb Hassan, Himat Shah, Bingding Huang, Pasi Fränti
Date: 2023-07-16 00:00:00
Tasks: Bayesian Inference, Knowledge Graphs, Motion Planning, Uncertainty Quantification

Bayesian inference has many advantages in robotic motion planning over four perspectives: The uncertainty quantification of the policy, safety (risk-aware) and optimum guarantees of robot motions, data-efficiency in training of reinforcement learning, and reducing the sim2real gap when the robot is applied to real-world tasks. However, the application of Bayesian inference in robotic motion planning is lagging behind the comprehensive theory of Bayesian inference. Further, there are no comprehensive reviews to summarize the progress of Bayesian inference to give researchers a systematic understanding in robotic motion planning. This paper first provides the probabilistic theories of Bayesian inference which are the preliminary of Bayesian inference for complex cases. Second, the Bayesian estimation is given to estimate the posterior of policies or unknown functions which are used to compute the policy. Third, the classical model-based Bayesian RL and model-free Bayesian RL algorithms for robotic motion planning are summarized, while these algorithms in complex cases are also analyzed. Fourth, the analysis of Bayesian inference in inverse RL is given to infer the reward functions in a data-efficient manner. Fifth, we systematically present the hybridization of Bayesian inference and RL which is a promising direction to improve the convergence of RL for better motion planning. Sixth, given the Bayesian inference, we present the interpretable and safe robotic motion plannings which are the hot research topic recently. Finally, all algorithms reviewed in this paper are summarized analytically as the knowledge graphs, and the future of Bayesian inference for robotic motion planning is also discussed, to pave the way for data-efficient, explainable, and safe robotic motion planning strategies for practical applications.
Breaking Down the Task: A Unit-Grained Hybrid Training Framework for Vision and Language Decision Making
Authors: Ruipu Luo, Jiwen Zhang, Zhongyu Wei
Date: 2023-07-16 00:00:00
Tasks: Decision Making

Vision language decision making (VLDM) is a challenging multimodal task. The agent have to understand complex human instructions and complete compositional tasks involving environment navigation and object manipulation. However, the long action sequences involved in VLDM make the task difficult to learn. From an environment perspective, we find that task episodes can be divided into fine-grained \textit{units}, each containing a navigation phase and an interaction phase. Since the environment within a unit stays unchanged, we propose a novel hybrid-training framework that enables active exploration in the environment and reduces the exposure bias. Such framework leverages the unit-grained configurations and is model-agnostic. Specifically, we design a Unit-Transformer (UT) with an intrinsic recurrent state that maintains a unit-scale cross-modal memory. Through extensive experiments on the TEACH benchmark, we demonstrate that our proposed framework outperforms existing state-of-the-art methods in terms of all evaluation metrics. Overall, our work introduces a novel approach to tackling the VLDM task by breaking it down into smaller, manageable units and utilizing a hybrid-training framework. By doing so, we provide a more flexible and effective solution for multimodal decision making.
SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction
Authors: Mahbub Ul Alam, Jaakko Hollmén, Jón Rúnar Baldvinsson, Rahim Rahmani
Date: 2023-07-16 00:00:00
Tasks: Transfer Learning

The interpretability of deep neural networks has become a subject of great interest within the medical and healthcare domain. This attention stems from concerns regarding transparency, legal and ethical considerations, and the medical significance of predictions generated by these deep neural networks in clinical decision support systems. To address this matter, our study delves into the application of four well-established interpretability methods: Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive exPlanations (SHAP), Gradient-weighted Class Activation Mapping (Grad-CAM), and Layer-wise Relevance Propagation (LRP). Leveraging the approach of transfer learning with a multi-label-multi-class chest radiography dataset, we aim to interpret predictions pertaining to specific pathology classes. Our analysis encompasses both single-label and multi-label predictions, providing a comprehensive and unbiased assessment through quantitative and qualitative investigations, which are compared against human expert annotation. Notably, Grad-CAM demonstrates the most favorable performance in quantitative evaluation, while the LIME heatmap score segmentation visualization exhibits the highest level of medical significance. Our research underscores both the outcomes and the challenges faced in the holistic approach adopted for assessing these interpretability methods and suggests that a multimodal-based approach, incorporating diverse sources of information beyond chest radiography images, could offer additional insights for enhancing interpretability in the medical domain.
MargCTGAN: A "Marginally'' Better CTGAN for the Low Sample Regime
Authors: Tejumade Afonja, Dingfan Chen, Mario Fritz
Date: 2023-07-16 00:00:00
Tasks: Tabular Data Generation

The potential of realistic and useful synthetic data is significant. However, current evaluation methods for synthetic tabular data generation predominantly focus on downstream task usefulness, often neglecting the importance of statistical properties. This oversight becomes particularly prominent in low sample scenarios, accompanied by a swift deterioration of these statistical measures. In this paper, we address this issue by conducting an evaluation of three state-of-the-art synthetic tabular data generators based on their marginal distribution, column-pair correlation, joint distribution and downstream task utility performance across high to low sample regimes. The popular CTGAN model shows strong utility, but underperforms in low sample settings in terms of utility. To overcome this limitation, we propose MargCTGAN that adds feature matching of de-correlated marginals, which results in a consistent improvement in downstream utility as well as statistical properties of the synthetic data.
Automated Polynomial Filter Learning for Graph Neural Networks
Authors: Wendi Yu, Zhichao Hou, Xiaorui Liu
Date: 2023-07-16 00:00:00

Polynomial graph filters have been widely used as guiding principles in the design of Graph Neural Networks (GNNs). Recently, the adaptive learning of the polynomial graph filters has demonstrated promising performance for modeling graph signals on both homophilic and heterophilic graphs, owning to their flexibility and expressiveness. In this work, we conduct a novel preliminary study to explore the potential and limitations of polynomial graph filter learning approaches, revealing a severe overfitting issue. To improve the effectiveness of polynomial graph filters, we propose Auto-Polynomial, a novel and general automated polynomial graph filter learning framework that efficiently learns better filters capable of adapting to various complex graph signals. Comprehensive experiments and ablation studies demonstrate significant and consistent performance improvements on both homophilic and heterophilic graphs across multiple learning settings considering various labeling ratios, which unleashes the potential of polynomial filter learning.
MinT: Boosting Generalization in Mathematical Reasoning via Multi-View Fine-Tuning
Authors: Zhenwen Liang, Dian Yu, Xiaoman Pan, Wenlin Yao, Qingkai Zeng, Xiangliang Zhang, Dong Yu
Date: 2023-07-16 00:00:00
Tasks: Knowledge Distillation, Mathematical Reasoning

Reasoning in mathematical domains remains a significant challenge for relatively small language models (LMs). Many current methods focus on specializing LMs in mathematical reasoning and rely heavily on knowledge distillation from powerful but inefficient large LMs (LLMs). In this work, we explore a new direction that avoids over-reliance on LLM teachers, introducing a multi-view fine-tuning method that efficiently exploits existing mathematical problem datasets with diverse annotation styles. Our approach uniquely considers the various annotation formats as different "views" and leverages them in training the model. By postpending distinct instructions to input questions, models can learn to generate solutions in diverse formats in a flexible manner. Experimental results show that our strategy enables a LLaMA-7B model to outperform prior approaches that utilize knowledge distillation, as well as carefully established baselines. Additionally, the proposed method grants the models promising generalization ability across various views and datasets, and the capability to learn from inaccurate or incomplete noisy data. We hope our multi-view training paradigm could inspire future studies in other machine reasoning domains.
Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling
Authors: Zhuoxiao Chen, Yadan Luo, Zheng Wang, Mahsa Baktashmotlagh, Zi Huang
Date: 2023-07-16 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Object Detection, Domain Adaptation, object-detection, Object Detection, Unsupervised Domain Adaptation

Unsupervised domain adaptation (DA) with the aid of pseudo labeling techniques has emerged as a crucial approach for domain-adaptive 3D object detection. While effective, existing DA methods suffer from a substantial drop in performance when applied to a multi-class training setting, due to the co-existence of low-quality pseudo labels and class imbalance issues. In this paper, we address this challenge by proposing a novel ReDB framework tailored for learning to detect all classes at once. Our approach produces Reliable, Diverse, and class-Balanced pseudo 3D boxes to iteratively guide the self-training on a distributionally different target domain. To alleviate disruptions caused by the environmental discrepancy (e.g., beam numbers), the proposed cross-domain examination (CDE) assesses the correctness of pseudo labels by copy-pasting target instances into a source environment and measuring the prediction consistency. To reduce computational overhead and mitigate the object shift (e.g., scales and point densities), we design an overlapped boxes counting (OBC) metric that allows to uniformly downsample pseudo-labeled objects across different geometric characteristics. To confront the issue of inter-class imbalance, we progressively augment the target point clouds with a class-balanced set of pseudo-labeled target instances and source objects, which boosts recognition accuracies on both frequently appearing and rare classes. Experimental results on three benchmark datasets using both voxel-based (i.e., SECOND) and point-based 3D detectors (i.e., PointRCNN) demonstrate that our proposed ReDB approach outperforms existing 3D domain adaptation methods by a large margin, improving 23.15% mAP on the nuScenes $\rightarrow$ KITTI task. The code is available at https://github.com/zhuoxiao-chen/ReDB-DA-3Ddet.
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection
Authors: Yadan Luo, Zhuoxiao Chen, Zhen Fang, Zheng Zhang, Zi Huang, Mahsa Baktashmotlagh
Date: 2023-07-16 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Object Detection, Active Learning, Autonomous Driving, Computational Efficiency, Informativeness, object-detection, Object Detection

Achieving a reliable LiDAR-based object detector in autonomous driving is paramount, but its success hinges on obtaining large amounts of precise 3D annotations. Active learning (AL) seeks to mitigate the annotation burden through algorithms that use fewer labels and can attain performance comparable to fully supervised learning. Although AL has shown promise, current approaches prioritize the selection of unlabeled point clouds with high uncertainty and/or diversity, leading to the selection of more instances for labeling and reduced computational efficiency. In this paper, we resort to a novel kernel coding rate maximization (KECOR) strategy which aims to identify the most informative point clouds to acquire labels through the lens of information theory. Greedy search is applied to seek desired point clouds that can maximize the minimal number of bits required to encode the latent features. To determine the uniqueness and informativeness of the selected samples from the model perspective, we construct a proxy network of the 3D detector head and compute the outer product of Jacobians from all proxy layers to form the empirical neural tangent kernel (NTK) matrix. To accommodate both one-stage (i.e., SECOND) and two-stage detectors (i.e., PVRCNN), we further incorporate the classification entropy maximization and well trade-off between detection performance and the total number of bounding boxes selected for annotation. Extensive experiments conducted on two 3D benchmarks and a 2D detection dataset evidence the superiority and versatility of the proposed approach. Our results show that approximately 44% box-level annotation costs and 26% computational time are reduced compared to the state-of-the-art AL method, without compromising detection performance.
GeoGPT: Understanding and Processing Geospatial Tasks through An Autonomous GPT
Authors: Yifan Zhang, Cheng Wei, Shangyou Wu, Zhengting He, Wenhao Yu
Date: 2023-07-16 00:00:00

Decision-makers in GIS need to combine a series of spatial algorithms and operations to solve geospatial tasks. For example, in the task of facility siting, the Buffer tool is usually first used to locate areas close or away from some specific entities; then, the Intersect or Erase tool is used to select candidate areas satisfied multiple requirements. Though professionals can easily understand and solve these geospatial tasks by sequentially utilizing relevant tools, it is difficult for non-professionals to handle these problems. Recently, Generative Pre-trained Transformer (e.g., ChatGPT) presents strong performance in semantic understanding and reasoning. Especially, AutoGPT can further extend the capabilities of large language models (LLMs) by automatically reasoning and calling externally defined tools. Inspired by these studies, we attempt to lower the threshold of non-professional users to solve geospatial tasks by integrating the semantic understanding ability inherent in LLMs with mature tools within the GIS community. Specifically, we develop a new framework called GeoGPT that can conduct geospatial data collection, processing, and analysis in an autonomous manner with the instruction of only natural language. In other words, GeoGPT is used to understand the demands of non-professional users merely based on input natural language descriptions, and then think, plan, and execute defined GIS tools to output final effective results. Several cases including geospatial data crawling, spatial query, facility siting, and mapping validate the effectiveness of our framework. Though limited cases are presented in this paper, GeoGPT can be further extended to various tasks by equipping with more GIS tools, and we think the paradigm of "foundational plus professional" implied in GeoGPT provides an effective way to develop next-generation GIS in this era of large foundation models.
Neural Architecture Retrieval
Authors: Xiaohuan Pei, Yanxi Li, Minjing Dong, Chang Xu
Date: 2023-07-16 00:00:00
Tasks: Contrastive Learning, Graph Representation Learning, Representation Learning, Retrieval

With the increasing number of new neural architecture designs and substantial existing neural architectures, it becomes difficult for the researchers to situate their contributions compared with existing neural architectures or establish the connections between their designs and other relevant ones. To discover similar neural architectures in an efficient and automatic manner, we define a new problem Neural Architecture Retrieval which retrieves a set of existing neural architectures which have similar designs to the query neural architecture. Existing graph pre-training strategies cannot address the computational graph in neural architectures due to the graph size and motifs. To fulfill this potential, we propose to divide the graph into motifs which are used to rebuild the macro graph to tackle these issues, and introduce multi-level contrastive learning to achieve accurate graph representation learning. Extensive evaluations on both human-designed and synthesized neural architectures demonstrate the superiority of our algorithm. Such a dataset which contains 12k real-world network architectures, as well as their embedding, is built for neural architecture retrieval.
Is Imitation All You Need? Generalized Decision-Making with Dual-Phase Training
Authors: Yao Wei, Yanchao Sun, Ruijie Zheng, Sai Vemprala, Rogerio Bonatti, Shuhang Chen, Ratnesh Madaan, Zhongjie Ba, Ashish Kapoor, Shuang Ma
Date: 2023-07-16 00:00:00
Proceeding: ICCV 2023 1
Tasks: All, Decision Making

We introduce DualMind, a generalist agent designed to tackle various decision-making tasks that addresses challenges posed by current methods, such as overfitting behaviors and dependence on task-specific fine-tuning. DualMind uses a novel "Dual-phase" training strategy that emulates how humans learn to act in the world. The model first learns fundamental common knowledge through a self-supervised objective tailored for control tasks and then learns how to make decisions based on different contexts through imitating behaviors conditioned on given prompts. DualMind can handle tasks across domains, scenes, and embodiments using just a single set of model weights and can execute zero-shot prompting without requiring task-specific fine-tuning. We evaluate DualMind on MetaWorld and Habitat through extensive experiments and demonstrate its superior generalizability compared to previous techniques, outperforming other generalist agents by over 50$\%$ and 70$\%$ on Habitat and MetaWorld, respectively. On the 45 tasks in MetaWorld, DualMind achieves over 30 tasks at a 90$\%$ success rate.
A Dialogue System for Assessing Activities of Daily Living: Improving Consistency with Grounded Knowledge
Authors: Zhecheng Sheng, Raymond Finzel, Michael Lucke, Sheena Dufresne, Maria Gini, Serguei Pakhomov
Date: 2023-07-15 00:00:00
Tasks: Natural Language Understanding, Text Generation

In healthcare, the ability to care for oneself is reflected in the "Activities of Daily Living (ADL)," which serve as a measure of functional ability (functioning). A lack of functioning may lead to poor living conditions requiring personal care and assistance. To accurately identify those in need of support, assistance programs continuously evaluate participants' functioning across various domains. However, the assessment process may encounter consistency issues when multiple assessors with varying levels of expertise are involved. Novice assessors, in particular, may lack the necessary preparation for real-world interactions with participants. To address this issue, we developed a dialogue system that simulates interactions between assessors and individuals of varying functioning in a natural and reproducible way. The dialogue system consists of two major modules, one for natural language understanding (NLU) and one for natural language generation (NLG), respectively. In order to generate responses consistent with the underlying knowledge base, the dialogue system requires both an understanding of the user's query and of biographical details of an individual being simulated. To fulfill this requirement, we experimented with query classification and generated responses based on those biographical details using some recently released InstructGPT-like models.
Anomaly Detection in Automated Fibre Placement: Learning with Data Limitations
Authors: Assef Ghamisi, Todd Charter, Li Ji, Maxime Rivard, Gil Lund, Homayoun Najjaran
Date: 2023-07-15 00:00:00
Tasks: Anomaly Detection, Binary Classification, Defect Detection

Conventional defect detection systems in Automated Fibre Placement (AFP) typically rely on end-to-end supervised learning, necessitating a substantial number of labelled defective samples for effective training. However, the scarcity of such labelled data poses a challenge. To overcome this limitation, we present a comprehensive framework for defect detection and localization in Automated Fibre Placement. Our approach combines unsupervised deep learning and classical computer vision algorithms, eliminating the need for labelled data or manufacturing defect samples. It efficiently detects various surface issues while requiring fewer images of composite parts for training. Our framework employs an innovative sample extraction method leveraging AFP's inherent symmetry to expand the dataset. By inputting a depth map of the fibre layup surface, we extract local samples aligned with each composite strip (tow). These samples are processed through an autoencoder, trained on normal samples for precise reconstructions, highlighting anomalies through reconstruction errors. Aggregated values form an anomaly map for insightful visualization. The framework employs blob detection on this map to locate manufacturing defects. The experimental findings reveal that despite training the autoencoder with a limited number of images, our proposed method exhibits satisfactory detection accuracy and accurately identifies defect locations. Our framework demonstrates comparable performance to existing methods, while also offering the advantage of detecting all types of anomalies without relying on an extensive labelled dataset of defects.