pwc-archive/papers-with-abstracts huggingface dataset:
Clustering and Mining Accented Speech for Inclusive and Fair Speech Recognition
Date: 2024-08-05 00:00:00

Modern automatic speech recognition (ASR) systems are typically trained on more than tens of thousands hours of speech data, which is one of the main factors for their great success. However, the distribution of such data is typically biased towards common accents or typical speech patterns. As a result, those systems often poorly perform on atypical accented speech. In this paper, we present accent clustering and mining schemes for fair speech recognition systems which can perform equally well on under-represented accented speech. For accent recognition, we applied three schemes to overcome limited size of supervised accent data: supervised or unsupervised pre-training, distributionally robust optimization (DRO) and unsupervised clustering. Three schemes can significantly improve the accent recognition model especially for unbalanced and small accented speech. Fine-tuning ASR on the mined Indian accent speech using the proposed supervised or unsupervised clustering schemes showed 10.0% and 5.3% relative improvements compared to fine-tuning on the randomly sampled speech, respectively.
Be More Real: Travel Diary Generation Using LLM Agents and Individual Profiles
Date: 2024-07-10 00:00:00

Human mobility is inextricably linked to social issues such as traffic congestion, energy consumption, and public health; however, privacy concerns restrict access to mobility data. Recently, research have utilized Large Language Models (LLMs) for human mobility generation, in which the challenge is how LLMs can understand individuals' mobility behavioral differences to generate realistic trajectories conforming to real world contexts. This study handles this problem by presenting an LLM agent-based framework (MobAgent) composing two phases: understanding-based mobility pattern extraction and reasoning-based trajectory generation, which enables generate more real travel diaries at urban scale, considering different individual profiles. MobAgent extracts reasons behind specific mobility trendiness and attribute influences to provide reliable patterns; infers the relationships between contextual factors and underlying motivations of mobility; and based on the patterns and the recursive reasoning process, MobAgent finally generates more authentic and personalized mobilities that reflect both individual differences and real-world constraints. We validate our framework with 0.2 million travel survey data, demonstrating its effectiveness in producing personalized and accurate travel diaries. This study highlights the capacity of LLMs to provide detailed and sophisticated understanding of human mobility through the real-world mobility data.
Graph Stochastic Neural Process for Inductive Few-shot Knowledge Graph Completion
Authors: Zicheng Zhao, Linhao Luo, Shirui Pan, Chengqi Zhang, Chen Gong
Date: 2024-08-03 00:00:00
Tasks: Knowledge Graph Completion, Knowledge Graphs

Knowledge graphs (KGs) store enormous facts as relationships between entities. Due to the long-tailed distribution of relations and the incompleteness of KGs, there is growing interest in few-shot knowledge graph completion (FKGC). Existing FKGC methods often assume the existence of all entities in KGs, which may not be practical since new relations and entities can emerge over time. Therefore, we focus on a more challenging task called inductive few-shot knowledge graph completion (I-FKGC), where both relations and entities during the test phase are unknown before. Inspired by the idea of inductive reasoning, we cast I-FKGC as an inductive reasoning problem. Specifically, we propose a novel Graph Stochastic Neural Process approach (GS-NP), which consists of two major modules. In the first module, to obtain a generalized hypothesis (e.g., shared subgraph), we present a neural process-based hypothesis extractor that models the joint distribution of hypothesis, from which we can sample a hypothesis for predictions. In the second module, based on the hypothesis, we propose a graph stochastic attention-based predictor to test if the triple in the query set aligns with the extracted hypothesis. Meanwhile, the predictor can generate an explanatory subgraph identified by the hypothesis. Finally, the training of these two modules is seamlessly combined into a unified objective function, of which the effectiveness is verified by theoretical analyses as well as empirical studies. Extensive experiments on three public datasets demonstrate that our method outperforms existing methods and derives new state-of-the-art performance.
Sharpness-Aware Cross-Domain Recommendation to Cold-Start Users
Authors: Guohang Zeng, Qian Zhang, Guangquan Zhang, Jie Lu
Date: 2024-08-04 00:00:00
Tasks: Recommendation Systems, Transfer Learning

Cross-Domain Recommendation (CDR) is a promising paradigm inspired by transfer learning to solve the cold-start problem in recommender systems. Existing state-of-the-art CDR methods train an explicit mapping function to transfer the cold-start users from a data-rich source domain to a target domain. However, a limitation of these methods is that the mapping function is trained on overlapping users across domains, while only a small number of overlapping users are available for training. By visualizing the loss landscape of the existing CDR model, we find that training on a small number of overlapping users causes the model to converge to sharp minima, leading to poor generalization. Based on this observation, we leverage loss-geometry-based machine learning approach and propose a novel CDR method called Sharpness-Aware CDR (SCDR). Our proposed method simultaneously optimizes recommendation loss and loss sharpness, leading to better generalization with theoretical guarantees. Empirical studies on real-world datasets demonstrate that SCDR significantly outperforms the other CDR models for cold-start recommendation tasks, while concurrently enhancing the model's robustness to adversarial attacks.
Exploring Query Understanding for Amazon Product Search
Authors: Chen Luo, Xianfeng Tang, Hanqing Lu, Yaochen Xie, Hui Liu, Zhenwei Dai, Limeng Cui, Ashutosh Joshi, Sreyashi Nag, Yang Li, Zhen Li, Rahul Goutam, Jiliang Tang, Haiyang Zhang, Qi He
Date: 2024-08-05 00:00:00
Tasks: Multi-Task Learning

Online shopping platforms, such as Amazon, offer services to billions of people worldwide. Unlike web search or other search engines, product search engines have their unique characteristics, primarily featuring short queries which are mostly a combination of product attributes and structured product search space. The uniqueness of product search underscores the crucial importance of the query understanding component. However, there are limited studies focusing on exploring this impact within real-world product search engines. In this work, we aim to bridge this gap by conducting a comprehensive study and sharing our year-long journey investigating how the query understanding service impacts Amazon Product Search. Firstly, we explore how query understanding-based ranking features influence the ranking process. Next, we delve into how the query understanding system contributes to understanding the performance of a ranking model. Building on the insights gained from our study on the evaluation of the query understanding-based ranking model, we propose a query understanding-based multi-task learning framework for ranking. We present our studies and investigations using the real-world system on Amazon Search.
Embedding Compression in Recommender Systems: A Survey
Authors: Shiwei Li, Huifeng Guo, Xing Tang, Ruiming Tang, Lu Hou, Ruixuan Li, Rui Zhang
Date: 2024-08-05 00:00:00
Tasks: Recommendation Systems, Survey

To alleviate the problem of information explosion, recommender systems are widely deployed to provide personalized information filtering services. Usually, embedding tables are employed in recommender systems to transform high-dimensional sparse one-hot vectors into dense real-valued embeddings. However, the embedding tables are huge and account for most of the parameters in industrial-scale recommender systems. In order to reduce memory costs and improve efficiency, various approaches are proposed to compress the embedding tables. In this survey, we provide a comprehensive review of embedding compression approaches in recommender systems. We first introduce deep learning recommendation models and the basic concept of embedding compression in recommender systems. Subsequently, we systematically organize existing approaches into three categories, namely low-precision, mixed-dimension, and weight-sharing, respectively. Lastly, we summarize the survey with some general suggestions and provide future prospects for this field.
Feedback Reciprocal Graph Collaborative Filtering
Authors: Weijun Chen, Yuanchen Bei, Qijie Shen, Hao Chen, Xiao Huang, Feiran Huang
Date: 2024-08-05 00:00:00
Tasks: Collaborative Filtering, Contrastive Learning, Recommendation Systems

Collaborative filtering on user-item interaction graphs has achieved success in the industrial recommendation. However, recommending users' truly fascinated items poses a seesaw dilemma for collaborative filtering models learned from the interaction graph. On the one hand, not all items that users interact with are equally appealing. Some items are genuinely fascinating to users, while others are unfascinated. Training graph collaborative filtering models in the absence of distinction between them can lead to the recommendation of unfascinating items to users. On the other hand, disregarding the interacted but unfascinating items during graph collaborative filtering will result in an incomplete representation of users' interaction intent, leading to a decline in the model's recommendation capabilities. To address this seesaw problem, we propose Feedback Reciprocal Graph Collaborative Filtering (FRGCF), which emphasizes the recommendation of fascinating items while attenuating the recommendation of unfascinating items. Specifically, FRGCF first partitions the entire interaction graph into the Interacted & Fascinated (I&F) graph and the Interacted & Unfascinated (I&U) graph based on the user feedback. Then, FRGCF introduces separate collaborative filtering on the I&F graph and the I&U graph with feedback-reciprocal contrastive learning and macro-level feedback modeling. This enables the I&F graph recommender to learn multi-grained interaction characteristics from the I&U graph without being misdirected by it. Extensive experiments on four benchmark datasets and a billion-scale industrial dataset demonstrate that FRGCF improves the performance by recommending more fascinating items and fewer unfascinating items. Besides, online A/B tests on Taobao's recommender system verify the superiority of FRGCF.
Towards Coarse-grained Visual Language Navigation Task Planning Enhanced by Event Knowledge Graph
Authors: Zhao Kaichen, Song Yaoxian, Zhao Haiquan, Liu Haoyu, Li Tiefeng, Li Zhixu
Date: 2024-08-05 00:00:00
Tasks: Language Modelling, Large Language Model, Task Planning

Visual language navigation (VLN) is one of the important research in embodied AI. It aims to enable an agent to understand the surrounding environment and complete navigation tasks. VLN instructions could be categorized into coarse-grained and fine-grained commands. Fine-grained command describes a whole task with subtasks step-by-step. In contrast, coarse-grained command gives an abstract task description, which more suites human habits. Most existing work focuses on the former kind of instruction in VLN tasks, ignoring the latter abstract instructions belonging to daily life scenarios. To overcome the above challenge in abstract instruction, we attempt to consider coarse-grained instruction in VLN by event knowledge enhancement. Specifically, we first propose a prompt-based framework to extract an event knowledge graph (named VLN-EventKG) for VLN integrally over multiple mainstream benchmark datasets. Through small and large language model collaboration, we realize knowledge-enhanced navigation planning (named EventNav) for VLN tasks with coarse-grained instruction input. Additionally, we design a novel dynamic history backtracking module to correct potential error action planning in real time. Experimental results in various public benchmarks show our knowledge-enhanced method has superiority in coarse-grained-instruction VLN using our proposed VLN-EventKG with over $5\%$ improvement in success rate. Our project is available at https://sites.google.com/view/vln-eventkg
pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy
Authors: Kartheik G. Iyer, Mikaeel Yunus, Charles O'Neill, Christine Ye, Alina Hyk, Kiera McCormick, Ioana Ciuca, John F. Wu, Alberto Accomazzi, Simone Astarita, Rishabh Chakrabarty, Jesse Cranney, Anjalie Field, Tirthankar Ghosal, Michele Ginolfi, Marc Huertas-Company, Maja Jablonska, Sandor Kruk, Huiling Liu, Gabriel Marchidan, Rohit Mistry, J. P. Naiman, J. E. G. Peek, Mugdha Polimera, Sergio J. Rodriguez, Kevin Schawinski, Sanjib Sharma, Michael J. Smith, Yuan-Sen Ting, Mike Walmsley
Date: 2024-08-02 00:00:00
Tasks: Astronomy, Pathfinder

The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present Pathfinder, a machine learning framework designed to enable literature review and knowledge discovery in astronomy, focusing on semantic searching with natural language instead of syntactic searches with keywords. Utilizing state-of-the-art large language models (LLMs) and a corpus of 350,000 peer-reviewed papers from the Astrophysics Data System (ADS), Pathfinder offers an innovative approach to scientific inquiry and literature exploration. Our framework couples advanced retrieval techniques with LLM-based synthesis to search astronomical literature by semantic context as a complement to currently existing methods that use keywords or citation graphs. It addresses complexities of jargon, named entities, and temporal aspects through time-based and citation-based weighting schemes. We demonstrate the tool's versatility through case studies, showcasing its application in various research scenarios. The system's performance is evaluated using custom benchmarks, including single-paper and multi-paper tasks. Beyond literature review, Pathfinder offers unique capabilities for reformatting answers in ways that are accessible to various audiences (e.g. in a different language or as simplified text), visualizing research landscapes, and tracking the impact of observatories and methodologies. This tool represents a significant advancement in applying AI to astronomical research, aiding researchers at all career stages in navigating modern astronomy literature.
On Validation of Search & Retrieval of Tissue Images in Digital Pathology
Authors: H. R. Tizhoosh
Date: 2024-08-02 00:00:00
Tasks: Content-Based Image Retrieval, Diagnostic, Image Retrieval, Management, Retrieval

Medical images play a crucial role in modern healthcare by providing vital information for diagnosis, treatment planning, and disease monitoring. Fields such as radiology and pathology rely heavily on accurate image interpretation, with radiologists examining X-rays, CT scans, and MRIs to diagnose conditions from fractures to cancer, while pathologists use microscopy and digital images to detect cellular abnormalities for diagnosing cancers and infections. The technological advancements have exponentially increased the volume and complexity of medical images, necessitating efficient tools for management and retrieval. Content-Based Image Retrieval (CBIR) systems address this need by searching and retrieving images based on visual content, enhancing diagnostic accuracy by allowing clinicians to find similar cases and compare pathological patterns. Comprehensive validation of image search engines in medical applications involves evaluating performance metrics like accuracy, indexing, and search times, and storage overhead, ensuring reliable and efficient retrieval of accurate results, as demonstrated by recent validations in histopathology.
A Novel Evaluation Framework for Image2Text Generation
Authors: Jia-Hong Huang, Hongyi Zhu, Yixian Shen, Stevan Rudinac, Alessio M. Pacces, Evangelos Kanoulas
Date: 2024-08-03 00:00:00
Tasks: Image Captioning, Image Generation, Language Modelling, Large Language Model

Evaluating the quality of automatically generated image descriptions is challenging, requiring metrics that capture various aspects such as grammaticality, coverage, correctness, and truthfulness. While human evaluation offers valuable insights, its cost and time-consuming nature pose limitations. Existing automated metrics like BLEU, ROUGE, METEOR, and CIDEr aim to bridge this gap but often show weak correlations with human judgment. We address this challenge by introducing a novel evaluation framework rooted in a modern large language model (LLM), such as GPT-4 or Gemini, capable of image generation. In our proposed framework, we begin by feeding an input image into a designated image captioning model, chosen for evaluation, to generate a textual description. Using this description, an LLM then creates a new image. By extracting features from both the original and LLM-created images, we measure their similarity using a designated similarity metric. A high similarity score suggests that the image captioning model has accurately generated textual descriptions, while a low similarity score indicates discrepancies, revealing potential shortcomings in the model's performance. Human-annotated reference captions are not required in our proposed evaluation framework, which serves as a valuable tool for evaluating the effectiveness of image captioning models. Its efficacy is confirmed through human evaluation.
Neural Term Structure of Additive Process for Option Pricing
Authors: Jimin Lin, Guixin Liu
Date: 2024-08-03 00:00:00

The additive process generalizes the L\'evy process by relaxing its assumption of time-homogeneous increments and hence covers a larger family of stochastic processes. Recent research in option pricing shows that modeling the underlying log price with an additive process has advantages in easier construction of the risk-neural measure, an explicit option pricing formula and characteristic function, and more flexibility to fit the implied volatility surface. Still, the challenge of calibrating an additive model arises from its time-dependent parameterization, for which one has to prescribe parametric functions for the term structure. For this, we propose the neural term structure model to utilize feedforward neural networks to represent the term structure, which alleviates the difficulty of designing parametric functions and thus attenuates the misspecification risk. Numerical studies with S\&P 500 option data are conducted to evaluate the performance of the neural term structure.
Principal component analysis balancing prediction and approximation accuracy for spatial data
Authors: Si Cheng, Magali N. Blanco, Timothy V. Larson, Lianne Sheppard, Adam Szpiro, Ali Shojaie
Date: 2024-08-03 00:00:00
Tasks: Dimensionality Reduction

Dimension reduction is often the first step in statistical modeling or prediction of multivariate spatial data. However, most existing dimension reduction techniques do not account for the spatial correlation between observations and do not take the downstream modeling task into consideration when finding the lower-dimensional representation. We formalize the closeness of approximation to the original data and the utility of lower-dimensional scores for downstream modeling as two complementary, sometimes conflicting, metrics for dimension reduction. We illustrate how existing methodologies fall into this framework and propose a flexible dimension reduction algorithm that achieves the optimal trade-off. We derive a computationally simple form for our algorithm and illustrate its performance through simulation studies, as well as two applications in air pollution modeling and spatial transcriptomics.
Winners with Confidence: Discrete Argmin Inference with an Application to Model Selection
Authors: Tianyu Zhang, Hao Lee, Jing Lei
Date: 2024-08-04 00:00:00
Tasks: Model Selection

We study the problem of finding the index of the minimum value of a vector from noisy observations. This problem is relevant in population/policy comparison, discrete maximum likelihood, and model selection. We develop an asymptotically normal test statistic, even in high-dimensional settings and with potentially many ties in the population mean vector, by integrating concepts and tools from cross-validation and differential privacy. The key technical ingredient is a central limit theorem for globally dependent data. We also propose practical ways to select the tuning parameter that adapts to the signal landscape. Numerical experiments and data examples demonstrate the ability of the proposed method to achieve a favorable bias-variance trade-off in practical scenarios.
Embedding generalization within the learning dynamics: An approach based-on sample path large deviation theory
Authors: Getachew K. Befekadu
Date: 2024-08-04 00:00:00

We consider a typical learning problem of point estimations for modeling of nonlinear functions or dynamical systems in which generalization, i.e., verifying a given learned model, can be embedded as an integral part of the learning process or dynamics. In particular, we consider an empirical risk minimization based learning problem that exploits gradient methods from continuous-time perspective with small random perturbations, which is guided by the training dataset loss. Here, we provide an asymptotic probability estimate in the small noise limit based-on the Freidlin-Wentzell theory of large deviations, when the sample path of the random process corresponding to the randomly perturbed gradient dynamical system hits a certain target set, i.e., a rare event, when the latter is specified by the testing dataset loss landscape. Interestingly, the proposed framework can be viewed as one way of improving generalization and robustness in learning problems that provides new insights leading to optimal point estimates which is guided by training data loss, while, at the same time, the learning dynamics has an access to the testing dataset loss landscape in some form of future achievable or anticipated target goal. Moreover, as a by-product, we establish a connection with optimal control problem, where the target set, i.e., the rare event, is considered as the desired outcome or achievable target goal for a certain optimal control problem, for which we also provide a verification result reinforcing the rationale behind the proposed framework. Finally, we present a computational algorithm that solves the corresponding variational problem leading to an optimal point estimates and, as part of this work, we also present some numerical results for a typical case of nonlinear regression problem.