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Articles 91 - 120 of 6884
Full-Text Articles in Physical Sciences and Mathematics
Diffusion Time-Step Curriculum For One Image To 3d Generation, Xuanyu Yi, Zike Wu, Qingshan Xu, Pan Zhou, Joo Hwee Lim, Hanwang Zhang
Diffusion Time-Step Curriculum For One Image To 3d Generation, Xuanyu Yi, Zike Wu, Qingshan Xu, Pan Zhou, Joo Hwee Lim, Hanwang Zhang
Research Collection School Of Computing and Information Systems
Score distillation sampling (SDS) has been widely adopted to overcome the absence of unseen views in reconstructing 3D objects from a single image. It leverages pretrained 2D diffusion models as teacher to guide the reconstruction of student 3D models. Despite their remarkable success, SDS-based methods often encounter geometric artifacts and texture saturation. We find out the crux is the overlooked indiscriminate treatment of diffusion time-steps during optimization: it unreasonably treats the studentteacher knowledge distillation to be equal at all time-steps and thus entangles coarse-grained and fine-grained modeling. Therefore, we propose the Diffusion Time-step Curriculum one-image-to-3D pipeline (DTC123), which involves both …
Detecting Foot Strikes During Running With Earbuds, Changshuo Hu, Thivya Kandappu, Jake Stuchbury-Wass, Yang Liu, Anthony Tang, Cecelia Mascolo, Dong Ma
Detecting Foot Strikes During Running With Earbuds, Changshuo Hu, Thivya Kandappu, Jake Stuchbury-Wass, Yang Liu, Anthony Tang, Cecelia Mascolo, Dong Ma
Research Collection School Of Computing and Information Systems
Running is a widely embraced form of aerobic exercise, offering various physical and mental benefits. However, improper running gaits (i.e., the way of foot landing) can pose safety risks and impact running efficiency. As many runners lack the knowledge or continuous attention to manage their foot strikes during running, in this work, we present a portable and non-invasive running gait monitoring system. Specifically, we leverage the in-ear microphone on wireless earbuds to capture the vibrations generated by foot strikes. Landing with different parts of the foot (e.g., forefoot and heel) generates distinct vibration patterns, and thus we utilize machine learning …
How Is Our Mobility Affected As We Age? Findings From A 934 Users Field Study Of Older Adults Conducted In An Urban Asian City, Yi Zhen Tan, Ngoc Doan Thu Tran, Sapphire Lin, Fang Zhao, Yee Sien Ng, Dong Ma, Jeonggil Ko, Rajesh Krishna Balan
How Is Our Mobility Affected As We Age? Findings From A 934 Users Field Study Of Older Adults Conducted In An Urban Asian City, Yi Zhen Tan, Ngoc Doan Thu Tran, Sapphire Lin, Fang Zhao, Yee Sien Ng, Dong Ma, Jeonggil Ko, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
In this paper, we analyze the results of a large study involving 934 older adults living in an urban Asian city that collected their mobility patterns, in the form of logged GPS data, along with a multitude of demographic and health data. We show that mobility, in terms of average distance travelled per day, is greatly affected by age and by employment status. In addition, other factors such as type of day, household size, physical and financial conditions and the onset of retirement also play a significant role in determining the mobility of an individual. These results will have high …
Ethical Considerations Toward Protestware, Marc Cheong, Raula Kula, Christoph Treude
Ethical Considerations Toward Protestware, Marc Cheong, Raula Kula, Christoph Treude
Research Collection School Of Computing and Information Systems
This article looks into possible scenarios where developers might consider turning their free and open source software into protestware. Using different frameworks commonly used in artificial intelligence (AI) ethics, we extend the applications of AI ethics to the study of protestware.
Consistent3d: Towards Consistent High-Fidelity Text-To-3d Generation With Deterministic Sampling Prior, Zike Wu, Pan Zhou, Xuanyu Yi, Xiaoding Yuan, Hanwang Zhang
Consistent3d: Towards Consistent High-Fidelity Text-To-3d Generation With Deterministic Sampling Prior, Zike Wu, Pan Zhou, Xuanyu Yi, Xiaoding Yuan, Hanwang Zhang
Research Collection School Of Computing and Information Systems
Score distillation sampling (SDS) and its variants have greatly boosted the development of text-to-3D generation, but are vulnerable to geometry collapse and poor textures yet. To solve this issue, we first deeply analyze the SDS and find that its distillation sampling process indeed corresponds to the trajectory sampling of a stochastic differential equation (SDE): SDS samples along an SDE trajectory to yield a less noisy sample which then serves as a guidance to optimize a 3D model. However, the randomness in SDE sampling often leads to a diverse and unpredictable sample which is not always less noisy, and thus is …
Closest Pairs Search Over Data Stream, Rui Zhu Zhu, Bin Wang, Xiaochun Yang, Baihua Zheng
Closest Pairs Search Over Data Stream, Rui Zhu Zhu, Bin Wang, Xiaochun Yang, Baihua Zheng
Research Collection School Of Computing and Information Systems
��-closest pair (KCP for short) search is a fundamental problem in database research. Given a set of��-dimensional streaming data S, KCP search aims to retrieve �� pairs with the shortest distances between them. While existing works have studied continuous 1-closest pair query (i.e., �� = 1) over dynamic data environments, which allow for object insertions/deletions, they require high computational costs and cannot easily support KCP search with �� > 1. This paper investigates the problem of KCP search over data stream, aiming to incrementally maintain as few pairs as possible to support KCP search with arbitrarily ��. To achieve this, we …
Improving Interpretable Embeddings For Ad-Hoc Video Search With Generative Captions And Multi-Word Concept Bank, Jiaxin Wu, Chong-Wah Ngo, Wing-Kwong Chan
Improving Interpretable Embeddings For Ad-Hoc Video Search With Generative Captions And Multi-Word Concept Bank, Jiaxin Wu, Chong-Wah Ngo, Wing-Kwong Chan
Research Collection School Of Computing and Information Systems
Aligning a user query and video clips in cross-modal latent space and that with semantic concepts are two mainstream approaches for ad-hoc video search (AVS). However, the effectiveness of existing approaches is bottlenecked by the small sizes of available video-text datasets and the low quality of concept banks, which results in the failures of unseen queries and the out-of-vocabulary problem. This paper addresses these two problems by constructing a new dataset and developing a multi-word concept bank. Specifically, capitalizing on a generative model, we construct a new dataset consisting of 7 million generated text and video pairs for pre-training. To …
Multigprompt For Multi-Task Pre-Training And Prompting On Graphs, Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhan
Multigprompt For Multi-Task Pre-Training And Prompting On Graphs, Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhan
Research Collection School Of Computing and Information Systems
Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph representation learning. However, their efficacy within an end-to-end supervised framework is significantly tied to the availability of task-specific labels. To mitigate labeling costs and enhance robustness in few-shot settings, pre-training on self-supervised tasks has emerged as a promising method, while prompting has been proposed to further narrow the objective gap between pretext and downstream tasks. Although there has been some initial exploration of prompt-based learning on graphs, they primarily leverage a single pretext task, resulting in a limited subset of general knowledge that could be learned from the …
Plug-And-Play Policy Planner For Large Language Model Powered Dialogue Agents, Yang Deng, Wenxuan Zhang, Wai Lam, See-Kiong Ng, Tat-Seng Chua
Plug-And-Play Policy Planner For Large Language Model Powered Dialogue Agents, Yang Deng, Wenxuan Zhang, Wai Lam, See-Kiong Ng, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Proactive dialogues serve as a practical yet challenging dialogue problem in the era of large language models (LLMs), where the dialogue policy planning is the key to improving the proactivity of LLMs. Most existing studies enable the dialogue policy planning of LLMs using various prompting schemes or iteratively enhance this capability in handling the given case with verbal AI feedback. However, these approaches are either bounded by the policy planning capability of the frozen LLMs or hard to be transferred to new cases. In this work, we introduce a new dialogue policy planning paradigm to strategize LLMs for proactive dialogue …
Grasper: A Generalist Pursuer For Pursuit-Evasion Problems, Pengdeng Li, Shuxin Li, Xinrun Wang, Jakub Cerny, Youzhi Zhang, Stephen Mcaleer, Hau Chan, Bo An
Grasper: A Generalist Pursuer For Pursuit-Evasion Problems, Pengdeng Li, Shuxin Li, Xinrun Wang, Jakub Cerny, Youzhi Zhang, Stephen Mcaleer, Hau Chan, Bo An
Research Collection School Of Computing and Information Systems
Pursuit-evasion games (PEGs) model interactions between a team of pursuers and an evader in graph-based environments such as urban street networks. Recent advancements have demonstrated the effectiveness of the pre-training and fine-tuning paradigm in Policy-Space Response Oracles (PSRO) to improve scalability in solving large-scale PEGs. However, these methods primarily focus on specific PEGs with fixed initial conditions that may vary substantially in real-world scenarios, which significantly hinders the applicability of the traditional methods. To address this issue, we introduce Grasper, a GeneRAlist purSuer for Pursuit-Evasion pRoblems, capable of efficiently generating pursuer policies tailored to specific PEGs. Our contributions are threefold: …
Unraveling The ‘Anomaly’ In Time Series Anomaly Detection: A Self-Supervised Tri-Domain Solution, Yuting Sun, Guansong Pang, Guanhua Ye, Tong Chen, Xia Hu, Hongzhi Yin
Unraveling The ‘Anomaly’ In Time Series Anomaly Detection: A Self-Supervised Tri-Domain Solution, Yuting Sun, Guansong Pang, Guanhua Ye, Tong Chen, Xia Hu, Hongzhi Yin
Research Collection School Of Computing and Information Systems
The ongoing challenges in time series anomaly detection (TSAD), including the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more robust and efficient solution. As limited anomaly labels hinder traditional supervised models in anomaly detection, various state-of-the-art (SOTA) deep learning (DL) techniques (e.g., self-supervised learning) are introduced to tackle this issue. However, they encounter difficulties handling variations in anomaly lengths and shapes, limiting their adaptability to diverse anomalies. Additionally, many benchmark datasets suffer from the problem of having explicit anomalies that even random functions can detect. This problem is …
Diffusion-Based Negative Sampling On Graphs For Link Prediction, Yuan Fang, Yuan Fang
Diffusion-Based Negative Sampling On Graphs For Link Prediction, Yuan Fang, Yuan Fang
Research Collection School Of Computing and Information Systems
Link prediction is a fundamental task for graph analysis with important applications on the Web, such as social network analysis and recommendation systems, etc. Modern graph link prediction methods often employ a contrastive approach to learn robust node representations, where negative sampling is pivotal. Typical negative sampling methods aim to retrieve hard examples based on either predefined heuristics or automatic adversarial approaches, which might be inflexible or difficult to control. Furthermore, in the context of link prediction, most previous methods sample negative nodes from existing substructures of the graph, missing out on potentially more optimal samples in the latent space. …
On The Feasibility Of Simple Transformer For Dynamic Graph Modeling, Yuxia Wu, Yuan Fang, Lizi Liao
On The Feasibility Of Simple Transformer For Dynamic Graph Modeling, Yuxia Wu, Yuan Fang, Lizi Liao
Research Collection School Of Computing and Information Systems
Dynamic graph modeling is crucial for understanding complex structures in web graphs, spanning applications in social networks, recommender systems, and more. Most existing methods primarily emphasize structural dependencies and their temporal changes. However, these approaches often overlook detailed temporal aspects or struggle with long-term dependencies. Furthermore, many solutions overly complicate the process by emphasizing intricate module designs to capture dynamic evolutions. In this work, we harness the strength of the Transformer’s self-attention mechanism, known for adeptly handling long-range dependencies in sequence modeling. Our approach offers a simple Transformer model, called SimpleDyG, tailored for dynamic graph modeling without complex modifications. We …
An Evaluation Of Heart Rate Monitoring With In-Ear Microphones Under Motion, Kayla-Jade Butkow, Ting Dang, Andrea Ferlini, Dong Ma, Yang Liu, Cecilia Mascolo
An Evaluation Of Heart Rate Monitoring With In-Ear Microphones Under Motion, Kayla-Jade Butkow, Ting Dang, Andrea Ferlini, Dong Ma, Yang Liu, Cecilia Mascolo
Research Collection School Of Computing and Information Systems
With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate detection systems. Heart rate is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable heart rate monitoring with wearable devices has therefore gained increasing attention in recent years. Existing heart rate detection systems in wearables mainly rely on photoplethysmography (PPG) sensors, however, these are notorious for poor performance in the presence of human motion. In this work, leveraging the occlusion effect that enhances low-frequency bone-conducted sounds in the ear canal, we investigate for the first time in-ear audio-based motion-resilient …
Large Language Models For Qualitative Research In Software Engineering: Exploring Opportunities And Challenges, Muneera Bano, Rashina Hoda, Didar Zowghi, Christoph Treude
Large Language Models For Qualitative Research In Software Engineering: Exploring Opportunities And Challenges, Muneera Bano, Rashina Hoda, Didar Zowghi, Christoph Treude
Research Collection School Of Computing and Information Systems
The recent surge in the integration of Large Language Models (LLMs) like ChatGPT into qualitative research in software engineering, much like in other professional domains, demands a closer inspection. This vision paper seeks to explore the opportunities of using LLMs in qualitative research to address many of its legacy challenges as well as potential new concerns and pitfalls arising from the use of LLMs. We share our vision for the evolving role of the qualitative researcher in the age of LLMs and contemplate how they may utilize LLMs at various stages of their research experience.
Breathpro: Monitoring Breathing Mode During Running With Earables, Changshuo Hu, Thivya Kandappu, Yang Liu, Cecilia Mascolo, Dong Ma
Breathpro: Monitoring Breathing Mode During Running With Earables, Changshuo Hu, Thivya Kandappu, Yang Liu, Cecilia Mascolo, Dong Ma
Research Collection School Of Computing and Information Systems
Running is a popular and accessible form of aerobic exercise, significantly benefiting our health and wellness. By monitoring a range of running parameters with wearable devices, runners can gain a deep understanding of their running behavior, facilitating performance improvement in future runs. Among these parameters, breathing, which fuels our bodies with oxygen and expels carbon dioxide, is crucial to improving the efficiency of running. While previous studies have made substantial progress in measuring breathing rate, exploration of additional breathing monitoring during running is still lacking. In this work, we fill this gap by presenting BreathPro, the first breathing mode monitoring …
The Impact Of Avatar Completeness On Embodiment And The Detectability Of Hand Redirection In Virtual Reality, Martin Feick, Andre Zenner, Simon Seibert, Anthony Tang, Antonio Krüger
The Impact Of Avatar Completeness On Embodiment And The Detectability Of Hand Redirection In Virtual Reality, Martin Feick, Andre Zenner, Simon Seibert, Anthony Tang, Antonio Krüger
Research Collection School Of Computing and Information Systems
To enhance interactions in VR, many techniques introduce offsets between the virtual and real-world position of users’ hands. Nevertheless, such hand redirection (HR) techniques are only effective as long as they go unnoticed by users—not disrupting the VR experience. While several studies consider how much unnoticeable redirection can be applied, these focus on mid-air floating hands that are disconnected from users’ bodies. Increasingly, VR avatars are embodied as being directly connected with the user’s body, which provide more visual cue anchoring, and may therefore reduce the unnoticeable redirection threshold. In this work, we studied more complete avatars and their effect …
Swapvid: Integrating Video Viewing And Document Exploration With Direct Manipulation, Taichi Murakami, Kazuyuki Fujita, Kotaro Hara, Kazuki Takashima, Yoshifumi Kitamura
Swapvid: Integrating Video Viewing And Document Exploration With Direct Manipulation, Taichi Murakami, Kazuyuki Fujita, Kotaro Hara, Kazuki Takashima, Yoshifumi Kitamura
Research Collection School Of Computing and Information Systems
Videos accompanied by documents—document-based videos—enable presenters to share contents beyond videos and audience to use them for detailed content comprehension. However, concurrently exploring multiple channels of information could be taxing. We propose SwapVid, a novel interface for viewing and exploring document-based videos. SwapVid seamlessly integrates a video and a document into a single view and lets the content behaves as both video and a document; it adaptively switches a document-based video to act as a video or a document upon direct manipulation (e.g., scrolling the document, manipulating the video timeline). We conducted a user study with twenty participants, comparing SwapVid …
Dlvs4audio2sheet: Deep Learning-Based Vocal Separation For Audio Into Music Sheet Conversion, Nicole Teo, Zhaoxia Wang, Ezekiel Ghe, Yee Sen Tan, Kevan Oktavio, Alexander Vincent Lewi, Allyne Zhang, Seng-Beng Ho
Dlvs4audio2sheet: Deep Learning-Based Vocal Separation For Audio Into Music Sheet Conversion, Nicole Teo, Zhaoxia Wang, Ezekiel Ghe, Yee Sen Tan, Kevan Oktavio, Alexander Vincent Lewi, Allyne Zhang, Seng-Beng Ho
Research Collection School Of Computing and Information Systems
While manual transcription tools exist, music enthusiasts, including amateur singers, still encounter challenges when transcribing performances into sheet music. This paper addresses the complex task of translating music audio into music sheets, particularly challenging in the intricate field of choral arrangements where multiple voices intertwine. We propose DLVS4Audio2Sheet, a novel method leveraging advanced deep learning models, Open-Unmix and Band-Split Recurrent Neural Networks (BSRNN), for vocal separation. DLVS4Audio2Sheet segments choral audio into individual vocal sections and selects the optimal model for further processing, aiming towards audio into music sheet conversion. We evaluate DLVS4Audio2Sheet’s performance using these deep learning algorithms and assess …
Flipped Classroom For Linear Algebra At Undergraduate Level, M. Thulasidas
Flipped Classroom For Linear Algebra At Undergraduate Level, M. Thulasidas
Research Collection School Of Computing and Information Systems
In this article, we describe our experience in developing an undergraduate Linear Algebra course tailored to highlight its relevance and applicability in Computer Science. Over the course of three years, the course transitioned from a traditional direct-instruction format to a flipped-classroom design, resulting in positive student learning outcomes. This article covers the course design philosophy, its syllabus, learning objectives, and the incorporation of both quantitative and qualitative student feedback in shaping the course. Furthermore, the article shares the insights gleaned from our experience, which can serve as best practices for instructors aiming to deliver a successful Linear Algebra course for …
The Grader: A Grading Assistant For Lab Tests And A Teaching Tool, M. Thulasidas, David Lo
The Grader: A Grading Assistant For Lab Tests And A Teaching Tool, M. Thulasidas, David Lo
Research Collection School Of Computing and Information Systems
This article presents the design and implementation of the Grader, a grading assistant application deployed for a Web Application Development course at our school. The Grader is equipped to handle various logistical aspects of lab tests, including file management, consistent application of rubrics, and auto-grading of questions with test cases. Additionally, it incorporates heuristic rules to detect cheating attempts. We anticipate that the Grader will find widespread utility in programming courses where lab tests serve as summative assessments. Developed within the same programming environment taught in the class, the Grader also serves as a pedagogical tool, demonstrating to students a …
Algorithms For Canvas-Based Attention Scheduling With Resizing, Yigong Hu, Ila Gokarn, Shengzhong Liu, Archan Misra, Tarek Adbelzaher
Algorithms For Canvas-Based Attention Scheduling With Resizing, Yigong Hu, Ila Gokarn, Shengzhong Liu, Archan Misra, Tarek Adbelzaher
Research Collection School Of Computing and Information Systems
Canvas-based attention scheduling was recently pro-posed to improve the efficiency of real-time machine perception systems. This framework introduces a notion of focus locales, referring to those areas where the attention of the inference system should “allocate its attention”. Data from these locales (e.g., parts of the input video frames containing objects of interest) are packed together into a smaller canvas frame which is processed by the downstream machine learning algorithm. Compared with processing the entire input data frame, this practice saves resources while maintaining inference quality. Previous work was limited to a simplified solution where the focus locales are quantized …
Learning Multi-Faceted Prototypical User Interests, Nhu Thuat Tran, Hady Wirawan Lauw
Learning Multi-Faceted Prototypical User Interests, Nhu Thuat Tran, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
We seek to uncover the latent interest units from behavioral data to better learn user preferences under the VAE framework. Existing practices tend to ignore the multiple facets of item characteristics, which may not capture it at appropriate granularity. Moreover, current studies equate the granularity of item space to that of user interests, which we postulate is not ideal as user interests would likely map to a small subset of item space. In addition, the compositionality of user interests has received inadequate attention, preventing the modeling of interactions between explanatory factors driving a user's decision. To resolve this, we propose …
Anomalyclip: Object-Agnostic Prompt Learning For Zero-Shot Anomaly Detection, Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, Jiming Chen
Anomalyclip: Object-Agnostic Prompt Learning For Zero-Shot Anomaly Detection, Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, Jiming Chen
Research Collection School Of Computing and Information Systems
Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset. It is a crucial task when training data is not accessible due to various concerns, e.g., data privacy, yet it is challenging since the models need to generalize to anomalies across different domains where the appearance of foreground objects, abnormal regions, and background features, such as defects/tumors on different products/ organs, can vary significantly. Recently large pre-trained vision-language models (VLMs), such as CLIP, have demonstrated strong zero-shot recognition ability in various vision tasks, including anomaly detection. However, their …
Non-Vacuous Generalization Bounds For Adversarial Risk In Stochastic Neural Networks, Mustafa Waleed, Liznerski Philipp, Antoine Ledent, Wagner Dennis, Wang Puyu, Kloft Marius
Non-Vacuous Generalization Bounds For Adversarial Risk In Stochastic Neural Networks, Mustafa Waleed, Liznerski Philipp, Antoine Ledent, Wagner Dennis, Wang Puyu, Kloft Marius
Research Collection School Of Computing and Information Systems
Adversarial examples are manipulated samples used to deceive machine learning models, posing a serious threat in safety-critical applications. Existing safety certificates for machine learning models are limited to individual input examples, failing to capture generalization to unseen data. To address this limitation, we propose novel generalization bounds based on the PAC-Bayesian and randomized smoothing frameworks, providing certificates that predict the model’s performance and robustness on unseen test samples based solely on the training data. We present an effective procedure to train and compute the first non-vacuous generalization bounds for neural networks in adversarial settings. Experimental results on the widely recognized …
Compositional Policy Learning In Stochastic Control Systems With Formal Guarantees, Dorde Zikelic, Mathias Lechner, Abhinav Verma, Krishnendu Chatterjee, Thomas A. Henzinger
Compositional Policy Learning In Stochastic Control Systems With Formal Guarantees, Dorde Zikelic, Mathias Lechner, Abhinav Verma, Krishnendu Chatterjee, Thomas A. Henzinger
Research Collection School Of Computing and Information Systems
Reinforcement learning has shown promising results in learning neural network policies for complicated control tasks. However, the lack of formal guarantees about the behavior of such policies remains an impediment to their deployment. We propose a novel method for learning a composition of neural network policies in stochastic environments, along with a formal certificate which guarantees that a specification over the policy's behavior is satisfied with the desired probability. Unlike prior work on verifiable RL, our approach leverages the compositional nature of logical specifications provided in SPECTRL, to learn over graphs of probabilistic reach-avoid specifications. The formal guarantees are provided …
Evaluation Of Orca 2 Against Other Llms For Retrieval Augmented Generation, Donghao Huang, Zhaoxia Wang
Evaluation Of Orca 2 Against Other Llms For Retrieval Augmented Generation, Donghao Huang, Zhaoxia Wang
Research Collection School Of Computing and Information Systems
This study presents a comprehensive evaluation of Microsoft Research’s Orca 2, a small yet potent language model, in the context of Retrieval Augmented Generation (RAG). The research involved comparing Orca 2 with other significant models such as Llama-2, GPT-3.5-Turbo, and GPT-4, particularly focusing on its application in RAG. Key metrics, included faithfulness, answer relevance, overall score, and inference speed, were assessed. Experiments conducted on high-specification PCs revealed Orca 2’s exceptional performance in generating high quality responses and its efficiency on consumer-grade GPUs, underscoring its potential for scalable RAG applications. This study highlights the pivotal role of smaller, efficient models like …
Unveiling Code Pre-Trained Models: Investigating Syntax And Semantics Capacities, Wei Ma, Shangqing Liu, Mengjie Zhao, Xiaofei Xie, Wenhang Wang, Qiang Hu, Jie Zhang, Liu Yang
Unveiling Code Pre-Trained Models: Investigating Syntax And Semantics Capacities, Wei Ma, Shangqing Liu, Mengjie Zhao, Xiaofei Xie, Wenhang Wang, Qiang Hu, Jie Zhang, Liu Yang
Research Collection School Of Computing and Information Systems
Code models have made significant advancements in code intelligence by encoding knowledge about programming languages. While previous studies have explored the capabilities of these models in learning code syntax, there has been limited investigation on their ability to understand code semantics. Additionally, existing analyses assume the number of edges between nodes at the abstract syntax tree (AST) is related to syntax distance, and also often require transforming the high-dimensional space of deep learning models to a low-dimensional one, which may introduce inaccuracies. To study how code models represent code syntax and semantics, we conduct a comprehensive analysis of 7 code …
Large Language Model Powered Agents In The Web, Yang Deng, An Zhang, Yankai Lin, Xu Chen, Ji-Rong Wen, Tat-Seng Chua
Large Language Model Powered Agents In The Web, Yang Deng, An Zhang, Yankai Lin, Xu Chen, Ji-Rong Wen, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Web applications serve as vital interfaces for users to access information, perform various tasks, and engage with content. Traditional web designs have predominantly focused on user interfaces and static experiences. With the advent of large language models (LLMs), there’s a paradigm shift as we integrate LLM-powered agents into these platforms. These agents bring forth crucial human capabilities like memory and planning to make them behave like humans in completing various tasks, effectively enhancing user engagement and offering tailored interactions in web applications. In this tutorial, we delve into the cutting-edge techniques of LLM-powered agents across various web applications, such as …
Reinforcement Nash Equilibrium Solver, Xinrun Wang, Chang Yang, Shuxin Li, Pengdeng Li, Xiao Huang, Hau Chan, Bo An
Reinforcement Nash Equilibrium Solver, Xinrun Wang, Chang Yang, Shuxin Li, Pengdeng Li, Xiao Huang, Hau Chan, Bo An
Research Collection School Of Computing and Information Systems
Nash Equilibrium (NE) is the canonical solution concept of game theory, which provides an elegant tool to understand the rationalities. Computing NE in two- or multi-player general-sum games is PPAD-Complete. Therefore, in this work, we propose REinforcement Nash Equilibrium Solver (RENES), which trains a single policy to modify the games with different sizes and applies the solvers on the modified games where the obtained solution is evaluated on the original games. Specifically, our contributions are threefold. i) We represent the games as ��-rank response graphs and leverage graph neural network (GNN) to handle the games with different sizes as inputs; …