Search Results for author:
Found 37 papers, 18 papers with code
Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers
To address this challenge, we introduce the first benchmark and metric suite for poster generation, which pairs recent conference papers with author-designed posters and evaluates outputs on (i)Visual Quality-semantic alignment with human posters, (ii)Textual Coherence-language fluency, (iii)Holistic Assessment-six fine-grained aesthetic and informational criteria scored by a VLM-as-judge, and notably (iv)PaperQuiz-the poster's ability to convey core paper content as measured by VLMs answering generated quizzes.
LazyVLM: Neuro-Symbolic Approach to Video Analytics
In this paper, we introduce LazyVLM, a neuro-symbolic video analytics system that provides a user-friendly query interface similar to VLMs, while addressing their scalability limitation.
Survey of Video Diffusion Models: Foundations, Implementations, and Applications
Recent advances in diffusion models have revolutionized video generation, offering superior temporal consistency and visual quality compared to traditional generative adversarial networks-based approaches.
GraphOmni: A Comprehensive and Extendable Benchmark Framework for Large Language Models on Graph-theoretic Tasks
This flexible and extendable benchmark not only deepens our understanding of LLM performance on structured tasks but also provides a robust foundation for advancing research in LLM-based graph reasoning.
Demystifying MPNNs: Message Passing as Merely Efficient Matrix Multiplication
While Graph Neural Networks (GNNs) have achieved remarkable success, their design largely relies on empirical intuition rather than theoretical understanding.
An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman Problem
To enhance the solution quality while maintaining fast inference, we propose DEITSP, a diffusion model with efficient iterations tailored for TSP that operates in a NAR manner.
Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning
We propose a novel model named MI-DELIGHT for short text classification in this work.
Scale Invariance of Graph Neural Networks
We address two fundamental challenges in Graph Neural Networks (GNNs): (1) the lack of theoretical support for invariance learning, a critical property in image processing, and (2) the absence of a unified model capable of excelling on both homophilic and heterophilic graph datasets.
Graph Learning
Node Classification on Non-Homophilic (Heterophilic) Graphs
ScaleNet: Scale Invariance Learning in Directed Graphs
This research extends the scale invariance concept to node classification by drawing an analogy to image processing: just as scale invariance being used in image classification to capture multi-scale features, we propose the concept of ``scaled ego-graphs''.
Enhancing Graph Self-Supervised Learning with Graph Interplay
Graph self-supervised learning (GSSL) has emerged as a compelling framework for extracting informative representations from graph-structured data without extensive reliance on labeled inputs.
Multi-Modal Dialogue State Tracking for Playing GuessWhich Game
GuessWhich is an engaging visual dialogue game that involves interaction between a Questioner Bot (QBot) and an Answer Bot (ABot) in the context of image-guessing.
SeLoRA: Self-Expanding Low-Rank Adaptation of Latent Diffusion Model for Medical Image Synthesis
The persistent challenge of medical image synthesis posed by the scarcity of annotated data and the need to synthesize `missing modalities' for multi-modal analysis, underscored the imperative development of effective synthesis methods.
Enhancing Visual Dialog State Tracking through Iterative Object-Entity Alignment in Multi-Round Conversations
MDST captures each round of dialog history, constructing internal dialogue state representations defined as 2-tuples of vision-language representations.
Phased Instruction Fine-Tuning for Large Language Models
Instruction Fine-Tuning enhances pre-trained language models from basic next-word prediction to complex instruction-following.
Rethinking Spectral Augmentation for Contrast-based Graph Self-Supervised Learning
The recent surge in contrast-based graph self-supervised learning has prominently featured an intensified exploration of spectral cues.
MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series
To improve the transparency of LLMs, the research community has formed to open-source truly open LLMs (e. g., Pythia, Amber, OLMo), where more details (e. g., pre-training corpus and training code) are being provided.
ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Models
Recent research in tabular data synthesis has focused on single tables, whereas real-world applications often involve complex data with tens or hundreds of interconnected tables.
Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness
These discrepancies can be used to approximate inequality and provide valuable insights to fairness issues.
Exploring Public Attention in the Circular Economy through Topic Modelling with Twin Hyperparameter Optimisation
To advance the circular economy (CE), it is crucial to gain insights into the evolution of public attention, cognitive pathways of the masses concerning circular products, and to identify primary concerns.
HaVTR: Improving Video-Text Retrieval Through Augmentation Using Large Foundation Models
While recent progress in video-text retrieval has been driven by the exploration of powerful model architectures and training strategies, the representation learning ability of video-text retrieval models is still limited due to low-quality and scarce training data annotations.
SAIS: A Novel Bio-Inspired Artificial Immune System Based on Symbiotic Paradigm
We propose a novel type of Artificial Immune System (AIS): Symbiotic Artificial Immune Systems (SAIS), drawing inspiration from symbiotic relationships in biology.
Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems
The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem with broad real-world applications.
CDIDN: A Registration Model with High Deformation Impedance Capability for Long-Term Tracking of Pulmonary Lesion Dynamics
In this paper, we propose a novel registration model called Cascade-Dilation Inter-Layer Differential Network (CDIDN), which exhibits both high deformation impedance capability (DIC) and accuracy.
PaCaNet: A Study on CycleGAN with Transfer Learning for Diversifying Fused Chinese Painting and Calligraphy
Using one-shot learning to increase the creativity of pre-trained models and diversify the content of the fused images.
Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction
In this research, we focus on the impact of selecting two types of GNN hyperparameters, those belonging to graph-related layers and those of task-specific layers, on the performance of GNN for molecular property prediction.
A Survey on Physarum Polycephalum Intelligent Foraging Behaviour and Bio-Inspired Applications
In recent years, research on Physarum polycephalum has become more popular after Nakagaki et al. (2000) performed their famous experiment showing that Physarum was able to find the shortest route through a maze.
A Genetic Algorithm with Tree-structured Mutation for Hyperparameter Optimisation of Graph Neural Networks
In particular, the genetic algorithm (GA) for HPO has been explored, which treats GNNs as a black-box model, of which only the outputs can be observed given a set of hyperparameters.
A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction
In this paper, we conducted a theoretical analysis of common and specific features for two state-of-the-art and popular algorithms for HPO: TPE and CMA-ES, and we compared them with random search (RS), which is used as a baseline.
Hyperparameter Optimization
Molecular Property Prediction
+1
A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks
While in the proposed fast evaluation process, the training will be interrupted at an early stage, the difference of RMSE values between the starting and interrupted epochs will be used as a fast score, which implies the potential of the GNN being considered.
ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network Architectures
In addition, in order to facilitate the mutation operation, we propose a novel two-component based neural structure coding strategy.
Guessing State Tracking for Visual Dialogue
This paper proposes a guessing state for the Guesser, and regards guess as a process with change of guessing state through a dialogue.
Short Text Classification via Term Graph
Short text classi cation is a method for classifying short sentence with prede ned labels.
ImmuNeCS: Neural Committee Search by an Artificial Immune System
Instead of searching for the 1-best architecture for a given task, we focus on building a population of neural networks that are then ensembled into a neural network committee, an approach we dub 'Neural Committee Search'.
Visual Dialogue State Tracking for Question Generation
A visual dialogue state is defined as the distribution on objects in the image as well as representations of objects.
DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence
In this paper we propose DeepSwarm, a novel neural architecture search (NAS) method based on Swarm Intelligence principles.
Towards making NLG a voice for interpretable Machine Learning
This paper presents a study to understand the issues related to using NLG to humanise explanations from a popular interpretable machine learning framework called LIME.
BIG-bench Machine Learning
Interpretable Machine Learning
+1
e-Distance Weighted Support Vector Regression
We propose a novel support vector regression approach called e-Distance Weighted Support Vector Regression (e-DWSVR). e-DWSVR specifically addresses two challenging issues in support vector regression: first, the process of noisy data; second, how to deal with the situation when the distribution of boundary data is different from that of the overall data.
