Search Results for author:
Found 209 papers, 94 papers with code
Multimodal 3D Genome Pre-training
Here, we propose MIX-HIC, the first multimodal foundation model of 3D genome that integrates both 3D genome structure and epigenomic tracks, which obtains unified and comprehensive semantics.
TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planning
Large language models (LLMs) have shown promise in automating travel planning, yet they often fall short in addressing nuanced spatiotemporal rationality.
3DBonsai: Structure-Aware Bonsai Modeling Using Conditioned 3D Gaussian Splatting
Recent advancements in text-to-3D generation have shown remarkable results by leveraging 3D priors in combination with 2D diffusion.
Logic-in-Frames: Dynamic Keyframe Search via Visual Semantic-Logical Verification for Long Video Understanding
Understanding long video content is a complex endeavor that often relies on densely sampled frame captions or end-to-end feature selectors, yet these techniques commonly overlook the logical relationships between textual queries and visual elements.
Cognitive Disentanglement for Referring Multi-Object Tracking
Specifically, our framework comprises three collaborative components: (1)The Bidirectional Interactive Fusion module first establishes cross-modal connections while preserving modality-specific characteristics; (2) Building upon this foundation, the Progressive Semantic-Decoupled Query Learning mechanism hierarchically injects complementary information into object queries, progressively refining object understanding from coarse to fine-grained semantic levels; (3) Finally, the Structural Consensus Constraint enforces bidirectional semantic consistency between visual features and language descriptions, ensuring that tracked objects faithfully reflect the referring expression.
Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks
To fill this gap, we first undertake a systematic review of GIA and categorize existing methods into three types, i. e., \textit{optimization-based} GIA (OP-GIA), \textit{generation-based} GIA (GEN-GIA), and \textit{analytics-based} GIA (ANA-GIA).
HiCMamba: Enhancing Hi-C Resolution and Identifying 3D Genome Structures with State Space Modeling
Hi-C technology measures genome-wide interaction frequencies, providing a powerful tool for studying the 3D genomic structure within the nucleus.
From Understanding to Excelling: Template-Free Algorithm Design through Structural-Functional Co-Evolution
Large language models (LLMs) have greatly accelerated the automation of algorithm generation and optimization.
SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Models
In recent years, the rapid advancement of Artificial Intelligence (AI) technologies, particularly Large Language Models (LLMs), has revolutionized the paradigm of scientific discovery, establishing AI-for-Science (AI4Science) as a dynamic and evolving field.
TimeFound: A Foundation Model for Time Series Forecasting
We present TimeFound, an encoder-decoder transformer-based time series foundation model for out-of-the-box zero-shot forecasting.
Simulating the Real World: A Unified Survey of Multimodal Generative Models
Understanding and replicating the real world is a critical challenge in Artificial General Intelligence (AGI) research.
Mark Your LLM: Detecting the Misuse of Open-Source Large Language Models via Watermarking
Our experiments reveal that backdoor watermarking could effectively detect IP Violation, while inference-time watermark distillation is applicable in both scenarios but less robust to further fine-tuning and has a more significant impact on LLM performance compared to backdoor watermarking.
SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reduction
Large Language Models (LLMs) have demonstrated improved generation performance by incorporating externally retrieved knowledge, a process known as retrieval-augmented generation (RAG).
Can Large Language Models Help Experimental Design for Causal Discovery?
Designing proper experiments and selecting optimal intervention targets is a longstanding problem in scientific or causal discovery.
MR-EIT: Multi-Resolution Reconstruction for Electrical Impedance Tomography via Data-Driven and Unsupervised Dual-Mode Neural Networks
This paper presents a multi-resolution reconstruction method for Electrical Impedance Tomography (EIT), referred to as MR-EIT, which is capable of operating in both supervised and unsupervised learning modes.
A Survey of fMRI to Image Reconstruction
Functional magnetic resonance imaging (fMRI) based image reconstruction plays a pivotal role in decoding human perception, with applications in neuroscience and brain-computer interfaces.
LongFaith: Enhancing Long-Context Reasoning in LLMs with Faithful Synthetic Data
Despite the growing development of long-context large language models (LLMs), data-centric approaches relying on synthetic data have been hindered by issues related to faithfulness, which limit their effectiveness in enhancing model performance on tasks such as long-context reasoning and question answering (QA).
Unleashing the Power of Large Language Model for Denoising Recommendation
Recommender systems are crucial for personalizing user experiences but often depend on implicit feedback data, which can be noisy and misleading.
GENERator: A Long-Context Generative Genomic Foundation Model
Recent developments in genomic language models have underscored the potential of LLMs in deciphering DNA sequences.
Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models
In this paper, we propose the Confidence-aware KG-based Recommendation Framework with LLM Augmentation (CKG-LLMA), a novel framework that combines KGs and LLMs for recommendation task.
OneForecast: A Universal Framework for Global and Regional Weather Forecasting
Accurate weather forecasts are important for disaster prevention, agricultural planning, and water resource management.
RGB-Event ISP: The Dataset and Benchmark
In summary, to the best of our knowledge, this is the very first research focusing on event-guided ISP, and we hope it will inspire the community.
LLM-powered Multi-agent Framework for Goal-oriented Learning in Intelligent Tutoring System
After identifying the skill gap, it schedules an efficient learning path using an evolving optimization approach, driven by a comprehensive and dynamic profile of learners' multifaceted status.
A Comprehensive Survey on Self-Interpretable Neural Networks
Neural networks have achieved remarkable success across various fields.
Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation
To address these limitations, we propose a Hierarchical time-aware Mixture of experts for multi-modal Sequential Recommendation (HM4SR) with a two-level Mixture of Experts (MoE) and a multi-task learning strategy.
EventVL: Understand Event Streams via Multimodal Large Language Model
The event-based Vision-Language Model (VLM) recently has made good progress for practical vision tasks.
Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation
This survey examines the key aspects of tabular data-centric AI, emphasizing feature selection and feature generation as essential techniques for data space refinement.
Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks
Extensive experiments demonstrate that CauSiam effectively improves the generalization performance of existing SIDD methods in continuously changing domains.
CHAT: Beyond Contrastive Graph Transformer for Link Prediction in Heterogeneous Networks
These findings substantiate the efficacy of CHAT in addressing the complex problem of link prediction in heterogeneous networks.
Uni-Renderer: Unifying Rendering and Inverse Rendering Via Dual Stream Diffusion
The rendering equation is the core of the two tasks, as an ideal conditional distribution transfer function from intrinsic properties to RGB images.
Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition
However, existing FER methods face three key challenges in real-world applications: (i) they rely on large amounts of continuously annotated data to ensure accuracy since emotions are complex and ambiguous in reality, which is costly and time-consuming; (ii) they cannot capture the temporal heterogeneity caused by changing emotion patterns, because they usually assume that the temporal correlation within sampling periods is the same; (iii) they do not consider the spatial heterogeneity of different FER scenarios, that is, the distribution of emotion information in different data may have bias or interference.
Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey
The rapid development of Artificial Intelligence (AI) has revolutionized numerous fields, with large language models (LLMs) and computer vision (CV) systems driving advancements in natural language understanding and visual processing, respectively.
Rethinking Generalizability and Discriminability of Self-Supervised Learning from Evolutionary Game Theory Perspective
Thus, to enhance the methodological generalization, we propose a novel self-supervised learning method that leverages advancements in reinforcement learning to jointly benefit from the general guidance of EGT and sequentially optimize the model to chase the consistent improvement of generalizability and discriminability for specific target domains during pre-training.
Neuromodulated Meta-Learning
To investigate the role of flexible network structure (FNS) in meta-learning, we conduct extensive empirical and theoretical analyses, finding that model performance is tied to structure, with no universally optimal pattern across tasks.
TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection
With the development of large language models (LLMs), the ability to handle longer contexts has become a key capability for Web applications such as cross-document understanding and LLM-powered search systems.
Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs
To address these limitations, we propose a novel self-correcting adaptive planning paradigm for KG-augmented LLM named Plan-on-Graph (PoG), which first decomposes the question into several sub-objectives and then repeats the process of adaptively exploring reasoning paths, updating memory, and reflecting on the need to self-correct erroneous reasoning paths until arriving at the answer.
NeuGPT: Unified multi-modal Neural GPT
This paper introduces NeuGPT, a groundbreaking multi-modal language generation model designed to harmonize the fragmented landscape of neural recording research.
Improve Dense Passage Retrieval with Entailment Tuning
Our method can be efficiently plugged into current dense retrieval methods, and experiments show the effectiveness of our method.
CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching
Anomaly detection in multivariate time series is challenging as heterogeneous subsequence anomalies may occur.
Language Model Preference Evaluation with Multiple Weak Evaluators
Despite the remarkable success of Large Language Models (LLMs), evaluating their outputs' quality regarding *preference* remains a critical challenge.
Meta-Transfer Learning Empowered Temporal Graph Networks for Cross-City Real Estate Appraisal
To this end, we propose Meta-Transfer Learning Empowered Temporal Graph Networks (MetaTransfer) to transfer valuable knowledge from multiple data-rich metropolises to the data-scarce city to improve valuation performance.
ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection
As the field of Multimodal Large Language Models (MLLMs) continues to evolve, their potential to revolutionize artificial intelligence is particularly promising, especially in addressing mathematical reasoning tasks.
Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding
Recent development of protein language models (pLMs) with supervised fine tuning provides a promising solution to this problem.
SEAL: SEmantic-Augmented Imitation Learning via Language Model
Hierarchical Imitation Learning (HIL) is a promising approach for tackling long-horizon decision-making tasks.
Labor Migration Modeling through Large-scale Job Query Data
Accurate and timely modeling of labor migration is crucial for various urban governance and commercial tasks, such as local policy-making and business site selection.
Channel-aware Contrastive Conditional Diffusion for Multivariate Probabilistic Time Series Forecasting
Then, we devise an ad-hoc denoising-based temporal contrastive learning to explicitly amplify the predictive mutual information between past observations and future forecasts.
On the Generalization and Causal Explanation in Self-Supervised Learning
Self-supervised learning (SSL) methods learn from unlabeled data and achieve high generalization performance on downstream tasks.
Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting
To this end, in this paper, we formulate the Federated Labor Market Forecasting (FedLMF) problem and propose a Meta-personalized Convergence-aware Clustered Federated Learning (MPCAC-FL) framework to provide accurate and timely collaborative talent demand and supply prediction in a privacy-preserving way.
TC-LLaVA: Rethinking the Transfer from Image to Video Understanding with Temporal Considerations
Multimodal Large Language Models (MLLMs) have significantly improved performance across various image-language applications.
CinePreGen: Camera Controllable Video Previsualization via Engine-powered Diffusion
With advancements in video generative AI models (e. g., SORA), creators are increasingly using these techniques to enhance video previsualization.
Fusing Pruned and Backdoored Models: Optimal Transport-based Data-free Backdoor Mitigation
This method, based on our findings on neuron weight changes (NWCs) of random unlearning, uses optimal transport (OT)-based model fusion to combine the advantages of both pruned and backdoored models.
Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation
Recent advances in Large Language Models (LLMs) have demonstrated significant potential in the field of Recommendation Systems (RSs).
Unleash The Power of Pre-Trained Language Models for Irregularly Sampled Time Series
This progress has inspired a series of innovative studies that explore the adaptation of PLMs to time series analysis, intending to create a unified foundation model that addresses various time series analytical tasks.
On the Causal Sufficiency and Necessity of Multi-Modal Representation Learning
Based on these results, we propose $C^3$ Regularization, a plug-and-play method that enforces the causal completeness of the learned representations by minimizing \(C^3\) risk.
On the Discriminability of Self-Supervised Representation Learning
We provide a theoretical framework showing that the performance gap between SSL and SL mainly stems from the inability of SSL methods to capture the aggregation of similar augmentations and the separation of dissimilar augmentations.
PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization
In addition, PAIL offers meaningful interpretability for the optimization in carbon neutrality.
Explaining Length Bias in LLM-Based Preference Evaluations
The use of large language models (LLMs) as judges, particularly in preference comparisons, has become widespread, but this reveals a notable bias towards longer responses, undermining the reliability of such evaluations.
Open-vocabulary Mobile Manipulation in Unseen Dynamic Environments with 3D Semantic Maps
Open-Vocabulary Mobile Manipulation (OVMM) is a crucial capability for autonomous robots, especially when faced with the challenges posed by unknown and dynamic environments.
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning
Specifically, the whole VNE process is decomposed into an upper-level policy for deciding whether to admit the arriving VNR or not and a lower-level policy for allocating resources of the physical network to meet the requirement of VNR through the HRL approach.
Interventional Imbalanced Multi-Modal Representation Learning via $β$-Generalization Front-Door Criterion
However, our empirical explorations challenge the fundamental idea behind such behavior, and we further conclude that benchmark approaches suffer from certain defects: insufficient theoretical interpretability and limited exploration capability of discriminative knowledge.
Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking
We benchmark a range of models on this dataset, evaluating their performance in standard scenarios, in predictions focused on lower value ranges, and in the presence of structural breaks, providing new insights for further research.
Refiner: Restructure Retrieval Content Efficiently to Advance Question-Answering Capabilities
Experiments show that a trained $\textit{Refiner}$ (with 7B parameters) exhibits significant gain to downstream LLM in improving answer accuracy, and outperforms other state-of-the-art advanced RAG and concurrent compressing approaches in various single-hop and multi-hop QA tasks.
Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion Prediction
Rapid urbanization has significantly escalated traffic congestion, underscoring the need for advanced congestion prediction services to bolster intelligent transportation systems.
Holistic Memory Diversification for Incremental Learning in Growing Graphs
To address that, we introduce a novel holistic Diversified Memory Selection and Generation (DMSG) framework for incremental learning in graphs, which first introduces a buffer selection strategy that considers both intra-class and inter-class diversities, employing an efficient greedy algorithm for sampling representative training nodes from graphs into memory buffers after learning each new task.
MAD: Multi-Alignment MEG-to-Text Decoding
Deciphering language from brain activity is a crucial task in brain-computer interface (BCI) research.
Unveiling and Mitigating Backdoor Vulnerabilities based on Unlearning Weight Changes and Backdoor Activeness
Recently, without poisoned data, unlearning models with clean data and then learning a pruning mask have contributed to backdoor defense.
Editable Concept Bottleneck Models
Concept Bottleneck Models (CBMs) have garnered much attention for their ability to elucidate the prediction process through a humanunderstandable concept layer.
Improving Gloss-free Sign Language Translation by Reducing Representation Density
In this paper, we identify a representation density problem that could be a bottleneck in restricting the performance of gloss-free SLT.
Contrastive Learning
Gloss-free Sign Language Translation
+2
SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Recognition with Jaccard Attentive Spiking Neural Network
This enables SpGesture to mitigate the accuracy degradation caused by distribution shifts.
HR-INR: Continuous Space-Time Video Super-Resolution via Event Camera
However, the highly ill-posed nature of C-STVSR limits the effectiveness of current INR-based methods: they assume linear motion between frames and use interpolation or feature warping to generate features at arbitrary spatiotemporal positions with two consecutive frames.
Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D Referring Expression Comprehension
Moreover, we propose a novel model, T-RadarNet, for 3D REC on point clouds, achieving State-Of-The-Art (SOTA) performance on the Talk2Radar dataset compared to counterparts.
Are EEG-to-Text Models Working?
Our analysis reveals that model performance on noise data can be comparable to that on EEG data.
MIPI 2024 Challenge on Demosaic for HybridEVS Camera: Methods and Results
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems.
COM3D: Leveraging Cross-View Correspondence and Cross-Modal Mining for 3D Retrieval
To address this issue, we propose COM3D, making the first attempt to exploit the cross-view correspondence and cross-modal mining to enhance the retrieval performance.
Uncertainty Quantification on Graph Learning: A Survey
Graphical models, including Graph Neural Networks (GNNs) and Probabilistic Graphical Models (PGMs), have demonstrated their exceptional capabilities across numerous fields.
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
In this paper, we propose a FLexible And Generalizable RL framework for VNE, named FlagVNE.
Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in Surface Electromyographic Signal Analysis
We propose a Short Term Enhancement Module(STEM) which can be easily integrated with various models.
Towards Efficient Resume Understanding: A Multi-Granularity Multi-Modal Pre-Training Approach
In the contemporary era of widespread online recruitment, resume understanding has been widely acknowledged as a fundamental and crucial task, which aims to extract structured information from resume documents automatically.
Enhancing Question Answering for Enterprise Knowledge Bases using Large Language Models
For the generation process, we propose a novel chain of thought (CoT) based fine-tuning method to empower the LLM-based generator to adeptly respond to user questions using retrieved documents.
JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer
Moreover, we adopt a two-stage learning strategy for skill-aware recommendation, in which we utilize the skill distribution to guide JD representation learning in the recall stage, and then combine the user profiles for final prediction in the ranking stage.
Event Camera Demosaicing via Swin Transformer and Pixel-focus Loss
To end this, we present a Swin-Transformer-based backbone and a pixel-focus loss function for demosaicing with missing pixel values in RAW domain processing.
AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms.
Harnessing Large Language Models for Text-Rich Sequential Recommendation
Recent advances in Large Language Models (LLMs) have been changing the paradigm of Recommender Systems (RS).
NeuSpeech: Decode Neural signal as Speech
In this paper, we explore the brain-to-text translation of MEG signals in a speech-decoding formation.
DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging Load
Accordingly, we devise a novel Diffusion model termed DiffPLF for Probabilistic Load Forecasting of EV charging, which can explicitly approximate the predictive load distribution conditioned on historical data and related covariates.
Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation
In the realm of deep learning-based recommendation systems, the increasing computational demands, driven by the growing number of users and items, pose a significant challenge to practical deployment.
Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models
The integration of machine learning techniques has become a cornerstone in the development of intelligent urban services, significantly contributing to the enhancement of urban efficiency, sustainability, and overall livability.
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling
Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks.
BayesPrompt: Prompting Large-Scale Pre-Trained Language Models on Few-shot Inference via Debiased Domain Abstraction
From the perspective of distribution analyses, we disclose that the intrinsic issues behind the phenomenon are the over-multitudinous conceptual knowledge contained in PLMs and the abridged knowledge for target downstream domains, which jointly result in that PLMs mis-locate the knowledge distributions corresponding to the target domains in the universal knowledge embedding space.
Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting
Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis, characterized by extensive input sequences, as opposed to the shorter spans typical of traditional approaches.
LLMLight: Large Language Models as Traffic Signal Control Agents
Extensive experiments conducted on ten real-world and synthetic datasets, along with evaluations by fifteen human experts, demonstrate the exceptional effectiveness, generalization ability, and interpretability of LLMLight with LightGPT, outperforming nine baseline methods and ten advanced LLMs.
Large Language Models are Not Stable Recommender Systems
With the significant successes of large language models (LLMs) in many natural language processing tasks, there is growing interest among researchers in exploring LLMs for novel recommender systems.
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook
Specifically, we first employ causal tools to analyze the primary trustworthiness risks of existing GNNs, underscoring the necessity for GNNs to comprehend the causal mechanisms within graph data.
A Survey of Reasoning with Foundation Models
Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation.
A Survey of Text Watermarking in the Era of Large Language Models
This paper conducts a comprehensive survey of the current state of text watermarking technology, covering four main aspects: (1) an overview and comparison of different text watermarking techniques; (2) evaluation methods for text watermarking algorithms, including their detectability, impact on text or LLM quality, robustness under target or untargeted attacks; (3) potential application scenarios for text watermarking technology; (4) current challenges and future directions for text watermarking.
How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model Ranking
This paper presents LEMR (Label-Efficient Model Ranking) and introduces the MoraBench Benchmark.
GeoDeformer: Geometric Deformable Transformer for Action Recognition
Specifically, at the core of GeoDeformer is the Geometric Deformation Predictor, a module designed to identify and quantify potential spatial and temporal geometric deformations within the given video.
Graph Signal Diffusion Model for Collaborative Filtering
In this paper, we adapt standard diffusion model and propose a novel Graph Signal Diffusion Model for Collaborative Filtering (named GiffCF).
The Impact of Generative Artificial Intelligence on Market Equilibrium: Evidence from a Natural Experiment
Generative artificial intelligence (AI) exhibits the capability to generate creative content akin to human output with greater efficiency and reduced costs.
DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops
We demonstrate theoretically that our approach mitigates the negative effects of feedback loops and unknown exposure mechanisms.
LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay
This paper explores the open research problem of understanding the social behaviors of LLM-based agents.
Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient Alignment
One prevalent solution is the bi-discriminator domain adversarial network, which strives to identify target domain samples outside the support of the source domain distribution and enforces their classification to be consistent on both discriminators.
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
In this survey, we offer a comprehensive and up-to-date review of large models tailored (or adapted) for time series and spatio-temporal data, spanning four key facets: data types, model categories, model scopes, and application areas/tasks.
Machine Learning for Urban Air Quality Analytics: A Survey
In this article, we present a comprehensive survey of ML-based air quality analytics, following a roadmap spanning from data acquisition to pre-processing, and encompassing various analytical tasks such as pollution pattern mining, air quality inference, and forecasting.
Cloth2Body: Generating 3D Human Body Mesh from 2D Clothing
Finally, how to generate diverse and plausible results from a 2D clothing image.
Towards Faithful Neural Network Intrinsic Interpretation with Shapley Additive Self-Attribution
Self-interpreting neural networks have garnered significant interest in research.
Semi-supervised Domain Adaptation in Graph Transfer Learning
To deal with the domain shift, we add adaptive shift parameters to each of the source nodes, which are trained in an adversarial manner to align the cross-domain distributions of node embedding, thus the node classifier trained on labeled source nodes can be transferred to the target nodes.
GRAPH DOMAIN ADAPTATION
Semi-supervised Domain Adaptation
+2
Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network
Accurate traffic forecasting is crucial for the development of Intelligent Transportation Systems (ITS), playing a pivotal role in modern urban traffic management.
DiffCharge: Generating EV Charging Scenarios via a Denoising Diffusion Model
Recent proliferation of electric vehicle (EV) charging events has brought prominent stress over power grid operation.
Digital twin brain: a bridge between biological intelligence and artificial intelligence
In recent years, advances in neuroscience and artificial intelligence have paved the way for unprecedented opportunities for understanding the complexity of the brain and its emulation by computational systems.
Towards Task Sampler Learning for Meta-Learning
We obtain three conclusions: (i) there is no universal task sampling strategy that can guarantee the optimal performance of meta-learning models; (ii) over-constraining task diversity may incur the risk of under-fitting or over-fitting during training; and (iii) the generalization performance of meta-learning models are affected by task diversity, task entropy, and task difficulty.
Multi-Dimensional Ability Diagnosis for Machine Learning Algorithms
Machine learning algorithms have become ubiquitous in a number of applications (e. g. image classification).
Generative Job Recommendations with Large Language Model
The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process.
A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics
To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management.
Token-Event-Role Structure-based Multi-Channel Document-Level Event Extraction
This paper introduces a novel framework for document-level event extraction, incorporating a new data structure called token-event-role and a multi-channel argument role prediction module.
Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective
Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features.
Spatial Heterophily Aware Graph Neural Networks
However, urban graphs usually can be observed to possess a unique spatial heterophily property; that is, the dissimilarity of neighbors at different spatial distances can exhibit great diversity.
UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction
We hope the proposed UUKG fosters research on urban knowledge graphs and broad smart city applications.
HomoGCL: Rethinking Homophily in Graph Contrastive Learning
Contrastive learning (CL) has become the de-facto learning paradigm in self-supervised learning on graphs, which generally follows the "augmenting-contrasting" learning scheme.
GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification
Class imbalance is the phenomenon that some classes have much fewer instances than others, which is ubiquitous in real-world graph-structured scenarios.
Multi-Temporal Relationship Inference in Urban Areas
Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL).
A Survey on Large Language Models for Recommendation
Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS).
Ranked #1 on
on Amazon Review 2023
Manifold Constraint Regularization for Remote Sensing Image Generation
This study identifies a previously overlooked issue: GANs exhibit a heightened susceptibility to overfitting on remote sensing images. To address this challenge, this paper analyzes the characteristics of remote sensing images and proposes manifold constraint regularization, a novel approach that tackles overfitting of GANs on remote sensing images for the first time.
Towards Language-guided Interactive 3D Generation: LLMs as Layout Interpreter with Generative Feedback
Generating and editing a 3D scene guided by natural language poses a challenge, primarily due to the complexity of specifying the positional relations and volumetric changes within the 3D space.
TriMLP: Revenge of a MLP-like Architecture in Sequential Recommendation
In this paper, we present a MLP-like architecture for sequential recommendation, namely TriMLP, with a novel Triangular Mixer for cross-token communications.
UniINR: Event-guided Unified Rolling Shutter Correction, Deblurring, and Interpolation
To query a specific sharp frame (GS or RS), we embed the exposure time into STR and decode the embedded features pixel-by-pixel to recover a sharp frame.
Preference or Intent? Double Disentangled Collaborative Filtering
In this way, the low-rating items will be treated as positive samples for modeling intents while the negative samples for modeling preferences.
Cross-modality Data Augmentation for End-to-End Sign Language Translation
To tackle these challenges, we propose a novel Cross-modality Data Augmentation (XmDA) framework to transfer the powerful gloss-to-text translation capabilities to end-to-end sign language translation (i. e. video-to-text) by exploiting pseudo gloss-text pairs from the sign gloss translation model.
Ranked #4 on
Sign Language Translation
on CSL-Daily
Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph
However, existing HGNNs usually represent each node as a single vector in the multi-layer graph convolution calculation, which makes the high-level graph convolution layer fail to distinguish information from different relations and different orders, resulting in the information loss in the message passing.
A Survey on Deep Learning based Time Series Analysis with Frequency Transformation
Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT.
Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach
Although remarkable progress on the neural table-to-text methods has been made, the generalization issues hinder the applicability of these models due to the limited source tables.
Pontryagin Optimal Control via Neural Networks
Solving real-world optimal control problems are challenging tasks, as the complex, high-dimensional system dynamics are usually unrevealed to the decision maker.
A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection
The core idea of such a framework is to firstly pre-train a basis (or master) model over the URG, and then to adaptively derive specific (or slave) models from the basis model for different regions.
Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity
To this end, we propose a methodology, specifically consistency and complementarity network (CoCoNet), which avails of strict global inter-view consistency and local cross-view complementarity preserving regularization to comprehensively learn representations from multiple views.
Self-Optimizing Feature Transformation
Feature transformation aims to extract a good representation (feature) space by mathematically transforming existing features.
MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning
As a successful approach to self-supervised learning, contrastive learning aims to learn invariant information shared among distortions of the input sample.
Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification
Specifically, we first propose a hierarchical transformer to extract the textual semantic information of proposals.
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences
It is critical and important to detect anomalies in event sequences, which becomes widely available in many application domains. In-deed, various efforts have been made to capture abnormal patterns from event sequences through sequential pattern analysis or event representation learning. However, existing approaches usually ignore the semantic information of event content. To this end, in this paper, we propose a self-attentive encoder-decoder transformer framework, Content-Aware Transformer(CAT), for anomaly detection in event sequences. In CAT, the encoder learns preamble event sequence representations with content awareness, and the decoder embeds sequences under detection into a latent space, where anomalies are distinguishable. Specifically, the event content is first fed to a content-awareness layer, generating representations of each event. The encoder accepts preamble event representation sequence, generating feature maps. In the decoder, an additional token is added at the beginning of the sequence under detection, denoting the sequence status. A one-class objective together with sequence reconstruction loss is collectively applied to train our framework under the label efficiency scheme. Furthermore, CAT is optimized under a scalable and efficient setting. Finally, extensive experiments on three real-world datasets demonstrate the superiority of CAT.
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction
Traffic demand forecasting by deep neural networks has attracted widespread interest in both academia and industry society.
Customized Conversational Recommender Systems
In this paper, we propose two key points for CRS to improve the user experience: (1) Speaking like a human, human can speak with different styles according to the current dialogue context.
Interventional Contrastive Learning with Meta Semantic Regularizer
Contrastive learning (CL)-based self-supervised learning models learn visual representations in a pairwise manner.
Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting
To equip the graph neural network with a flexible and practical graph structure, in this paper, we investigate how to model the evolutionary and multi-scale interactions of time series.
Graph Neural Network
Multivariate Time Series Forecasting
+2
Priors in Deep Image Restoration and Enhancement: A Survey
Image restoration and enhancement is a process of improving the image quality by removing degradations, such as noise, blur, and resolution degradation.
Detect Professional Malicious User with Metric Learning in Recommender Systems
2) the PMU detection model should take both ratings and reviews into consideration, which makes PMU detection a multi-modal problem.
Feature and Instance Joint Selection: A Reinforcement Learning Perspective
Feature selection and instance selection are two important techniques of data processing.
Reinforced Imitative Graph Learning for Mobile User Profiling
The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities.
MetAug: Contrastive Learning via Meta Feature Augmentation
We perform a meta learning technique to build the augmentation generator that updates its network parameters by considering the performance of the encoder.
Robust Local Preserving and Global Aligning Network for Adversarial Domain Adaptation
We conduct theoretical analysis on the robustness of the proposed RLPGA and prove that the robust informative-theoretic-based loss and the local preserving module are beneficial to reduce the empirical risk of the target domain.
Hyperbolic Graph Neural Networks: A Review of Methods and Applications
Graph neural networks generalize conventional neural networks to graph-structured data and have received widespread attention due to their impressive representation ability.
Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams
An event that a user visits a POI in stream updates the states of both users and geospatial contexts; the agent perceives the updated environment state to make online recommendations.
Modelling of Bi-directional Spatio-Temporal Dependence and Users' Dynamic Preferences for Missing POI Check-in Identification
Then, target temporal pattern in combination with user and POI information are fed into a multi-layer network to capture users' dynamic preferences.
Learning to Walk with Dual Agents for Knowledge Graph Reasoning
Graph walking based on reinforcement learning (RL) has shown great success in navigating an agent to automatically complete various reasoning tasks over an incomplete knowledge graph (KG) by exploring multi-hop relational paths.
Multi-Domain Transformer-Based Counterfactual Augmentation for Earnings Call Analysis
Earnings call (EC), as a periodic teleconference of a publicly-traded company, has been extensively studied as an essential market indicator because of its high analytical value in corporate fundamentals.
Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation
To this end, in this paper, we aim to discern the decision-making processes of neural networks through a hierarchical voting strategy by developing an explainable deep learning model, namely Voting Transformation-based Explainable Neural Network (VOTEN).
Topic Modeling Revisited: A Document Graph-based Neural Network Perspective
To this end, in this paper, we revisit the task of topic modeling by transforming each document into a directed graph with word dependency as edges between word nodes, and develop a novel approach, namely Graph Neural Topic Model (GNTM).
Domain-oriented Language Pre-training with Adaptive Hybrid Masking and Optimal Transport Alignment
Also, the word co-occurrences guided semantic learning of pre-training models can be largely augmented by entity-level association knowledge.
Towards Robust Knowledge Graph Embedding via Multi-task Reinforcement Learning
This will lead to low-quality and unreliable representations of KGs.
Exploiting Cross-Modal Prediction and Relation Consistency for Semi-Supervised Image Captioning
In detail, considering that the heterogeneous gap between modalities always leads to the supervision difficulty of using the global embedding directly, CPRC turns to transform both the raw image and corresponding generated sentence into the shared semantic space, and measure the generated sentence from two aspects: 1) Prediction consistency.
Domain-Invariant Representation Learning with Global and Local Consistency
In this paper, we give an analysis of the existing representation learning framework of unsupervised domain adaptation and show that the learned feature representations of the source domain samples are with discriminability, compressibility, and transferability.
Iterative Prediction-and-Optimization for E-Logistics Distribution Network Design
Summary of Contribution: We propose an iterative prediction-and-optimization algorithm for multilevel distribution network design for e-logistics and evaluate its operational value for online retailers.
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction
Though graph contrastive learning (GCL) methods have achieved extraordinary performance with insufficient labeled data, most focused on designing data augmentation schemes for general graphs.
Adversarial Neural Trip Recommendation
To that end, we propose an Adversarial Neural Trip Recommendation (ANT) framework to tackle the above challenges.
Information Theory-Guided Heuristic Progressive Multi-View Coding
To this end, we rethink the existing multi-view learning paradigm from the information theoretical perspective and then propose a novel information theoretical framework for generalized multi-view learning.
Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity
To this end, we propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool).
Ranked #3 on
Protein-Ligand Affinity Prediction
on PDBbind
MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal
Real estate appraisal refers to the process of developing an unbiased opinion for real property's market value, which plays a vital role in decision-making for various players in the marketplace (e. g., real estate agents, appraisers, lenders, and buyers).
Deep Subdomain Adaptation Network for Image Classification
The adaptation can be achieved easily with most feed-forward network models by extending them with LMMD loss, which can be trained efficiently via back-propagation.
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning
In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection.
Heterogeneous Graph Representation Learning with Relation Awareness
Moreover, a semantic fusing module is presented to aggregate relation-aware node representations into a compact representation with the learned relation representations.
Ranked #22 on
Node Property Prediction
on ogbn-mag
Graphfool: Targeted Label Adversarial Attack on Graph Embedding
To the best of our knowledge, this is the first targeted label attack technique.
Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning
Electric Vehicle (EV) has become a preferable choice in the modern transportation system due to its environmental and energy sustainability.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+3
Out-of-Town Recommendation with Travel Intention Modeling
Second, a user-specific travel intention is formulated as an aggregation combining home-town preference and generic travel intention together, where the generic travel intention is regarded as a mixture of inherent intentions that can be learned by Neural Topic Model (NTM).
CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation
Electric vehicles have been rapidly increasing in usage, but stations to charge them have not always kept up with demand, so efficient routing of vehicles to stations is critical to operating at maximum efficiency.
Spatial Object Recommendation with Hints: When Spatial Granularity Matters
We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level.
Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks
Specifically, we first propose a heterogeneous recurrent graph neural network to model the spatiotemporal autocorrelation among air quality and weather monitoring stations.
Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning
Representation learning on heterogeneous graphs aims to obtain low-dimensional node representations that could preserve both node attributes and relation information.
Ranked #24 on
Node Property Prediction
on ogbn-mag
Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction
The hierarchical attentive aggregation can capture spatial dependencies among atoms, as well as fuse the position-enhanced information with the capability of discriminating multiple spatial relations among atoms.
Coupled Layer-wise Graph Convolution for Transportation Demand Prediction
Graph Convolutional Network (GCN) has been widely applied in transportation demand prediction due to its excellent ability to capture non-Euclidean spatial dependence among station-level or regional transportation demands.
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning.
Ranked #1 on
Time Series Forecasting
on ETTh2 (336) Univariate
Joint Intent Detection and Entity Linking on Spatial Domain Queries
Continuous efforts have been devoted to language understanding (LU) for conversational queries with the fast and wide-spread popularity of voice assistants.
T$^2$-Net: A Semi-supervised Deep Model for Turbulence Forecasting
Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs.
Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop
In this journal version, we propose a novel interactive and closed-loop architecture to simultaneously model interactive reinforcement learning (IRL) and decision tree feedback (DTF).
Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning
However, it is still a challenging task since (1) the job title and job transition (job-hopping) data is messy which contains a lot of subjective and non-standard naming conventions for the same position (e. g., Programmer, Software Development Engineer, SDE, Implementation Engineer), (2) there is a large amount of missing title/transition information, and (3) one talent only seeks limited numbers of jobs which brings the incompleteness and randomness modeling job transition patterns.
E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce
Pre-trained language models such as BERT have achieved great success in a broad range of natural language processing tasks.
Learning Adaptive Embedding Considering Incremental Class
To this end, we propose a Class-Incremental Learning without Forgetting (CILF) framework, which aims to learn adaptive embedding for processing novel class detection and model update in a unified framework.
S2OSC: A Holistic Semi-Supervised Approach for Open Set Classification
Open set classification (OSC) tackles the problem of determining whether the data are in-class or out-of-class during inference, when only provided with a set of in-class examples at training time.
Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning
A mobile app interface usually consists of a set of user interface modules.
Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine
Then, we introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation.
Predicting Temporal Sets with Deep Neural Networks
Given a sequence of sets, where each set contains an arbitrary number of elements, the problem of temporal sets prediction aims to predict the elements in the subsequent set.
Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems
Unlike the traditional dock-based systems, dockless bike-sharing systems are more convenient for users in terms of flexibility.
A Survey on Knowledge Graph-Based Recommender Systems
On the one hand, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation.
SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback
The recent development of online recommender systems has a focus on collaborative ranking from implicit feedback, such as user clicks and purchases.
Comprehensive and Efficient Data Labeling via Adaptive Model Scheduling
With limited computing resources and stringent delay, given a data stream and a collection of applicable resource-hungry deep-learning models, we design a novel approach to adaptively schedule a subset of these models to execute on each data item, aiming to maximize the value of the model output (e. g., the number of high-confidence labels).
Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction
However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e. g., camera, ultrasonic sensor, and GPS).
A Machine Learning-enhanced Robust P-Phase Picker for Real-time Seismic Monitoring
To this end, in this paper, we present a machine learning-enhanced framework based on ensemble learning strategy, EL-Picker, for the automatic identification of seismic P-phase arrivals on continuous and massive waveforms.
A Comprehensive Survey on Transfer Learning
In order to show the performance of different transfer learning models, over twenty representative transfer learning models are used for experiments.
STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control
Moreover, based on the temporally-dependent traffic information, we design a Graph Neural Network based model to represent relationships among multiple traffic lights, and the decision for each traffic light will be made in a distributed way by the deep Q-learning method.
EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction
In EERNN, we simply summarize each student's state into an integrated vector and trace it with a recurrent neural network, where we design a bidirectional LSTM to learn the encoding of each exercise's content.
Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation
Then, we integrate the aesthetic features into a cross-domain network to transfer users' domain independent aesthetic preferences.
POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm
In this paper, comprehensive evaluation metrics are brought up for different adversarial attack methods.
