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Found 69 papers, 25 papers with code
Efficient Dynamic Attributed Graph Generation
iii) Current state-of-the-art dynamic graph generators are based on the temporal random walk, making the simulation process time-consuming.
Distributed Coordination of Grid-Forming and Grid-Following Inverter-Based Resources for Optimal Frequency Control in Power Systems
With the fast-growing penetration of power inverter-interfaced renewable generation, power systems face significant challenges in maintaining power balance and the nominal frequency.
Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning
In order to improve the efficiency, PoG prunes irrelevant information from the graph exploration first and introduces efficient three-step pruning techniques that incorporate graph structures, LLM prompting, and a pre-trained language model (e. g., SBERT) to effectively narrow down the explored candidate paths.
Ranked #1 on
Knowledge Base Question Answering
on WebQuestions
Router-Tuning: A Simple and Effective Approach for Enabling Dynamic-Depth in Transformers
Traditional transformer models often allocate a fixed amount of computational resources to every input token, leading to inefficient and unnecessary computation.
TCGU: Data-centric Graph Unlearning based on Transferable Condensation
With growing demands for data privacy and model robustness, graph unlearning (GU), which erases the influence of specific data on trained GNN models, has gained significant attention.
DeFine: Enhancing LLM Decision-Making with Factor Profiles and Analogical Reasoning
In this paper, we introduce DeFine, a new framework that constructs probabilistic factor profiles from complex scenarios.
DSBench: How Far Are Data Science Agents to Becoming Data Science Experts?
To bridge this gap, we introduce DSBench, a comprehensive benchmark designed to evaluate data science agents with realistic tasks.
RIDA: A Robust Attack Framework on Incomplete Graphs
However, current research overlooks the real-world scenario of incomplete graphs. To address this gap, we introduce the Robust Incomplete Deep Attack Framework (RIDA).
HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning
Based on this structure, we propose a novel Hierarchical Heterogeneous Graph Transformer (HHGT) model, which seamlessly integrates a Type-level Transformer for aggregating nodes of different types within each k-ring neighborhood, followed by a Ring-level Transformer for aggregating different k-ring neighborhoods in a hierarchical manner.
Coordinate-Aware Thermal Infrared Tracking Via Natural Language Modeling
In this paper, to address these issues, we apply natural language modeling to TIR tracking and propose a coordinate-aware thermal infrared tracking model called NLMTrack, which enhances the utilization of coordinate and temporal information.
xApp Distillation: AI-based Conflict Mitigation in B5G O-RAN
The advancements of machine learning-based (ML) decision-making algorithms created various research and industrial opportunities.
Scaling Synthetic Data Creation with 1,000,000,000 Personas
We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data.
When Reasoning Meets Information Aggregation: A Case Study with Sports Narratives
Finally, the effectiveness of reasoning is influenced by narrative complexity, information density, and domain-specific terms, highlighting the challenges in analytical reasoning tasks.
FakeBench: Probing Explainable Fake Image Detection via Large Multimodal Models
Meanwhile, large multimodal models (LMMs) have exhibited immense visual-text capabilities on various tasks, bringing the potential for explainable fake image detection.
Explainable AI for Fair Sepsis Mortality Predictive Model
By focusing on the predictive modeling of sepsis-related mortality, we propose a method that learns a performance-optimized predictive model and then employs the transfer learning process to produce a model with better fairness.
Hypergraph Self-supervised Learning with Sampling-efficient Signals
Self-supervised learning (SSL) provides a promising alternative for representation learning on hypergraphs without costly labels.
An ExplainableFair Framework for Prediction of Substance Use Disorder Treatment Completion
Fairness of machine learning models in healthcare has drawn increasing attention from clinicians, researchers, and even at the highest level of government.
Polarity Calibration for Opinion Summarization
To address this issue and make the summarizer express both sides of opinions, we introduce the concept of polarity calibration, which aims to align the polarity of output summary with that of input text.
Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation
Inspired by the low-density separation assumption in semi-supervised learning, our key insight is that feature density can shed a light on the most promising direction for the segmentation classifier to explore, which is the regions with lower density.
Continual Segmentation with Disentangled Objectness Learning and Class Recognition
Based on these findings, we propose CoMasTRe by disentangling continual segmentation into two stages: forgetting-resistant continual objectness learning and well-researched continual classification.
Can Large Language Models do Analytical Reasoning?
Our analytical reasoning embodies the tasks of letting large language models count how many points each team scores in a quarter in the NBA and NFL games.
SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs
In this paper, we introduce four novel tasks centered around sports data analytics to evaluate the numerical reasoning and information fusion capabilities of LLMs.
SPECTRUM: Speaker-Enhanced Pre-Training for Long Dialogue Summarization
Multi-turn dialogues are characterized by their extended length and the presence of turn-taking conversations.
FedCore: Straggler-Free Federated Learning with Distributed Coresets
Federated learning (FL) is a machine learning paradigm that allows multiple clients to collaboratively train a shared model while keeping their data on-premise.
SFC: Shared Feature Calibration in Weakly Supervised Semantic Segmentation
Specifically, we leverage the class prototypes that carry positive shared features and propose a Multi-Scaled Distribution-Weighted (MSDW) consistency loss for narrowing the gap between the CAMs generated through classifier weights and class prototypes during training.
InFoBench: Evaluating Instruction Following Ability in Large Language Models
This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models' (LLMs) ability to follow instructions.
UFC-Net: Unrolling Fixed-point Continuous Network for Deep Compressive Sensing
Deep unfolding networks (DUNs) renowned for their interpretability and superior performance have invigorated the realm of compressive sensing (CS).
Zebra: Extending Context Window with Layerwise Grouped Local-Global Attention
This paper introduces a novel approach to enhance the capabilities of Large Language Models (LLMs) in processing and understanding extensive text sequences, a critical aspect in applications requiring deep comprehension and synthesis of large volumes of information.
MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning
Recognizing the need for a comprehensive evaluation of LMM chart understanding, we also propose a MultiModal Chart Benchmark (\textbf{MMC-Benchmark}), a comprehensive human-annotated benchmark with nine distinct tasks evaluating reasoning capabilities over charts.
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning
In this work, we investigate how the instruction tuning adjusts pre-trained models with a focus on intrinsic changes.
Unsupervised Multi-document Summarization with Holistic Inference
SRI balances the importance and diversity of a subset of sentences from the source documents and can be calculated in unsupervised and adaptive manners.
Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models
We investigate how to elicit compositional generalization capabilities in large language models (LLMs).
Ranked #34 on
Math Word Problem Solving
on MATH
FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs
This paper introduces FedSecurity, an end-to-end benchmark that serves as a supplementary component of the FedML library for simulating adversarial attacks and corresponding defense mechanisms in Federated Learning (FL).
Pretrained Language Model based Web Search Ranking: From Relevance to Satisfaction
In this work, we focus on ranking user satisfaction rather than relevance in web search, and propose a PLM-based framework, namely SAT-Ranker, which comprehensively models different dimensions of user satisfaction in a unified manner.
DecipherPref: Analyzing Influential Factors in Human Preference Judgments via GPT-4
Human preference judgments are pivotal in guiding large language models (LLMs) to produce outputs that align with human values.
Streamlining Multimodal Data Fusion in Wireless Communication and Sensor Networks
This paper presents a novel approach for multimodal data fusion based on the Vector-Quantized Variational Autoencoder (VQVAE) architecture.
Hunting Sparsity: Density-Guided Contrastive Learning for Semi-Supervised Semantic Segmentation
Inspired by density-based unsupervised clustering, we propose to leverage feature density to locate sparse regions within feature clusters defined by label and pseudo labels.
FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction
In this paper, we propose a few-shot anomaly detection strategy that works in a low-data regime and can generalize across products at no cost.
OASum: Large-Scale Open Domain Aspect-based Summarization
Specifically, zero/few-shot and fine-tuning results show that the model pre-trained on our corpus demonstrates a strong aspect or query-focused generation ability compared with the backbone model.
Toward Unifying Text Segmentation and Long Document Summarization
The problem is only exacerbated by a lack of segmentation in transcripts of audio/video recordings.
Ranked #6 on
Text Summarization
on Pubmed
Salience Allocation as Guidance for Abstractive Summarization
Recent literature adds extractive summaries as guidance for abstractive summarization models to provide hints of salient content and achieves better performance.
Ranked #7 on
Abstractive Text Summarization
on CNN / Daily Mail
Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination
Large-scale pretrained language models have made significant advances in solving downstream language understanding tasks.
Ranked #2 on
Visual Commonsense Tests
on ViComTe-color
Invariant Aggregator for Defending against Federated Backdoor Attacks
Federated learning enables training high-utility models across several clients without directly sharing their private data.
Federated Meta-Learning for Traffic Steering in O-RAN
In order to provide such services, 5G systems will support various combinations of access technologies such as LTE, NR, NR-U and Wi-Fi.
Transmit Power Control for Indoor Small Cells: A Method Based on Federated Reinforcement Learning
The model will also be used as the base model for adaptive training in the new environment.
Multimodal sensor fusion in the latent representation space
In the second stage, the generative model serves as a reconstruction prior and the search manifold for the sensor fusion tasks.
Variational Autoencoder Assisted Neural Network Likelihood RSRP Prediction Model
Measuring customer experience on mobile data is of utmost importance for global mobile operators.
Sim2real for Reinforcement Learning Driven Next Generation Networks
One of the main reasons is the modelling gap between the simulation and the real environment, which could make the RL agent trained by simulation ill-equipped for the real environment.
Towards Abstractive Grounded Summarization of Podcast Transcripts
Summarization of podcast transcripts is of practical benefit to both content providers and consumers.
RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN
We provide a taxonomy for the challenges faced by ML/RL models throughout the development life-cycle: from the system specification to production deployment (data acquisition, model design, testing and management, etc.).
GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing
However, to realize efficient GNN training is challenging, especially on large graphs.
Robust and Personalized Federated Learning with Spurious Features: an Adversarial Approach
To this end, we propose a strategy to mitigate the effect of spurious features based on our observation that the global model in the federated learning step has a low accuracy disparity due to statistical heterogeneity.
AppQ: Warm-starting App Recommendation Based on View Graphs
In this work, we propose AppQ, a novel app quality grading and recommendation system that extracts inborn features of apps based on app source code.
Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection
Then, we propose a novel temporal message propagation network to extract the graph feature from the normalized graph, and combine the graph feature with designed expert patterns to yield a final detection system.
Self-play Learning Strategies for Resource Assignment in Open-RAN Networks
Open Radio Access Network (ORAN) is being developed with an aim to democratise access and lower the cost of future mobile data networks, supporting network services with various QoS requirements, such as massive IoT and URLLC.
NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversation
To facilitate the research on this corpus, we provide results of several benchmark models.
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning
However, these AutoML pipelines only focus on improving the learning accuracy of benign samples while ignoring the ML model robustness under adversarial attacks.
Identifying Coarse-grained Independent Causal Mechanisms with Self-supervision
Current approaches for learning disentangled representations assume that independent latent variables generate the data through a single data generation process.
Automatic Summarization of Open-Domain Podcast Episodes
Instead, we investigate several less-studied aspects of neural abstractive summarization, including (i) the importance of selecting important segments from transcripts to serve as input to the summarizer; (ii) striking a balance between the amount and quality of training instances; (iii) the appropriate summary length and start/end points.
Graph Neural Network for Fraud Detection via Spatial-Temporal Attention
But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant fraudulent behavior patterns in the online detection system.
FedML: A Research Library and Benchmark for Federated Machine Learning
Federated learning (FL) is a rapidly growing research field in machine learning.
Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints
Text generation from a knowledge base aims to translate knowledge triples to natural language descriptions.
Lipschitz Learning for Signal Recovery
We will develop a theoretical framework to characterize the signals that can be robustly recovered from their observations by an ML algorithm, and establish a Lipschitz condition on signals and observations that is both necessary and sufficient for the existence of a robust recovery.
Location Anomalies Detection for Connected and Autonomous Vehicles
Future Connected and Automated Vehicles (CAV), and more generally ITS, will form a highly interconnected system.
STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification
Thus, a more robust clip-level feature representation can be generated according to a weighted sum operation guided by the mined 2-D attention score matrix.
Large-Scale Person Re-Identification
Video-Based Person Re-Identification
Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels
In this paper, a novel Hierarchical Hypothesis Testing framework with Request-and-Reverify strategy is developed to detect concept drifts by requesting labels only when necessary.
Video Event Recognition With Deep Hierarchical Context Model
Video event recognition still faces great challenges due to large intra-class variation and low image resolution, in particular for surveillance videos.
A Hierarchical Context Model for Event Recognition in Surveillance Video
These three levels of context provide crucial bottom-up, middle level, and top down information that can benefit the recognition task itself.
Ranked #1 on
Action Recognition
on VIRAT Ground 2.0
