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Found 32 papers, 9 papers with code
V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion for Multi-Agent Perception and Prediction
Vehicle-to-everything (V2X) technologies offer a promising paradigm to mitigate the limitations of constrained observability in single-vehicle systems.
Data-Centric and Heterogeneity-Adaptive Sequence Parallelism for Efficient LLM Training
Furthermore, we implement our method in a high-performance system that supports adaptive parallelization in distributed LLM training.
AutoGLM: Autonomous Foundation Agents for GUIs
This limitation underscores the importance of developing foundation agents capable of learning through autonomous environmental interactions by reinforcing existing models.
Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning
This paper addresses the problem of optimizing the allocation of labeling resources for semi-supervised belief representation learning in social networks.
RANSAC Back to SOTA: A Two-stage Consensus Filtering for Real-time 3D Registration
Firstly, one-point RANSAC obtains a consensus set based on length consistency.
SplaTraj: Camera Trajectory Generation with Semantic Gaussian Splatting
These regions are then projected to the camera's view as it moves over time and a cost is constructed.
MHAD: Multimodal Home Activity Dataset with Multi-Angle Videos and Synchronized Physiological Signals
This non-contact, real-time monitoring method holds great potential for home settings.
VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents
Large Multimodal Models (LMMs) have ushered in a new era in artificial intelligence, merging capabilities in both language and vision to form highly capable Visual Foundation Agents.
When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments
The StockAgent allows users to evaluate the impact of different external factors on investor trading and to analyze trading behavior and profitability effects.
Evaluating the Semantic Profiling Abilities of LLMs for Natural Language Utterances in Data Visualization
In this study, we evaluate four publicly available LLMs (GPT-4, Gemini-Pro, Llama3, and Mixtral), investigating their ability to comprehend utterances even in the presence of uncertainty and identify the relevant data context and visual tasks.
Statistical inference for case-control logistic regression via integrating external summary data
When fitting the logistic regression model with case-control data, although the slope parameter of the model can be consistently estimated, the intercept parameter is not identifiable, and the marginal case proportion is not estimatable, either.
An Empirical Analysis on Large Language Models in Debate Evaluation
In this study, we investigate the capabilities and inherent biases of advanced large language models (LLMs) such as GPT-3. 5 and GPT-4 in the context of debate evaluation.
Deep Representation Learning for Multi-functional Degradation Modeling of Community-dwelling Aging Population
As the aging population grows, particularly for the baby boomer generation, the United States is witnessing a significant increase in the elderly population experiencing multifunctional disabilities.
Neurological disorders leading to mechanical dysfunction of the esophagus: an emergent behavior of a neuromechanical dynamical system
Specifically, repetitive antegrade contractions (RACs) are conclusively shown to emerge from the coupled neuromechanical dynamics in response to sustained volumetric distension.
MEDs for PETs: Multilingual Euphemism Disambiguation for Potentially Euphemistic Terms
This study investigates the computational processing of euphemisms, a universal linguistic phenomenon, across multiple languages.
Text Classification Based on Knowledge Graphs and Improved Attention Mechanism
To resolve the semantic ambiguity in texts, we propose a model, which innovatively combines a knowledge graph with an improved attention mechanism.
Guided Image Restoration via Simultaneous Feature and Image Guided Fusion
Currently, joint image filtering-inspired deep learning-based methods represent the state-of-the-art for GIR tasks.
Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration
GOLD distills an offline DT policy into a lightweight policy network through guided online safe RL training, which outperforms both the offline DT policy and online safe RL algorithms.
Completing point cloud from few points by Wasserstein GAN and Transformers
Inspired by the successful applications of GAN and Transformers in the image-based vision task, we introduce GAN and Transformer techniques to address the above problem.
Tensor Recovery Based on A Novel Non-convex Function Minimax Logarithmic Concave Penalty Function
The proposed function is generalized to tensor cases, yielding tensor MLCP and weighted tensor $L\gamma$-norm.
LiDAR-guided Stereo Matching with a Spatial Consistency Constraint
Next, we propose a riverbed enhancement function to optimize the cost volume of the LiDAR projection points and their homogeneous pixels to improve the matching robustness.
Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment
Besides, to avoid manually parameter tuning, we also propose a self-supervised fine-tuning strategy, which can always guarantee a promising performance.
Low-Rank Tensor Completion Based on Bivariate Equivalent Minimax-Concave Penalty
To makes all the constant parameters of the MCP function as variables so that futherly improving the adaptability to the change of singular values in the LRTC problem, we propose the bivariate equivalent minimax-concave penalty (BEMCP) theorem.
ELSR: Efficient Line Segment Reconstruction With Planes and Points Guidance
In this paper, we propose an efficient line segment reconstruction method called ELSR.
Emergency Vehicles Audio Detection and Localization in Autonomous Driving
Hence, all other vehicles are supposed to take proper actions to yield emergency vehicles with active sirens.
Tensor Full Feature Measure and Its Nonconvex Relaxation Applications to Tensor Recovery
Tensor sparse modeling as a promising approach, in the whole of science and engineering has been a huge success.
Two New Low Rank Tensor Completion Methods Based on Sum Nuclear Norm
This paper proposes a new tensor $L_{2, 1}$ norm minimization model (TLNM) that integrates sum nuclear norm (SNN) method, differing from the classical tensor nuclear norm (TNN)-based tensor completion method, with $L_{2, 1}$ norm and Qatar Riyal decomposition for solving the LRTC problem.
Cancer image classification based on DenseNet model
Computer-aided diagnosis establishes methods for robust assessment of medical image-based examination.
Transfer Learning Robustness in Multi-Class Categorization by Fine-Tuning Pre-Trained Contextualized Language Models
This study compares the effectiveness and robustness of multi-class categorization of Amazon product data using transfer learning on pre-trained contextualized language models.
Behavior Pattern and Compiled Information Based Performance Prediction in MOOCs
This paper mainly focuses on the students' accomplishment behaviors in programming assignments and compiled information from programming assignments.
A Collaborative Approach to Angel and Venture Capital Investment Recommendations
With a similar model, the same dataset was used to generate investor recommendations for companies undergoing fundraising, which achieved highest prediction accuracy of 11. 1%.
