Search Results for author:
Found 30 papers, 10 papers with code
ManuSearch: Democratizing Deep Search in Large Language Models with a Transparent and Open Multi-Agent Framework
Recent advances in web-augmented large language models (LLMs) have exhibited strong performance in complex reasoning tasks, yet these capabilities are mostly locked in proprietary systems with opaque architectures.
Dynamic Regularized CBDT: Variance-Calibrated Causal Boosting for Interpretable Heterogeneous Treatment Effects
Heterogeneous treatment effect estimation in high-stakes applications demands models that simultaneously optimize precision, interpretability, and calibration.
Deep Learning for Time Series Forecasting: A Survey
However, existing surveys have not provided a unified summary of the wide range of model architectures in this field, nor have they given detailed summaries of works in feature extraction and datasets.
MaskGWM: A Generalizable Driving World Model with Video Mask Reconstruction
The prevailing driving world model mainly build on video prediction model.
Music Genre Classification: Ensemble Learning with Subcomponents-level Attention
Music Genre Classification is one of the most popular topics in the fields of Music Information Retrieval (MIR) and digital signal processing.
UniMLVG: Unified Framework for Multi-view Long Video Generation with Comprehensive Control Capabilities for Autonomous Driving
The creation of diverse and realistic driving scenarios has become essential to enhance perception and planning capabilities of the autonomous driving system.
LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios
As Large Language Models (LLMs) evolve in natural language processing (NLP), their ability to stably follow instructions in long-context inputs has become critical for real-world applications.
Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility Data with Large Language Models
Drawing inspiration from the exceptional semantic understanding and contextual information processing capabilities of large language models (LLMs) across various domains, we present Mobility-LLM, a novel framework that leverages LLMs to analyze check-in sequences for multiple tasks.
Optimized Biomedical Question-Answering Services with LLM and Multi-BERT Integration
We present a refined approach to biomedical question-answering (QA) services by integrating large language models (LLMs) with Multi-BERT configurations.
Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning
In this work, we introduce Denoising World Model Learning (DWL), an end-to-end reinforcement learning framework for humanoid locomotion control, which demonstrates the world's first humanoid robot to master real-world challenging terrains such as snowy and inclined land in the wild, up and down stairs, and extremely uneven terrains.
PTrajM: Efficient and Semantic-rich Trajectory Learning with Pretrained Trajectory-Mamba
To better utilize vehicle trajectories, it is essential to develop a trajectory learning approach that can effectively and efficiently extract rich semantic information, including movement behavior and travel purposes, to support accurate downstream applications.
Multi-Garment Customized Model Generation
This paper introduces Multi-Garment Customized Model Generation, a unified framework based on Latent Diffusion Models (LDMs) aimed at addressing the unexplored task of synthesizing images with free combinations of multiple pieces of clothing.
VP-LLM: Text-Driven 3D Volume Completion with Large Language Models through Patchification
Recent conditional 3D completion works have mainly relied on CLIP or BERT to encode textual information, which cannot support complex instruction.
Neural Radiance Field in Autonomous Driving: A Survey
To the best of our knowledge, this is the first survey specifically focused on the applications of NeRF in the Autonomous Driving domain.
DepthMOT: Depth Cues Lead to a Strong Multi-Object Tracker
Inspired by this, even though the bounding boxes of objects are close on the camera plane, we can differentiate them in the depth dimension, thereby establishing a 3D perception of the objects.
DiffYOLO: Object Detection for Anti-Noise via YOLO and Diffusion Models
Object detection models represented by YOLO series have been widely used and have achieved great results on the high quality datasets, but not all the working conditions are ideal.
SANeRF-HQ: Segment Anything for NeRF in High Quality
Recently, the Segment Anything Model (SAM) has showcased remarkable capabilities of zero-shot segmentation, while NeRF (Neural Radiance Fields) has gained popularity as a method for various 3D problems beyond novel view synthesis.
centroIDA: Cross-Domain Class Discrepancy Minimization Based on Accumulative Class-Centroids for Imbalanced Domain Adaptation
Unsupervised Domain Adaptation (UDA) approaches address the covariate shift problem by minimizing the distribution discrepancy between the source and target domains, assuming that the label distribution is invariant across domains.
Building a digital twin of EDFA: a grey-box modeling approach
To enable intelligent and self-driving optical networks, high-accuracy physical layer models are required.
On Uni-Modal Feature Learning in Supervised Multi-Modal Learning
We abstract the features (i. e. learned representations) of multi-modal data into 1) uni-modal features, which can be learned from uni-modal training, and 2) paired features, which can only be learned from cross-modal interactions.
Instance Neural Radiance Field
This paper presents one of the first learning-based NeRF 3D instance segmentation pipelines, dubbed as {\bf \inerflong}, or \inerf.
ONeRF: Unsupervised 3D Object Segmentation from Multiple Views
We present ONeRF, a method that automatically segments and reconstructs object instances in 3D from multi-view RGB images without any additional manual annotations.
NeRF-RPN: A general framework for object detection in NeRFs
This paper presents the first significant object detection framework, NeRF-RPN, which directly operates on NeRF.
A Grey-box Launch-profile Aware Model for C+L Band Raman Amplification
Based on the physical features of Raman amplification, we propose a three-step modelling scheme based on neural networks (NN) and linear regression.
Physics-informed EDFA Gain Model Based on Active Learning
We propose a physics-informed EDFA gain model based on the active learning method.
Confidence-aware Self-Semantic Distillation on Knowledge Graph Embedding
Knowledge Graph Embedding (KGE), which projects entities and relations into continuous vector spaces, has garnered significant attention.
Learning Visual Styles from Audio-Visual Associations
Our model learns to manipulate the texture of a scene to match a sound, a problem we term audio-driven image stylization.
Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension
We present Native Chinese Reader (NCR), a new machine reading comprehension (MRC) dataset with particularly long articles in both modern and classical Chinese.
Modality Laziness: Everybody's Business is Nobody's Business
We name this problem of multi-modal training, \emph{Modality Laziness}.
Improving Multi-Modal Learning with Uni-Modal Teachers
We name this problem Modality Failure, and hypothesize that the imbalance of modalities and the implicit bias of common objectives in fusion method prevent encoders of each modality from sufficient feature learning.
Ranked #72 on
Semantic Segmentation
on NYU Depth v2
