Search Results for author:
Found 37 papers, 10 papers with code
Understanding Individual Agent Importance in Multi-Agent System via Counterfactual Reasoning
Inspired by counterfactual reasoning, a larger change in reward caused by the randomized action of agent indicates its higher importance.
Modality-Independent Graph Neural Networks with Global Transformers for Multimodal Recommendation
Our results indicate that the optimal $K$ for certain modalities on specific datasets can be as low as 1 or 2, which may restrict the GNNs' capacity to capture global information.
Ranked #1 on
Multi-modal Recommendation
on Amazon Clothing
SparseLGS: Sparse View Language Embedded Gaussian Splatting
In this work, we propose SparseLGS to address the challenge of 3D scene understanding with pose-free and sparse view input images.
Online Multi-Label Classification under Noisy and Changing Label Distribution
Motivated by this, we propose an online multi-label classification algorithm under Noisy and Changing Label Distribution (NCLD).
TG-LMM: Enhancing Medical Image Segmentation Accuracy through Text-Guided Large Multi-Modal Model
We propose TG-LMM (Text-Guided Large Multi-Modal Model), a novel approach that leverages textual descriptions of organs to enhance segmentation accuracy in medical images.
Neural Graph Matching for Video Retrieval in Large-Scale Video-driven E-commerce
In this paper, we first leverage the dual graph to model the co-existing of user-video and user-item interactions in video-driven e-commerce and innovatively reduce user preference understanding to a graph matching problem.
Quality assurance of organs-at-risk delineation in radiotherapy
Automatic segmentation can be used to reduce the physician workload and improve the consistency.
A hybrid iterative method based on MIONet for PDEs: Theory and numerical examples
We propose a hybrid iterative method based on MIONet for PDEs, which combines the traditional numerical iterative solver and the recent powerful machine learning method of neural operator, and further systematically analyze its theoretical properties, including the convergence condition, the spectral behavior, as well as the convergence rate, in terms of the errors of the discretization and the model inference.
Second-order Confidence Network for Early Classification of Time Series
Existing approaches mainly utilize heuristic stopping rules to capture stopping signals from the prediction results of time series classifiers.
Ranked #1 on
Early Classification
on ECG200
The NeurIPS 2022 Neural MMO Challenge: A Massively Multiagent Competition with Specialization and Trade
In this paper, we present the results of the NeurIPS-2022 Neural MMO Challenge, which attracted 500 participants and received over 1, 600 submissions.
Robust NL-to-Cypher Translation for KBQA: Harnessing Large Language Model with Chain of Prompts
Knowledge Base Question Answering (KBQA) is a significant task in natural language processing, aiming to retrieve answers from structured knowledge bases in response to natural language questions.
Efficient Heterogeneous Graph Learning via Random Projection
To achieve low information loss, we introduce a Relation-wise Neighbor Collection component with an Even-odd Propagation Scheme, which aims to collect information from neighbors in a finer-grained way.
Ranked #1 on
Heterogeneous Node Classification
on OAG-L1-Field
One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems
We expect that by mixing the user's behaviors across different domains, we can exploit the common knowledge encoded in the pre-trained language model to alleviate the problems of data sparsity and cold start problems.
Efficient Model Adaptation for Continual Learning at the Edge
This paper presents the Encoder-Adaptor-Reconfigurator (EAR) framework for efficient continual learning under domain shifts.
IntrinsicNGP: Intrinsic Coordinate based Hash Encoding for Human NeRF
To achieve this target, we introduce a continuous and optimizable intrinsic coordinate rather than the original explicit Euclidean coordinate in the hash encoding module of instant-NGP.
Experimental observation on a low-rank tensor model for eigenvalue problems
Here we utilize a low-rank tensor model (LTM) as a function approximator, combined with the gradient descent method, to solve eigenvalue problems including the Laplacian operator and the harmonic oscillator.
Tracking Multiple Deformable Objects in Egocentric Videos
DETracker outperforms existing state-of-the-art method on the DogThruGlasses dataset and YouTube-Hand dataset.
SelfNeRF: Fast Training NeRF for Human from Monocular Self-rotating Video
Extensive experimental results on several different datasets demonstrate the effectiveness and efficiency of SelfNeRF to challenging monocular videos.
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators
In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at the edge leveraging non-MAC (free of floating-point MultiplyACcumulate operations) deep neural nets (DNN) combined with hyperdimensional (HD) computing accelerators.
MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering
Based on a Markov process that trades off two types of distances, we present Markov Graph Diffusion Collaborative Filtering (MGDCF) to generalize some state-of-the-art GNN-based CF models.
Ranked #4 on
Multi-modal Recommendation
on Amazon Sports
DRTAM: Dual Rank-1 Tensor Attention Module
Recently, attention mechanisms have been extensively investigated in computer vision, but few of them show excellent performance on both large and mobile networks.
Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph Learning
Recently the field has advanced from local propagation schemes that focus on local neighbors towards extended propagation schemes that can directly deal with extended neighbors consisting of both local and high-order neighbors.
GRecX: An Efficient and Unified Benchmark for GNN-based Recommendation
In this paper, we present GRecX, an open-source TensorFlow framework for benchmarking GNN-based recommendation models in an efficient and unified way.
Adaptive Convolutions with Per-pixel Dynamic Filter Atom
Applying feature dependent network weights have been proved to be effective in many fields.
VTLayout: Fusion of Visual and Text Features for Document Layout Analysis
In the second stage, the deep visual, shallow visual, and text features are extracted for fusion to identify the category blocks of documents.
Feature-Align Network with Knowledge Distillation for Efficient Denoising
(2) A novel Feature Matching Loss that allows knowledge distillation from large denoising networks in the form of a perceptual content loss.
Efficient Graph Deep Learning in TensorFlow with tf_geometric
We introduce tf_geometric, an efficient and friendly library for graph deep learning, which is compatible with both TensorFlow 1. x and 2. x.
Super strong paramagnetism of aromatic peptides adsorbed with monovalent cations
We experimentally demonstrated that the AYFFF self-assemblies adsorbed with various monovalent cations (Na+, K+, and Li+) show unexpectedly super strong paramagnetism.
Biological Physics
Fault Detection for Covered Conductors With High-Frequency Voltage Signals: From Local Patterns to Global Features
The detection and characterization of partial discharge (PD) are crucial for the insulation diagnosis of overhead lines with covered conductors.
PEL-BERT: A Joint Model for Protocol Entity Linking
Pre-trained models such as BERT are widely used in NLP tasks and are fine-tuned to improve the performance of various NLP tasks consistently.
Scale- and Context-Aware Convolutional Non-intrusive Load Monitoring
Non-intrusive load monitoring addresses the challenging task of decomposing the aggregate signal of a household's electricity consumption into appliance-level data without installing dedicated meters.
Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks
This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks.
Convolutional Sequence to Sequence Non-intrusive Load Monitoring
A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this paper.
Short-term Load Forecasting with Deep Residual Networks
We present in this paper a model for forecasting short-term power loads based on deep residual networks.
Locally Non-rigid Registration for Mobile HDR Photography
Image registration for stack-based HDR photography is challenging.
HDR Deghosting: How to Deal with Saturation?
We present a novel method for aligning images in an HDR (high-dynamic-range) image stack to produce a new exposure stack where all the images are aligned and appear as if they were taken simultaneously, even in the case of highly dynamic scenes.
