Search Results for author:
Found 35 papers, 13 papers with code
Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View
In open domain table-to-text generation, we notice that the unfaithful generation usually contains hallucinated content which can not be aligned to any input table record.
Traj-LO: In Defense of LiDAR-Only Odometry Using an Effective Continuous-Time Trajectory
Therefore, our proposed Traj-LO approach tries to recover the spatial-temporal consistent movement of LiDAR by tightly coupling the geometric information from LiDAR points and kinematic constraints from trajectory smoothness.
Traj-LIO: A Resilient Multi-LiDAR Multi-IMU State Estimator Through Sparse Gaussian Process
Nowadays, sensor suits have been equipped with redundant LiDARs and IMUs to mitigate the risks associated with sensor failure.
What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation
kNN-MT presents a new paradigm for domain adaptation by building an external datastore, which usually saves all target language token occurrences in the parallel corpus.
Adaptive Nearest Neighbor Machine Translation
On four benchmark machine translation datasets, we demonstrate that the proposed method is able to effectively filter out the noises in retrieval results and significantly outperforms the vanilla kNN-MT model.
ECTLO: Effective Continuous-time Odometry Using Range Image for LiDAR with Small FoV
Prism-based LiDARs are more compact and cheaper than the conventional mechanical multi-line spinning LiDARs, which have become increasingly popular in robotics, recently.
Towards Data-centric Graph Machine Learning: Review and Outlook
Data-centric AI, with its primary focus on the collection, management, and utilization of data to drive AI models and applications, has attracted increasing attention in recent years.
Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
Specifically, SFGC contains two collaborative components: (1) a training trajectory meta-matching scheme for effectively synthesizing small-scale graph-free data; (2) a graph neural feature score metric for dynamically evaluating the quality of the condensed data.
Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation
Recently, $k$NN-MT has shown the promising capability of directly incorporating the pre-trained neural machine translation (NMT) model with domain-specific token-level $k$-nearest-neighbor ($k$NN) retrieval to achieve domain adaptation without retraining.
Online GNN Evaluation Under Test-time Graph Distribution Shifts
This enables the effective evaluation of the well-trained GNNs' ability to capture test node semantics and structural representations, making it an expressive metric for estimating the generalization error in online GNN evaluation.
Toward Unified Controllable Text Generation via Regular Expression Instruction
Controllable text generation is a fundamental aspect of natural language generation, with numerous methods proposed for different constraint types.
An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference
The prior work on natural language inference (NLI) debiasing mainly targets at one or few known biases while not necessarily making the models more robust.
Executing Natural Language-Described Algorithms with Large Language Models: An Investigation
In this paper, we seek to examine the capacity of present-day LLMs to comprehend and execute algorithms outlined in natural language.
Character-Based Text Classification using Top Down Semantic Model for Sentence Representation
Despite the success of deep learning on many fronts especially image and speech, its application in text classification often is still not as good as a simple linear SVM on n-gram TF-IDF representation especially for smaller datasets.
Application of Deep Neural Network in Estimation of the Weld Bead Parameters
We present a deep learning approach to estimation of the bead parameters in welding tasks.
Feature Learning with Gaussian Restricted Boltzmann Machine for Robust Speech Recognition
In this paper, we first present a new variant of Gaussian restricted Boltzmann machine (GRBM) called multivariate Gaussian restricted Boltzmann machine (MGRBM), with its definition and learning algorithm.
A Survey of Deep Facial Attribute Analysis
Deep learning based facial attribute analysis consists of two basic sub-issues: facial attribute estimation (FAE), which recognizes whether facial attributes are present in given images, and facial attribute manipulation (FAM), which synthesizes or removes desired facial attributes.
Subtopic-driven Multi-Document Summarization
In multi-document summarization, a set of documents to be summarized is assumed to be on the same topic, known as the underlying topic in this paper.
Two-dimensional Multi-fiber Spectrum Image Correction Based on Machine Learning Techniques
At the same time, the spectrum extraction results before and after calibration are compared, results show the characteristics of the extracted one-dimensional waveform are more close to an ideal optics system, and the PSF of the corrected object spectrum image estimated by the blind deconvolution method is nearly central symmetry, which indicates that our proposed method can significantly reduce the complexity of spectrum extraction and improve extraction accuracy.
HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference
Many recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by merely looking at the hypothesis while completely ignoring the premise.
A Survey of Location Prediction on Twitter
Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations.
Neural Network Compression for Noisy Storage Devices
Compression and efficient storage of neural network (NN) parameters is critical for applications that run on resource-constrained devices.
Efficient LiDAR Odometry for Autonomous Driving
LiDAR odometry plays an important role in self-localization and mapping for autonomous navigation, which is usually treated as a scan registration problem.
Robust Physical-World Attacks on Face Recognition
Face recognition has been greatly facilitated by the development of deep neural networks (DNNs) and has been widely applied to many safety-critical applications.
Noisy Neural Network Compression for Analog Storage Devices
Efficient compression and storage of neural network (NN) parameters is critical for resource-constrained, downstream machine learning applications.
Graph Neural Networks for Graphs with Heterophily: A Survey
In the end, we point out the potential directions to advance and stimulate more future research and applications on heterophilic graph learning with GNNs.
Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis
At the second stage, a self-supervised contrastive learning is designed for the improvement of the distilled unimodal representations after cross-modal interaction.
DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog
Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or continuous integration scenarios.
Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs
Therefore, in this paper, we propose a novel automated graph neural network on heterophilic graphs, namely Auto-HeG, to automatically build heterophilic GNN models with expressive learning abilities.
DialogVCS: Robust Natural Language Understanding in Dialogue System Upgrade
In the constant updates of the product dialogue systems, we need to retrain the natural language understanding (NLU) model as new data from the real users would be merged into the existent data accumulated in the last updates.
Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model
Constructing commonsense knowledge graphs (CKGs) has attracted wide research attention due to its significant importance in cognitive intelligence.
Graph Neural Architecture Search with GPT-4
In this paper, we integrate GPT-4 into GNAS and propose a new GPT-4 based Graph Neural Architecture Search method (GPT4GNAS for short).
ViP-Mixer: A Convolutional Mixer for Video Prediction
Video prediction aims to predict future frames from a video's previous content.
Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
Graph learning plays a pivotal role and has gained significant attention in various application scenarios, from social network analysis to recommendation systems, for its effectiveness in modeling complex data relations represented by graph structural data.
OVEL: Large Language Model as Memory Manager for Online Video Entity Linking
In this paper, we propose a task called Online Video Entity Linking OVEL, aiming to establish connections between mentions in online videos and a knowledge base with high accuracy and timeliness.
