Search Results for author:
Found 33 papers, 8 papers with code
Aspect-based Sentiment Analysis as Machine Reading Comprehension
Existing studies typically handle aspect-based sentiment analysis by stacking multiple neural modules, which inevitably result in severe error propagation.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Nested Named Entity Recognition as Corpus Aware Holistic Structure Parsing
Thus in order to address this mismatch, this work models the full nested NEs in a sentence as a holistic structure, then we propose a holistic structure parsing algorithm to disclose the entire NEs once for all.
Modeling Semantics and Pragmatics of Spatial Prepositions via Hierarchical Common-Sense Primitives
Understanding spatial expressions and using them appropriately is necessary for seamless and natural human-machine interaction.
Lagrangian Motion Fields for Long-term Motion Generation
This method seamlessly integrates static spatial information with interpretable temporal dynamics, transcending the limitations of existing network architectures and motion sequence content types.
Nothing in Excess: Mitigating the Exaggerated Safety for LLMs via Safety-Conscious Activation Steering
Safety alignment is indispensable for Large language models (LLMs) to defend threats from malicious instructions.
BatGPT-Chem: A Foundation Large Model For Retrosynthesis Prediction
Retrosynthesis analysis is pivotal yet challenging in drug discovery and organic chemistry.
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization
Hyperbolic representations have shown remarkable efficacy in modeling inherent hierarchies and complexities within data structures.
Sparse Matrix Coding for URLLC
Sparse Vector Coding (SVC) has long been considered an encoding method that meets the URLLC QOS requirements.
$ν$-DBA: Neural Implicit Dense Bundle Adjustment Enables Image-Only Driving Scene Reconstruction
The joint optimization of the sensor trajectory and 3D map is a crucial characteristic of bundle adjustment (BA), essential for autonomous driving.
Drag Your Noise: Interactive Point-based Editing via Diffusion Semantic Propagation
Point-based interactive editing serves as an essential tool to complement the controllability of existing generative models.
Hypertext Entity Extraction in Webpage
Webpage entity extraction is a fundamental natural language processing task in both research and applications.
Head-wise Shareable Attention for Large Language Models
In this paper, we present a perspective on $\textit{$\textbf{head-wise shareable attention for large language models}$}$.
LaCo: Large Language Model Pruning via Layer Collapse
Large language models (LLMs) based on transformer are witnessing a notable trend of size expansion, which brings considerable costs to both model training and inference.
FreDF: Learning to Forecast in Frequency Domain
Time series modeling is uniquely challenged by the presence of autocorrelation in both historical and label sequences.
GRAM: An Interpretable Approach for Graph Anomaly Detection using Gradient Attention Maps
Detecting unusual patterns in graph data is a crucial task in data mining.
Monotone Generative Modeling via a Gromov-Monge Embedding
Generative adversarial networks (GANs) are popular for generative tasks; however, they often require careful architecture selection, extensive empirical tuning, and are prone to mode collapse.
DORec: Decomposed Object Reconstruction and Segmentation Utilizing 2D Self-Supervised Features
Recovering 3D geometry and textures of individual objects is crucial for many robotics applications, such as manipulation, pose estimation, and autonomous driving.
AutoHall: Automated Hallucination Dataset Generation for Large Language Models
While Large language models (LLMs) have garnered widespread applications across various domains due to their powerful language understanding and generation capabilities, the detection of non-factual or hallucinatory content generated by LLMs remains scarce.
BatGPT: A Bidirectional Autoregessive Talker from Generative Pre-trained Transformer
BatGPT is a large-scale language model designed and trained jointly by Wuhan University and Shanghai Jiao Tong University.
CMMLU: Measuring massive multitask language understanding in Chinese
As the capabilities of large language models (LLMs) continue to advance, evaluating their performance becomes increasingly crucial and challenging.
RefGPT: Dialogue Generation of GPT, by GPT, and for GPT
Therefore, we propose a method called RefGPT to generate enormous truthful and customized dialogues without worrying about factual errors caused by the model hallucination.
Attack Named Entity Recognition by Entity Boundary Interference
This is due to the fine-grained nature of NER, as even minor word changes in the sentence can result in the emergence or mutation of any entities, resulting in invalid adversarial examples.
UrbanGIRAFFE: Representing Urban Scenes as Compositional Generative Neural Feature Fields
Generating photorealistic images with controllable camera pose and scene contents is essential for many applications including AR/VR and simulation.
Learning Transformation-Predictive Representations for Detection and Description of Local Features
The task of key-points detection and description is to estimate the stable location and discriminative representation of local features, which is essential for image matching.
Open-Set Object Detection Using Classification-free Object Proposal and Instance-level Contrastive Learning
To disambiguate unknown objects and background in the first subtask, we propose to use classification-free region proposal network (CF-RPN) which estimates the objectness score of each region purely using cues from object's location and shape preventing overfitting to the training categories.
Graph Neural Network Based Node Deployment for Throughput Enhancement
Besides, we show that an expressive GNN has the capacity to approximate both the function value and the gradients of a multivariate permutation-invariant function, as a theoretic support to the proposed method.
Nested Named Entity Recognition as Holistic Structure Parsing
Thus in order to address this mismatch, this work models the full nested NEs in a sentence as a holistic structure, then we propose a holistic structure parsing algorithm to disclose the entire NEs once for all.
Rethinking Controllable Variational Autoencoders
It has shown success in a variety of applications, such as image generation, disentangled representation learning, and language modeling.
A Unified Knowledge Distillation Framework for Deep Directed Graphical Models
Moreover, our method achieves good performance for discriminative deep DGMs compression.
Improving unsupervised anomaly localization by applying multi-scale memories to autoencoders
Autoencoder and its variants have been widely applicated in anomaly detection. The previous work memory-augmented deep autoencoder proposed memorizing normality to detect anomaly, however it neglects the feature discrepancy between different resolution scales, therefore we introduce multi-scale memories to record scale-specific features and multi-scale attention fuser between the encoding and decoding module of the autoencoder for anomaly detection, namely MMAE. MMAE updates slots at corresponding resolution scale as prototype features during unsupervised learning.
Attend to the Difference: Cross-Modality Person Re-identification via Contrastive Correlation
The problem of cross-modality person re-identification has been receiving increasing attention recently, due to its practical significance.
Cross-Modality Person Re-identification
Person Re-Identification
Person Re-identification in Aerial Imagery
Our method can learn a discriminative and compact feature representation for ReID in aerial imagery and can be trained in an end-to-end fashion efficiently.
Vehicle Re-identification in Aerial Imagery: Dataset and Approach
It is capable of explicitly detecting discriminative parts for each specific vehicle and significantly outperforms the evaluated baselines and state-of-the-art vehicle ReID approaches.
