Search Results for author:
Found 36 papers, 14 papers with code
Scalable Weibull Graph Attention Autoencoder for Modeling Document Networks
Although existing variational graph autoencoders (VGAEs) have been widely used for modeling and generating graph-structured data, most of them are still not flexible enough to approximate the sparse and skewed latent node representations, especially those of document relational networks (DRNs) with discrete observations.
TsCA: On the Semantic Consistency Alignment via Conditional Transport for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to recognize novel state-object compositions by leveraging the shared knowledge of their primitive components.
Instruction Tuning-free Visual Token Complement for Multimodal LLMs
However, current research is inherently constrained by challenges such as the need for high-quality instruction pairs and the loss of visual information in image-to-text training objectives.
2M-NER: Contrastive Learning for Multilingual and Multimodal NER with Language and Modal Fusion
To tackle this challenging MMNER task on the dataset, we introduce a new model called 2M-NER, which aligns the text and image representations using contrastive learning and integrates a multimodal collaboration module to effectively depict the interactions between the two modalities.
BuDDIE: A Business Document Dataset for Multi-task Information Extraction
Several datasets exist for research on specific tasks of VRDU such as document classification (DC), key entity extraction (KEE), entity linking, visual question answering (VQA), inter alia.
Large Language Models as Financial Data Annotators: A Study on Effectiveness and Efficiency
Collecting labeled datasets in finance is challenging due to scarcity of domain experts and higher cost of employing them.
MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
However, transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks.
Aerial Scene Classification
Building change detection for remote sensing images
+13
DocGraphLM: Documental Graph Language Model for Information Extraction
Advances in Visually Rich Document Understanding (VrDU) have enabled information extraction and question answering over documents with complex layouts.
DocLLM: A layout-aware generative language model for multimodal document understanding
Enterprise documents such as forms, invoices, receipts, reports, contracts, and other similar records, often carry rich semantics at the intersection of textual and spatial modalities.
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection
First, instead of learning the continuous representations, we preserve the typical normal patterns as discrete iconic prototypes, and confirm the importance of Vector Quantization in preventing the model from falling into the shortcut.
Ranked #1 on
Multi-class Anomaly Detection
on MVTec AD
PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification
We find that by formulating the multi-label classification as a CT problem, we can exploit the interactions between the image and label efficiently by minimizing the bidirectional CT cost.
REFinD: Relation Extraction Financial Dataset
A number of datasets for Relation Extraction (RE) have been created to aide downstream tasks such as information retrieval, semantic search, question answering and textual entailment.
Dual Memory Aggregation Network for Event-Based Object Detection with Learnable Representation
To fully exploit information with event streams to detect objects, a dual-memory aggregation network (DMANet) is proposed to leverage both long and short memory along event streams to aggregate effective information for object detection.
Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models
For downstream applications of vision-language pre-trained models, there has been significant interest in constructing effective prompts.
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing
Zero-shot capability has been considered as a new revolution of deep learning, letting machines work on tasks without curated training data.
HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding
With the tree-likeness property of hyperbolic space, the underlying semantic hierarchy among words and topics can be better exploited to mine more interpretable topics.
Knowledge-Aware Bayesian Deep Topic Model
We propose a Bayesian generative model for incorporating prior domain knowledge into hierarchical topic modeling.
Ordinal Graph Gamma Belief Network for Social Recommender Systems
To build recommender systems that not only consider user-item interactions represented as ordinal variables, but also exploit the social network describing the relationships between the users, we develop a hierarchical Bayesian model termed ordinal graph factor analysis (OGFA), which jointly models user-item and user-user interactions.
Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings
This paper introduces a new topic-modeling framework where each document is viewed as a set of word embedding vectors and each topic is modeled as an embedding vector in the same embedding space.
Short Range Correlation Transformer for Occluded Person Re-Identification
However, the performance of vision transformer in extracting local features is inferior to that of convolutional neural network.
TopicNet: Semantic Graph-Guided Topic Discovery
Existing deep hierarchical topic models are able to extract semantically meaningful topics from a text corpus in an unsupervised manner and automatically organize them into a topic hierarchy.
Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network
However, they often assume in the prior that the topics at each layer are independently drawn from the Dirichlet distribution, ignoring the dependencies between the topics both at the same layer and across different layers.
Semantic Representation and Inference for NLP
This thesis investigates the use of deep learning for novel semantic representation and inference, and makes contributions in the following three areas: creating training data, improving semantic representations and extending inference learning.
Image Inpainting with Edge-guided Learnable Bidirectional Attention Maps
In this paper, we present an edge-guided learnable bidirectional attention map (Edge-LBAM) for improving image inpainting of irregular holes with several distinct merits.
Structural block driven - enhanced convolutional neural representation for relation extraction
In this paper, we propose a novel lightweight relation extraction approach of structural block driven - convolutional neural learning.
DynaComm: Accelerating Distributed CNN Training between Edges and Clouds through Dynamic Communication Scheduling
To reduce uploading bandwidth and address privacy concerns, deep learning at the network edge has been an emerging topic.
Topic-aware Contextualized Transformers
Moving beyond conventional Transformers that ignore longer-range word dependencies and contextualize their word representations at the segment level, the proposed method not only captures global semantic coherence of all segments and global word concurrence patterns, but also enriches the representation of each token by adapting it to its local context, which is not limited to the segment it resides in and can be flexibly defined according to the task.
Multi-Head Self-Attention with Role-Guided Masks
The state of the art in learning meaningful semantic representations of words is the Transformer model and its attention mechanisms.
Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network
To analyze a collection of interconnected documents, relational topic models (RTMs) have been developed to describe both the link structure and document content, exploring their underlying relationships via a single-layer latent representation with limited expressive capability.
Denmark's Participation in the Search Engine TREC COVID-19 Challenge: Lessons Learned about Searching for Precise Biomedical Scientific Information on COVID-19
This report describes the participation of two Danish universities, University of Copenhagen and Aalborg University, in the international search engine competition on COVID-19 (the 2020 TREC-COVID Challenge) organised by the U. S. National Institute of Standards and Technology (NIST) and its Text Retrieval Conference (TREC) division.
QGAN: Quantize Generative Adversarial Networks to Extreme low-bits
Despite the success in model reduction of convolutional neural networks (CNNs), neural network quantization methods have not yet been studied on GANs, which are mainly faced with the issues of both the effectiveness of quantization algorithms and the instability of training GAN models.
MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims
We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification.
Contextual Compositionality Detection with External Knowledge Bases andWord Embeddings
In this paper, we operationalize the viewpoint that compositionality is contextual rather than deterministic, i. e., that whether a phrase is compositional or non-compositional depends on its context.
QGAN: Quantized Generative Adversarial Networks
The intensive computation and memory requirements of generative adversarial neural networks (GANs) hinder its real-world deployment on edge devices such as smartphones.
HitNet: Hybrid Ternary Recurrent Neural Network
For example, we improve the perplexity per word (PPW) of a ternary LSTM on Penn Tree Bank (PTB) corpus from 126 (the state-of-the-art result to the best of our knowledge) to 110. 3 with a full precision model in 97. 2, and a ternary GRU from 142 to 113. 5 with a full precision model in 102. 7.
Computation Error Analysis of Block Floating Point Arithmetic Oriented Convolution Neural Network Accelerator Design
The heavy burdens of computation and off-chip traffic impede deploying the large scale convolution neural network on embedded platforms.
