Search Results for author:
Found 26 papers, 19 papers with code
Naive Bayes-based Context Extension for Large Language Models
However, conventional In-Context Learning (ICL) approaches are often impeded by length limitations of transformer architecture, which pose challenges when attempting to effectively integrate supervision from a substantial number of demonstration examples.
VisionLLaMA: A Unified LLaMA Interface for Vision Tasks
Large language models are built on top of a transformer-based architecture to process textual inputs.
Elucidating the Exposure Bias in Diffusion Models
In this paper, we systematically investigate the exposure bias problem in diffusion models by first analytically modelling the sampling distribution, based on which we then attribute the prediction error at each sampling step as the root cause of the exposure bias issue.
Ranked #9 on
Image Generation
on CIFAR-10
Rank-Aware Negative Training for Semi-Supervised Text Classification
To alleviate the noisy information, we adapt a reasoning with uncertainty-based approach to rank the unlabeled texts based on the evidential support received from the labeled texts.
Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition
The ultimate goal is to enable a global view that considers the beginning and the end positions to predict the entity.
ZLPR: A Novel Loss for Multi-label Classification
To support the application of deep learning in multi-label classification (MLC) tasks, we propose the ZLPR (zero-bounded log-sum-exp \& pairwise rank-based) loss in this paper.
BERT-ASC: Implicit Aspect Representation Learning through Auxiliary-Sentence Construction for Sentiment Analysis
Unfortunately, the aspect is often expressed implicitly through a set of representatives and thus renders implicit mapping process unattainable unless sufficient labeled examples are available.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Sparse-softmax: A Simpler and Faster Alternative Softmax Transformation
The softmax function is widely used in artificial neural networks for the multiclass classification problems, where the softmax transformation enforces the output to be positive and sum to one, and the corresponding loss function allows to use maximum likelihood principle to optimize the model.
ZARTS: On Zero-order Optimization for Neural Architecture Search
Also, we search on the search space of DARTS to compare with peer methods, and our discovered architecture achieves 97. 54% accuracy on CIFAR-10 and 75. 7% top-1 accuracy on ImageNet, which are state-of-the-art performance.
BioCopy: A Plug-And-Play Span Copy Mechanism in Seq2Seq Models
Copy mechanisms explicitly obtain unchanged tokens from the source (input) sequence to generate the target (output) sequence under the neural seq2seq framework.
Multiattention network for semantic segmentation of fine-resolution remote sensing images
A novel attention mechanism of kernel attention with linear complexity is proposed to alleviate the large computational demand in attention.
Ranked #7 on
Semantic Segmentation
on ISPRS Vaihingen
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks
Based on them, we design MinGE to directly calculate the ideal node embedding dimension for any graph.
RoFormer: Enhanced Transformer with Rotary Position Embedding
Then, we propose a novel method named Rotary Position Embedding(RoPE) to effectively leverage the positional information.
Ranked #1 on
Semantic Text Matching
on CAIL2019-SCM - test
Whitening Sentence Representations for Better Semantics and Faster Retrieval
Therefore, some attempts of boosting the isotropy of sentence distribution, such as flow-based model, have been applied to sentence representations and achieved some improvement.
Multi-stage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images
The attention mechanism can refine the extracted feature maps and boost the classification performance of the deep network, which has become an essential technique in computer vision and natural language processing.
Multi-Attention-Network for Semantic Segmentation of Fine Resolution Remote Sensing Images
A novel attention mechanism of kernel attention with linear complexity is proposed to alleviate the large computational demand in attention.
Linear Attention Mechanism: An Efficient Attention for Semantic Segmentation
In this paper, to remedy this deficiency, we propose a Linear Attention Mechanism which is approximate to dot-product attention with much less memory and computational costs.
A Batch Normalized Inference Network Keeps the KL Vanishing Away
Variational Autoencoder (VAE) is widely used as a generative model to approximate a model's posterior on latent variables by combining the amortized variational inference and deep neural networks.
A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
Extracting relational triples from unstructured text is crucial for large-scale knowledge graph construction.
Ranked #5 on
Relation Extraction
on NYT11-HRL
O-GAN: Extremely Concise Approach for Auto-Encoding Generative Adversarial Networks
We decompose the network of discriminator orthogonally and add an extra loss into the objective of common GANs, which can enforce discriminator become an effective encoder.
Artist Style Transfer Via Quadratic Potential
In this paper we address the problem of artist style transfer where the painting style of a given artist is applied on a real world photograph.
Evaluating Generalization Ability of Convolutional Neural Networks and Capsule Networks for Image Classification via Top-2 Classification
In this paper, we propose a new image classification task called Top-2 classification to evaluate the generalization ability of CNNs and CapsNets.
GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraint
A significant improvement is WGAN, with the help of 1-Lipschitz constraint on discriminator to prevent from gradient vanishing.
Training Generative Adversarial Networks Via Turing Test
In this article, we introduce a new mode for training Generative Adversarial Networks (GANs).
f-VAEs: Improve VAEs with Conditional Flows
In this paper, we integrate VAEs and flow-based generative models successfully and get f-VAEs.
Variational Inference: A Unified Framework of Generative Models and Some Revelations
We reinterpreting the variational inference in a new perspective.
