Search Results for author:
Found 17 papers, 12 papers with code
Higher Layers Need More LoRA Experts
In this paper, we introduce a novel parameter-efficient MoE method, \textit{\textbf{M}oE-L\textbf{o}RA with \textbf{L}ayer-wise Expert \textbf{A}llocation (MoLA)} for Transformer-based models, where each model layer has the flexibility to employ a varying number of LoRA experts.
How to Configure Good In-Context Sequence for Visual Question Answering
Inspired by the success of Large Language Models in dealing with new tasks via In-Context Learning (ICL) in NLP, researchers have also developed Large Vision-Language Models (LVLMs) with ICL capabilities.
Improving Representation Learning for Histopathologic Images with Cluster Constraints
This framework aims for transferable representation learning and semantically meaningful clustering by synergizing invariance loss and clustering loss in WSI analysis.
Bootstrapping Vision-Language Learning with Decoupled Language Pre-training
We present a novel methodology aimed at optimizing the application of frozen large language models (LLMs) for resource-intensive vision-language (VL) pre-training.
Joint Latent Topic Discovery and Expectation Modeling for Financial Markets
In the pursuit of accurate and scalable quantitative methods for financial market analysis, the focus has shifted from individual stock models to those capturing interrelations between companies and their stocks.
Knowledge from Large-Scale Protein Contact Prediction Models Can Be Transferred to the Data-Scarce RNA Contact Prediction Task
Our findings indicate that the learned structural patterns of proteins can be transferred to RNAs, opening up potential new avenues for research.
Learning to Collocate Visual-Linguistic Neural Modules for Image Captioning
This is because the language is only partially observable, for which we need to dynamically collocate the modules during the process of image captioning.
Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings
This indicates that Transformer models are able to generalize better by doing a similar task (i. e., clustering) with unpaired examples from different modalities in a multi-task fashion.
Embedding Hallucination for Few-Shot Language Fine-tuning
Few-shot language learners adapt knowledge from a pre-trained model to recognize novel classes from a few-labeled sentences.
Contrastive Learning for Prompt-Based Few-Shot Language Learners
Following this line of work, we present a contrastive learning framework that clusters inputs from the same class for better generality of models trained with only limited examples.
Non-Parallel Text Style Transfer with Self-Parallel Supervision
The performance of existing text style transfer models is severely limited by the non-parallel datasets on which the models are trained.
Knowledge Infused Decoding
Hence, they tend to suffer from counterfactual or hallucinatory generation when used in knowledge-intensive natural language generation (NLG) tasks.
Ranked #2 on
Question Answering
on KILT: ELI5
MetaPix: Domain Transfer for Semantic Segmentation by Meta Pixel Weighting
Previous work has shown that the performance of a semantic segmentation model can be improved by training jointly with real and synthetic examples with a proper weighting on the synthetic data.
Auto-Parsing Network for Image Captioning and Visual Question Answering
We propose an Auto-Parsing Network (APN) to discover and exploit the input data's hidden tree structures for improving the effectiveness of the Transformer-based vision-language systems.
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path
A common type of network is the heterogeneous network, where the nodes (and edges) can be of different types.
An Empirical Survey of Unsupervised Text Representation Methods on Twitter Data
The field of NLP has seen unprecedented achievements in recent years.
Hierarchical Scene Graph Encoder-Decoder for Image Paragraph Captioning
We propose irredundant attention in SSG-RNN to improve the possibility of abstracting topics from rarely described sub-graphs and inheriting attention in WSG-RNN to generate more grounded sentences with the abstracted topics, both of which give rise to more distinctive paragraphs.
