Search Results for author:
Found 9 papers, 3 papers with code
HGOT: Hierarchical Graph of Thoughts for Retrieval-Augmented In-Context Learning in Factuality Evaluation
With the widespread adoption of large language models (LLMs) in numerous applications, the challenge of factuality and the propensity for hallucinations raises significant concerns.
ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detection
Open intent detection, a crucial aspect of natural language understanding, involves the identification of previously unseen intents in user-generated text.
Ranked #1 on
Open Intent Detection
on StackOverflow_CG
Intrinsic and extrinsic deep learning on manifolds
We propose extrinsic and intrinsic deep neural network architectures as general frameworks for deep learning on manifolds.
Extrinsic Bayesian Optimizations on Manifolds
We propose an extrinsic Bayesian optimization (eBO) framework for general optimization problems on manifolds.
Exploring Robustness of Prefix Tuning in Noisy Data: A Case Study in Financial Sentiment Analysis
The invention of transformer-based models such as BERT, GPT, and RoBERTa has enabled researchers and financial companies to finetune these powerful models and use them in different downstream tasks to achieve state-of-the-art performance.
Training Graph Neural Networks by Graphon Estimation
In this work, we propose to train a graph neural network via resampling from a graphon estimate obtained from the underlying network data.
Optimization of Graph Neural Networks with Natural Gradient Descent
In this work, we propose to employ information-geometric tools to optimize a graph neural network architecture such as the graph convolutional networks.
Ranked #1 on
Node Classification
on Cora
CacheNet: A Model Caching Framework for Deep Learning Inference on the Edge
Inference of uncompressed large scale DNN models can only run in the cloud with extra communication latency back and forth between cloud and end devices, while compressed DNN models achieve real-time inference on end devices at the price of lower predictive accuracy.
Logographic Subword Model for Neural Machine Translation
A novel logographic subword model is proposed to reinterpret logograms as abstract subwords for neural machine translation.
