Search Results for author:
Found 11 papers, 5 papers with code
AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension
By revealing the limitations of existing LALMs through evaluation results, AIR-Bench can provide insights into the direction of future research.
An Adaptive Framework of Geographical Group-Specific Network on O2O Recommendation
Online to offline recommendation strongly correlates with the user and service's spatiotemporal information, therefore calling for a higher degree of model personalization.
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
Recently, instruction-following audio-language models have received broad attention for audio interaction with humans.
Ranked #1 on
Acoustic Scene Classification
on TUT Acoustic Scenes 2017
(using extra training data)
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT
In this paper, we propose LauraGPT, a unified GPT model for audio recognition, understanding, and generation.
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
Ranked #3 on
Multi-Label Text Classification
on CC3M-TagMask
Knowledge Distillation of Transformer-based Language Models Revisited
In the past few years, transformer-based pre-trained language models have achieved astounding success in both industry and academia.
Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI
However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed.
Dynamic Sequential Graph Learning for Click-Through Rate Prediction
In this paper, we propose a novel method, named Dynamic Sequential Graph Learning (DSGL), to enhance users or items' representations by utilizing collaborative information from the local sub-graphs associated with users or items.
TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning
Secondly, on top of the proposed graph transformer, we introduce a two-stream encoder that separately extracts representations from temporal neighborhoods associated with the two interaction nodes and then utilizes a co-attentional transformer to model inter-dependencies at a semantic level.
Inductive Granger Causal Modeling for Multivariate Time Series
To bridge this gap, we propose an Inductive GRanger cAusal modeling (InGRA) framework for inductive Granger causality learning and common causal structure detection on multivariate time series, which exploits the shared commonalities underlying the different individuals.
Granger Causal Structure Reconstruction from Heterogeneous Multivariate Time Series
Granger causal structure reconstruction is an emerging topic that can uncover causal relationship behind multivariate time series data.
