Search Results for author:
Found 7 papers, 2 papers with code
Aquila2 Technical Report
This paper introduces the Aquila2 series, which comprises a wide range of bilingual models with parameter sizes of 7, 34, and 70 billion.
FE-Adapter: Adapting Image-based Emotion Classifiers to Videos
This breakthrough highlights the potential for cross-modality approaches in enhancing the capabilities of AI models, particularly in fields like video emotion analysis where the demand for efficiency and accuracy is constantly rising.
Ranked #7 on
Dynamic Facial Expression Recognition
on FERV39k
SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking
In contrast, human brains, which contain approximately 86 billion biological neurons, exhibit significantly greater energy efficiency compared to LLMs with a similar number of parameters.
Meta Mirror Descent: Optimiser Learning for Fast Convergence
The resulting framework, termed Meta Mirror Descent (MetaMD), learns to accelerate optimisation speed.
Loss Function Learning for Domain Generalization by Implicit Gradient
We take a different approach, and explore the impact of the ERM loss function on out-of-domain generalisation.
Searching for Robustness: Loss Learning for Noisy Classification Tasks
We present a "learning to learn" approach for automatically constructing white-box classification loss functions that are robust to label noise in the training data.
Deep clustering with concrete k-means
We address the problem of simultaneously learning a k-means clustering and deep feature representation from unlabelled data, which is of interest due to the potential of deep k-means to outperform traditional two-step feature extraction and shallow-clustering strategies.
Ranked #2 on
Online Clustering
on cifar10
