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Found 12 papers, 4 papers with code
BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation
The upscaling of Large Language Models (LLMs) has yielded impressive advances in natural language processing, yet it also poses significant deployment challenges.
CharacterGLM: Customizing Chinese Conversational AI Characters with Large Language Models
In this paper, we present CharacterGLM, a series of models built upon ChatGLM, with model sizes ranging from 6B to 66B parameters.
AFPQ: Asymmetric Floating Point Quantization for LLMs
Large language models (LLMs) show great performance in various tasks, but face deployment challenges from limited memory capacity and bandwidth.
A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations
Emotion recognition in conversations (ERC), the task of recognizing the emotion of each utterance in a conversation, is crucial for building empathetic machines.
Ranked #1 on
Emotion Recognition in Conversation
on IEMOCAP
Emotion Recognition in Conversation
Multimodal Emotion Recognition
TKwinFormer: Top k Window Attention in Vision Transformers for Feature Matching
Local feature matching remains a challenging task, primarily due to difficulties in matching sparse keypoints and low-texture regions.
Adam Accumulation to Reduce Memory Footprints of both Activations and Gradients for Large-scale DNN Training
We find that previous gradient accumulation reduces activation memory but fails to be compatible with gradient memory reduction due to a contradiction between preserving gradients and releasing gradients.
Integer or Floating Point? New Outlooks for Low-Bit Quantization on Large Language Models
In this study, we conduct a comparative analysis of INT and FP quantization with the same bit-width, revealing that the optimal quantization format varies across different layers due to the complexity and diversity of tensor distribution.
TC-GAT: Graph Attention Network for Temporal Causality Discovery
The present study explores the intricacies of causal relationship extraction, a vital component in the pursuit of causality knowledge.
A Unified Review of Deep Learning for Automated Medical Coding
Automated medical coding, an essential task for healthcare operation and delivery, makes unstructured data manageable by predicting medical codes from clinical documents.
Exploring Semi-supervised Variational Autoencoders for Biomedical Relation Extraction
Experimental results show that our method effectively exploits the unlabeled data to improve the performance and reduce the dependence on labeled data.
Drug–drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths
Results In this article, we present a hierarchical recurrent neural networks (RNNs)-based method to integrate the SDP and sentence sequence for DDI extraction task.
A graph kernel based on context vectors for extracting drug–drug interactions
Especially for the Medline-2013 dataset, our system outperforms the top-ranking DDIs systems by F-scores of 10. 7 and 12. 2 in detection and classification, respectively.
