Search Results for author:
Found 15 papers, 7 papers with code
FedHQ: Hybrid Runtime Quantization for Federated Learning
However, many studies fail to consider the distinct performance attribution between particular quantization strategies, such as post-training quantization (PTQ) or quantization-aware training (QAT).
MoQa: Rethinking MoE Quantization with Multi-stage Data-model Distribution Awareness
However, their analysis is designed for dense LLMs and relies on the simple one-model-all-data mapping, which is unsuitable for MoEs.
Test Time Training for 4D Medical Image Interpolation
Our method not only advances 4D medical image interpolation but also provides a template for domain adaptation in other fields such as image segmentation and image registration.
RaSeRec: Retrieval-Augmented Sequential Recommendation
Although prevailing supervised and self-supervised learning (SSL)-augmented sequential recommendation (SeRec) models have achieved improved performance with powerful neural network architectures, we argue that they still suffer from two limitations: (1) Preference Drift, where models trained on past data can hardly accommodate evolving user preference; and (2) Implicit Memory, where head patterns dominate parametric learning, making it harder to recall long tails.
Ranked #2 on
Sequential Recommendation
on Amazon-Beauty
Threshold Neuron: A Brain-inspired Artificial Neuron for Efficient On-device Inference
Enhancing the computational efficiency of on-device Deep Neural Networks (DNNs) remains a significant challengein mobile and edge computing.
KNOWCOMP POKEMON Team at DialAM-2024: A Two-Stage Pipeline for Detecting Relations in Dialogical Argument Mining
Dialogical Argument Mining(DialAM) is an important branch of Argument Mining(AM).
Infinite-Dimensional Feature Interaction
The past neural network design has largely focused on feature representation space dimension and its capacity scaling (e. g., width, depth), but overlooked the feature interaction space scaling.
Simulate and Eliminate: Revoke Backdoors for Generative Large Language Models
We initially propose Overwrite Supervised Fine-tuning (OSFT) for effective backdoor removal when the trigger is known.
Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation
To evaluate the effectiveness of our approach in terms of truthfulness and detoxification, we conduct extensive experiments on LLMs, encompassing additional abilities such as language modeling and mathematical reasoning.
Exploring & Exploiting High-Order Graph Structure for Sparse Knowledge Graph Completion
The proposed approach comprises two main components: a GNN-based predictor and a reasoning path distiller.
CKBP v2: Better Annotation and Reasoning for Commonsense Knowledge Base Population
We show that CKBP v2 serves as a challenging and representative evaluation dataset for the CSKB Population task, while its development set aids in selecting a population model that leads to improved knowledge acquisition for downstream commonsense reasoning.
VEM$^2$L: A Plug-and-play Framework for Fusing Text and Structure Knowledge on Sparse Knowledge Graph Completion
In this paper, we solve the sparse KGC from these two motivations simultaneously and handle their respective drawbacks further, and propose a plug-and-play unified framework VEM$^2$L over sparse KGs.
DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension
Multiparty Dialogue Machine Reading Comprehension (MRC) differs from traditional MRC as models must handle the complex dialogue discourse structure, previously unconsidered in traditional MRC.
Ranked #4 on
Question Answering
on Molweni
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure
Research into the area of multiparty dialog has grown considerably over recent years.
Ranked #7 on
Discourse Parsing
on Molweni
An Annotation Scheme of A Large-scale Multi-party Dialogues Dataset for Discourse Parsing and Machine Comprehension
In this paper, we propose the scheme for annotating large-scale multi-party chat dialogues for discourse parsing and machine comprehension.
