Search Results for author:
Found 28 papers, 14 papers with code
Enhancing the Context Representation in Similarity-based Word Sense Disambiguation
In previous similarity-based WSD systems, studies have allocated much effort on learning comprehensive sense embeddings using contextual representations and knowledge sources.
A Synset Relation-enhanced Framework with a Try-again Mechanism for Word Sense Disambiguation
Contextual embeddings are proved to be overwhelmingly effective to the task of Word Sense Disambiguation (WSD) compared with other sense representation techniques.
Benchmarking Post-Training Quantization in LLMs: Comprehensive Taxonomy, Unified Evaluation, and Comparative Analysis
Then, we conduct extensive experiments with the baseline within each class, covering models with various sizes (7B-70B), bitwidths, training levels (LLaMA1/2/3/3. 1), architectures (Mixtral, DeepSeekMoE and Mamba) and modality (LLaVA1. 5 and VILA1. 5) on a wide range of evaluation metrics. Through comparative analysis on the results, we summarize the superior of each PTQ strategy and modelsize-bitwidth trade-off considering the performance.
PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models
To explore the real limit of PTQ, we propose an extremely low-bit PTQ method called PTQ1. 61, which enables weight quantization to 1. 61-bit for the first time.
Language Models as Continuous Self-Evolving Data Engineers
Large Language Models (LLMs) have demonstrated remarkable capabilities on various tasks, while the further evolvement is limited to the lack of high-quality training data.
Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts
LLMs have demonstrated commendable performance across diverse domains.
Creating a Microstructure Latent Space with Rich Material Information for Multiphase Alloy Design
By integrating this deep learning model with a specific sampling strategy in the latent space, a novel, microstructure-centered algorithm for multiphase alloy design is developed.
Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answering
Multi-hop Question Answering (QA) necessitates complex reasoning by integrating multiple pieces of information to resolve intricate questions.
FEEL: A Framework for Evaluating Emotional Support Capability with Large Language Models
Emotional Support Conversation (ESC) is a typical dialogue that can effectively assist the user in mitigating emotional pressures.
Is Mamba Effective for Time Series Forecasting?
For TSF tasks, these characteristics enable Mamba to comprehend hidden patterns as the Transformer and reduce computational overhead compared to the Transformer.
Ranked #60 on
Time Series Forecasting
on ETTh1 (336) Multivariate
LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language
Experiments illustrate that LangGPT significantly enhances the performance of LLMs.
MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks
Consequently, our work complements research on the performance of MLLMs in multimodal comprehension tasks, achieving a more comprehensive and holistic evaluation of MLLMs.
T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systems
The application of CEs encounters two main challenges: general user preferences and variable ML systems.
RoCar: A Relationship Network-based Evaluation Method for Large Language Models
Large language models (LLMs) have received increasing attention.
G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer
The detection of the underlying shopping intentions of users based on their historical interactions is a crucial aspect for e-commerce platforms, such as Amazon, to enhance the convenience and efficiency of their customers' shopping experiences.
Text Is All You Need: Learning Language Representations for Sequential Recommendation
In this paper, we propose to model user preferences and item features as language representations that can be generalized to new items and datasets.
Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy
We propose a binary stochastic learning algorithm that modifies all elementary neural network operations, by introducing (i) stochastic binarization of both the forwarding signals and the activation function derivatives, (ii) signed binarization of the backpropagating errors, and (iii) step-wised weight updates.
DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition
This is the first framework on gait recognition that is designed to focus on the extraction of dynamic features.
Evidence-based Match-status-Aware Gait Recognition for Out-of-Gallery Gait Identification
We measure the uncertainty and predict the match status of the recognition results, and thus determine whether the probe is an OOG query. To the best of our knowledge, our method is the first attempt to tackle OOG queries in gait recognition.
Deep Baseline Network for Time Series Modeling and Anomaly Detection
By using this deep network, we can easily locate the baseline position and then provide reliable and interpretable anomaly detection result.
GaitGL: Learning Discriminative Global-Local Feature Representations for Gait Recognition
GFR extractor aims to extract contextual information, e. g., the relationship among various body parts, and the mask-based LFR extractor is presented to exploit the detailed posture changes of local regions.
EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing
The success of Pre-Trained Models (PTMs) has reshaped the development of Natural Language Processing (NLP).
GaitStrip: Gait Recognition via Effective Strip-based Feature Representations and Multi-Level Framework
ECM consists of the Spatial-Temporal feature extractor (ST), the Frame-Level feature extractor (FL) and SPB, and has two obvious advantages: First, each branch focuses on a specific representation, which can be used to improve the robustness of the network.
Machine Learning for Real-Time, Automatic, and Early Diagnosis of Parkinson's Disease by Extracting Signs of Micrographia from Handwriting Images
Parkinson's disease (PD) is debilitating, progressive, and clinically marked by motor symptoms.
Word Sense Disambiguation: Towards Interactive Context Exploitation from Both Word and Sense Perspectives
Lately proposed Word Sense Disambiguation (WSD) systems have approached the estimated upper bound of the task on standard evaluation benchmarks.
Word Sense Disambiguation: A comprehensive knowledge exploitation framework
Word Sense Disambiguation (WSD) has been a basic and on-going issue since its introduction in natural language processing (NLP) community.
Ranked #1 on
Word Sense Disambiguation
on Knowledge-based:
YNUDLG at SemEval-2017 Task 4: A GRU-SVM Model for Sentiment Classification and Quantification in Twitter
Sentiment analysis is one of the central issues in Natural Language Processing and has become more and more important in many fields.
