Search Results for author:
Found 9 papers, 3 papers with code
NatLan: Native Language Prompting Facilitates Knowledge Elicitation Through Language Trigger Provision and Domain Trigger Retention
In this study, we analogize the dominant language of MLLMs to the native language of humans and use two human cognitive features: the Language Trigger (LT) and the Domain Trigger (DT), to interpret the mechanisms behind translate-then-answer methods.
High-probability minimax lower bounds
Our overall goal is to argue that minimax quantiles can provide a finer-grained understanding of the difficulty of statistical problems, and that, in wide generality, lower bounds on these quantities can be obtained via user-friendly tools.
Satisficing Exploration in Bandit Optimization
We propose SELECT, a general algorithmic template for Satisficing Exploration via LowEr Confidence bound Testing, that attains constant satisficing regret for a wide variety of bandit optimization problems in the realizable case (i. e., a satisficing arm exists).
Dual-level Hypergraph Contrastive Learning with Adaptive Temperature Enhancement
However, these works have the following limitations in modeling the high-order relationships over unlabeled data: (i) They primarily focus on maximizing the agreements among individual node embeddings while neglecting the capture of group-wise collective behaviors within hypergraphs; (ii) Most of them disregard the importance of the temperature index in discriminating contrastive pairs during contrast optimization.
Ranked #1 on
Hypergraph Contrastive Learning
on Twitter-HyDrug-UR
Hypergraph Contrastive Learning for Drug Trafficking Community Detection
To this end, we propose a novel HyperGraph Contrastive Learning framework called HyGCL-DC that employs hypergraph to model the higher-order relationships among users to detect Drug trafficking Communities.
Ranked #1 on
Community Detection
on Twitter-HyDrug
Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction
However, integrating LLMs into existing quantitative models presents two primary challenges: the insufficient utilization of semantic information embedded within LLMs and the difficulties in aligning the latent information within LLMs with pre-existing quantitative stock features.
ProD: Prompting-To-Disentangle Domain Knowledge for Cross-Domain Few-Shot Image Classification
Based on these two common practices, the key point of ProD is using the prompting mechanism in the transformer to disentangle the domain-general (DG) and domain-specific (DS) knowledge from the backbone feature.
Visual-speech Synthesis of Exaggerated Corrective Feedback
To provide more discriminative feedback for the second language (L2) learners to better identify their mispronunciation, we propose a method for exaggerated visual-speech feedback in computer-assisted pronunciation training (CAPT).
Dialogue Design and Management for Multi-Session Casual Conversation with Older Adults
We address the problem of designing a conversational avatar capable of a sequence of casual conversations with older adults.
