Search Results for author: Jing Tang

Found 46 papers, 27 papers with code

Deep Learning-Based Feature Fusion for Emotion Analysis and Suicide Risk Differentiation in Chinese Psychological Support Hotlines

1 code implementation15 Jan 2025 Han Wang, Jianqiang Li, Qing Zhao, Zhonglong Chen, Changwei Song, Jing Tang, Yuning Huang, Wei Zhai, Yongsheng Tong, Guanghui Fu

Mental health is a critical global public health issue, and psychological support hotlines play a pivotal role in providing mental health assistance and identifying suicide risks at an early stage.

Emotion Classification Emotion Recognition

Are LLMs Really Not Knowledgable? Mining the Submerged Knowledge in LLMs' Memory

no code implementations30 Dec 2024 Xingjian Tao, Yiwei Wang, Yujun Cai, Zhicheng Yang, Jing Tang

Large language models (LLMs) have shown promise as potential knowledge bases, yet they often struggle with question-answering tasks and are prone to hallucinations.

Question Answering

KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models

1 code implementation8 Dec 2024 Fan Wang, Juyong Jiang, Chansung Park, Sunghun Kim, Jing Tang

The increasing sizes of large language models (LLMs) result in significant computational overhead and memory usage when adapting these models to specific tasks or domains.

Instruction Following Natural Language Understanding +1

Health AI Developer Foundations

no code implementations22 Nov 2024 Atilla P. Kiraly, Sebastien Baur, Kenneth Philbrick, Fereshteh Mahvar, Liron Yatziv, Tiffany Chen, Bram Sterling, Nick George, Fayaz Jamil, Jing Tang, Kai Bailey, Faruk Ahmed, Akshay Goel, Abbi Ward, Lin Yang, Andrew Sellergren, Yossi Matias, Avinatan Hassidim, Shravya Shetty, Daniel Golden, Shekoofeh Azizi, David F. Steiner, Yun Liu, Tim Thelin, Rory Pilgrim, Can Kirmizibayrak

Finally, while HAI-DEF and specifically the foundation models lower the barrier to entry for ML in healthcare, we emphasize the importance of validation with problem- and population-specific data for each desired usage setting.

Fairness

GCoder: Improving Large Language Model for Generalized Graph Problem Solving

1 code implementation24 Oct 2024 Qifan Zhang, Xiaobin Hong, Jianheng Tang, Nuo Chen, Yuhan Li, Wenzhong Li, Jing Tang, Jia Li

Furthermore, GCoder efficiently manages large-scale graphs with millions of nodes and diverse input formats, overcoming the limitations of previous models focused on the reasoning steps paradigm.

Language Modeling Language Modelling +1

Optimal Streaming Algorithms for Multi-Armed Bandits

no code implementations23 Oct 2024 Tianyuan Jin, Keke Huang, Jing Tang, Xiaokui Xiao

We propose an algorithm that works for any $k$ and achieves the optimal sample complexity $O(\frac{n}{\eps^2} \log\frac{k}{\delta})$ using a single-arm memory and a single pass of the stream.

Multi-Armed Bandits

Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification

1 code implementation22 Oct 2024 Yihong Luo, Yuhan Chen, Siya Qiu, Yiwei Wang, Chen Zhang, Yan Zhou, Xiaochun Cao, Jing Tang

The standard SAM approach, however, consists of two forward-backward steps in each training iteration, doubling the computational cost compared to the base optimizer (e. g., Adam).

Node Classification

Plots Unlock Time-Series Understanding in Multimodal Models

no code implementations3 Oct 2024 Mayank Daswani, Mathias M. J. Bellaiche, Marc Wilson, Desislav Ivanov, Mikhail Papkov, Eva Schnider, Jing Tang, Kay Lamerigts, Gabriela Botea, Michael A. Sanchez, Yojan Patel, Shruthi Prabhakara, Shravya Shetty, Umesh Telang

While multimodal foundation models can now natively work with data beyond text, they remain underutilized in analyzing the considerable amounts of multi-dimensional time-series data in fields like healthcare, finance, and social sciences, representing a missed opportunity for richer, data-driven insights.

Activity Recognition Time Series

LlamaDuo: LLMOps Pipeline for Seamless Migration from Service LLMs to Small-Scale Local LLMs

1 code implementation24 Aug 2024 Chansung Park, Juyong Jiang, Fan Wang, Sayak Paul, Jing Tang

The widespread adoption of cloud-based proprietary large language models (LLMs) has introduced significant challenges, including operational dependencies, privacy concerns, and the necessity of continuous internet connectivity.

Language Modeling Language Modelling

CLLMFS: A Contrastive Learning enhanced Large Language Model Framework for Few-Shot Named Entity Recognition

no code implementations23 Aug 2024 Yafeng Zhang, Zilan Yu, Yuang Huang, Jing Tang

To address this issue, we propose CLLMFS, a Contrastive Learning enhanced Large Language Model (LLM) Framework for Few-Shot Named Entity Recognition, achieving promising results with limited training data.

Contrastive Learning few-shot-ner +8

OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling

1 code implementation13 Jul 2024 Zhicheng Yang, Yiwei Wang, Yinya Huang, Zhijiang Guo, Wei Shi, Xiongwei Han, Liang Feng, Linqi Song, Xiaodan Liang, Jing Tang

Furthermore, to alleviate the data scarcity for optimization problems, and to bridge the gap between open-source LLMs on a small scale (e. g., Llama-3-8b) and closed-source LLMs (e. g., GPT-4), we further propose a data synthesis method namely ReSocratic.

Benchmarking Math +1

AnyTaskTune: Advanced Domain-Specific Solutions through Task-Fine-Tuning

1 code implementation9 Jul 2024 Jiaxi Cui, Wentao Zhang, Jing Tang, Xudong Tong, Zhenwei Zhang, Amie, Jing Wen, Rongsheng Wang, Pengfei Wu

Our findings demonstrate that models fine-tuned using the \textbf{Task-Fine-Tune} methodology not only achieve superior performance on these specific tasks but also significantly outperform models with higher general capabilities in their respective domains.

Keyword Extraction Sentence

A Survey on Mixture of Experts

1 code implementation26 Jun 2024 Weilin Cai, Juyong Jiang, Fan Wang, Jing Tang, Sunghun Kim, Jiayi Huang

Large language models (LLMs) have garnered unprecedented advancements across diverse fields, ranging from natural language processing to computer vision and beyond.

In-Context Learning Survey

Process-Driven Autoformalization in Lean 4

2 code implementations4 Jun 2024 Jianqiao Lu, Yingjia Wan, Zhengying Liu, Yinya Huang, Jing Xiong, Chengwu Liu, Jianhao Shen, Hui Jin, Jipeng Zhang, Haiming Wang, Zhicheng Yang, Jing Tang, Zhijiang Guo

Autoformalization, the conversion of natural language mathematics into formal languages, offers significant potential for advancing mathematical reasoning.

Mathematical Reasoning

Proving Theorems Recursively

1 code implementation23 May 2024 Haiming Wang, Huajian Xin, Zhengying Liu, Wenda Li, Yinya Huang, Jianqiao Lu, Zhicheng Yang, Jing Tang, Jian Yin, Zhenguo Li, Xiaodan Liang

This approach allows the theorem to be tackled incrementally by outlining the overall theorem at the first level and then solving the intermediate conjectures at deeper levels.

Automated Theorem Proving

DTIAM: A unified framework for predicting drug-target interactions, binding affinities and activation/inhibition mechanisms

1 code implementation23 Dec 2023 Zhangli Lu, Chuqi Lei, Kaili Wang, Libo Qin, Jing Tang, Min Li

DTIAM, for the first time, provides a unified framework for accurate and robust prediction of drug-target interactions, binding affinities, and activation/inhibition mechanisms.

Drug Discovery

Machine Mindset: An MBTI Exploration of Large Language Models

1 code implementation20 Dec 2023 Jiaxi Cui, Liuzhenghao Lv, Jing Wen, Rongsheng Wang, Jing Tang, Yonghong Tian, Li Yuan

We present a novel approach for integrating Myers-Briggs Type Indicator (MBTI) personality traits into large language models (LLMs), addressing the challenges of personality consistency in personalized AI.

Large Language Model Personality Alignment +2

AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations

1 code implementation22 Nov 2023 Zhicheng Yang, Yinya Huang, Jing Xiong, Liang Feng, Xiaodan Liang, Yiwei Wang, Jing Tang

Large Language Models prompting, such as using in-context demonstrations, is a mainstream technique for invoking LLMs to perform high-performance and solid complex reasoning (e. g., mathematical reasoning, commonsense reasoning), and has the potential for further human-machine collaborative scientific findings.

Common Sense Reasoning GSM8K +4

Energy-Calibrated VAE with Test Time Free Lunch

1 code implementation7 Nov 2023 Yihong Luo, Siya Qiu, Xingjian Tao, Yujun Cai, Jing Tang

To address these issues, we introduce a conditional EBM for calibrating the generative direction of VAE during training, without requiring it for the generation at test time.

Image Generation Image Restoration

Optimal Batched Best Arm Identification

no code implementations21 Oct 2023 Tianyuan Jin, Yu Yang, Jing Tang, Xiaokui Xiao, Pan Xu

Based on Tri-BBAI, we further propose the almost optimal batched best arm identification (Opt-BBAI) algorithm, which is the first algorithm that achieves the near-optimal sample and batch complexity in the non-asymptotic setting (i. e., $\delta>0$ is arbitrarily fixed), while enjoying the same batch and sample complexity as Tri-BBAI when $\delta$ tends to zero.

DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning

1 code implementation4 Oct 2023 Jing Xiong, Zixuan Li, Chuanyang Zheng, Zhijiang Guo, Yichun Yin, Enze Xie, Zhicheng Yang, Qingxing Cao, Haiming Wang, Xiongwei Han, Jing Tang, Chengming Li, Xiaodan Liang

Dual Queries first query LLM to obtain LLM-generated knowledge such as CoT, then query the retriever to obtain the final exemplars via both question and the knowledge.

Dimensionality Reduction In-Context Learning +1

Practical Parallel Algorithms for Non-Monotone Submodular Maximization

no code implementations21 Aug 2023 Shuang Cui, Kai Han, Jing Tang, Xueying Li, Aakas Zhiyuli, Hanxiao Li

Submodular maximization has found extensive applications in various domains within the field of artificial intelligence, including but not limited to machine learning, computer vision, and natural language processing.

How Fragile is Relation Extraction under Entity Replacements?

1 code implementation22 May 2023 Yiwei Wang, Bryan Hooi, Fei Wang, Yujun Cai, Yuxuan Liang, Wenxuan Zhou, Jing Tang, Manjuan Duan, Muhao Chen

In principle, textual context determines the ground-truth relation and the RE models should be able to correctly identify the relations reflected by the textual context.

Benchmarking Causal Inference +2

LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity

1 code implementation7 May 2023 Yuhan Chen, Yihong Luo, Jing Tang, Liang Yang, Siya Qiu, Chuan Wang, Xiaochun Cao

Motivated by it, we propose to use the local similarity (LocalSim) to learn node-level weighted fusion, which can also serve as a plug-and-play module.

Graph Neural Network Node Classification

Adaptive adversarial training method for improving multi-scale GAN based on generalization bound theory

no code implementations30 Nov 2022 Jing Tang, Bo Tao, Zeyu Gong, Zhouping Yin

Based on the drastic changes we found of the generalization error bound under different adversarial attacks and different training states, we proposed an adaptive training method which can greatly improve the image manipulation ability of multi-scale GANs.

Image Manipulation Image Super-Resolution

PGMG: A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecular Generation

1 code implementation2 Jul 2022 Huimin Zhu, Renyi Zhou, Jing Tang, Min Li

The rational design of novel molecules with desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets.

Diversity Drug Discovery

Diversifying Agent's Behaviors in Interactive Decision Models

no code implementations6 Mar 2022 Yinghui Pan, Hanyi Zhang, Yifeng Zeng, Biyang Ma, Jing Tang, Zhong Ming

In this article, we investigate into diversifying behaviors of other agents in the subject agent's decision model prior to their interactions.

Diversity

Bayes in Wonderland! Predictive supervised classification inference hits unpredictability

1 code implementation3 Dec 2021 Ali Amiryousefi, Ville Kinnula, Jing Tang

The marginal Bayesian predictive classifiers (mBpc) as opposed to the simultaneous Bayesian predictive classifiers (sBpc), handle each data separately and hence tacitly assumes the independence of the observations.

Classification

Structure-Aware Label Smoothing for Graph Neural Networks

no code implementations1 Dec 2021 Yiwei Wang, Yujun Cai, Yuxuan Liang, Wei Wang, Henghui Ding, Muhao Chen, Jing Tang, Bryan Hooi

Representing a label distribution as a one-hot vector is a common practice in training node classification models.

Classification Node Classification

R-BERT-CNN: Drug-target interactions extraction from biomedical literature

no code implementations31 Oct 2021 Jehad Aldahdooh, Ziaurrehman Tanoli, Jing Tang

Drug-target interactions (DTIs) are critical for drug discovery and repurposing, which are often manually extracted from the experimental articles.

Drug Discovery DrugProt +2

Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer

2 code implementations NeurIPS 2021 Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, Jing Tang

Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry of VRP solutions (i. e., cyclic sequences).

Traveling Salesman Problem

Towards Making Deep Learning-based Vulnerability Detectors Robust

1 code implementation2 Aug 2021 Zhen Li, Jing Tang, Deqing Zou, Qian Chen, Shouhuai Xu, Chao Zhang, Yichen Li, Hai Jin

Automatically detecting software vulnerabilities in source code is an important problem that has attracted much attention.

Deep Learning

Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence

no code implementations23 Sep 2020 Kanglin Hsieh, Yinyin Wang, Luyao Chen, Zhongming Zhao, Sean Savitz, Xiaoqian Jiang, Jing Tang, Yejin Kim

In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment.

Graph Neural Network

Revisiting Modified Greedy Algorithm for Monotone Submodular Maximization with a Knapsack Constraint

no code implementations12 Aug 2020 Jing Tang, Xueyan Tang, Andrew Lim, Kai Han, Chongshou Li, Junsong Yuan

Second, we enhance the modified greedy algorithm to derive a data-dependent upper bound on the optimum.

Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene

1 code implementation11 Aug 2020 Xinke Li, Chongshou Li, Zekun Tong, Andrew Lim, Junsong Yuan, Yuwei Wu, Jing Tang, Raymond Huang

Based on it, we formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies.

Instance Segmentation Point Cloud Segmentation +3

Tensor Decomposition for Multi-agent Predictive State Representation

no code implementations27 May 2020 Bilian Chen, Biyang Ma, Yifeng Zeng, Langcai Cao, Jing Tang

It is a concise knowledge representation that is well studied in a single-agent planning problem domain.

Tensor Decomposition

Efficient Approximation Algorithms for Adaptive Influence Maximization

2 code implementations14 Apr 2020 Keke Huang, Jing Tang, Kai Han, Xiaokui Xiao, Wei Chen, Aixin Sun, Xueyan Tang, Andrew Lim

In this paper, we propose the first practical algorithm for the adaptive IM problem that could provide the worst-case approximation guarantee of $1-\mathrm{e}^{\rho_b(\varepsilon-1)}$, where $\rho_b=1-(1-1/b)^b$ and $\varepsilon \in (0, 1)$ is a user-specified parameter.

Social and Information Networks

Highly fluorescent copper nanoclusters for sensing and bioimaging

no code implementations29 Dec 2019 Yu An, Ying Ren, Jing Tang, Jun Chen, Baisong Chang

Metal nanoclusters (NCs), typically consisting of a few to tens of metal atoms, bridge the gap between organometallic compounds and crystalline metal nanoparticles.

Numerical evaluation of the transition probability of the simple birth-and-death process

1 code implementation24 Sep 2019 Alberto Pessia, Jing Tang

To overcome this difficulty we will rewrite the transition probability in terms of a Gaussian hypergeometric function and subsequently obtain a three-term recurrence relation for its accurate evaluation.

Numerical Analysis Numerical Analysis Probability Methodology 65Q30 (Primary), 60J80 (Secondary), 33C05, 62F10, 62M05

Π-cyc: A Reference-free SNP Discovery Application using Parallel Graph Search

no code implementations18 Sep 2018 Reda Younsi, Jing Tang, Liisa Holm

Specialised fast algorithms for efficient bubble search are needed for coloured de bruijn graph variant calling applications.

Computational Engineering, Finance, and Science Genomics

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