1 code implementation • 7 Apr 2024 • Jiankai Tang, Xinyi Li, Jiacheng Liu, Xiyuxing Zhang, Zeyu Wang, Yuntao Wang
Remote photoplethysmography (rPPG) emerges as a promising method for non-invasive, convenient measurement of vital signs, utilizing the widespread presence of cameras.
no code implementations • 3 Apr 2024 • Da Li, Peian Li, Jiabiao Zhao, Jianjian Liang, Jiacheng Liu, Guohao Liu, Yuanshuai Lei, Wenbo Liu, Jianqin Deng, Fuyong Liu, Jianjun Ma
Employing experimental measurements through an unmodulated channel setup and a geometry-based stochastic model (GBSM) that integrates three-dimensional positional coordinates and beamwidth, this work evaluates the impact of UAV dynamic movements and antenna orientation on channel performance.
no code implementations • 3 Apr 2024 • Hanxuan Wang, Na Lu, Zixuan Wang, Jiacheng Liu, Jun Liu
TSA-ENRT utilizes an expert guiding nonlinear regression tree to approximate the neural network prediction and the neural network can be explained by the interpretive rules generated by the tree model.
no code implementations • 30 Mar 2024 • Ziyi Zhou, XiaoMing Zhang, Litian Zhang, Jiacheng Liu, Xi Zhang, Chaozhuo Li
Existing benchmarks for fake news detection have significantly contributed to the advancement of models in assessing the authenticity of news content.
no code implementations • 9 Feb 2024 • Jiacheng Liu, Jaideep Srivastava
To achieve this goal, we propose variance SHAP with variational time series models, an application of Shapley Additive Expanation(SHAP) algorithm to attribute epistemic prediction uncertainty.
no code implementations • 9 Feb 2024 • Jiacheng Liu, Lisa Kirkland, Jaideep Srivastava
Therefore, in this study, for binary classifiers running in a long time period, we proposed to adjust these performance metrics for sample size and class distribution, so that a fair comparison can be made between two time periods.
no code implementations • 6 Feb 2024 • Soumya Sanyal, Tianyi Xiao, Jiacheng Liu, Wenya Wang, Xiang Ren
Finally, we use this model to filter out inconsistent model-generated rationales in self-consistency decoding, resulting in a 6% accuracy improvement on average across three MCQ datasets.
no code implementations • 30 Jan 2024 • Jiacheng Liu, Sewon Min, Luke Zettlemoyer, Yejin Choi, Hannaneh Hajishirzi
Second, existing $n$-gram LMs use small $n$ which hinders their performance; we instead allow $n$ to be arbitrarily large, by introducing a new $\infty$-gram LM with backoff.
2 code implementations • 10 Jan 2024 • Yuanchun Li, Hao Wen, Weijun Wang, Xiangyu Li, Yizhen Yuan, Guohong Liu, Jiacheng Liu, Wenxing Xu, Xiang Wang, Yi Sun, Rui Kong, Yile Wang, Hanfei Geng, Jian Luan, Xuefeng Jin, Zilong Ye, Guanjing Xiong, Fan Zhang, Xiang Li, Mengwei Xu, Zhijun Li, Peng Li, Yang Liu, Ya-Qin Zhang, Yunxin Liu
Next, we discuss several key challenges to achieve intelligent, efficient and secure Personal LLM Agents, followed by a comprehensive survey of representative solutions to address these challenges.
no code implementations • 22 Dec 2023 • Filippos Christianos, Georgios Papoudakis, Matthieu Zimmer, Thomas Coste, Zhihao Wu, Jingxuan Chen, Khyati Khandelwal, James Doran, Xidong Feng, Jiacheng Liu, Zheng Xiong, Yicheng Luo, Jianye Hao, Kun Shao, Haitham Bou-Ammar, Jun Wang
This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
1 code implementation • 7 Oct 2023 • Jiacheng Liu, Ramakanth Pasunuru, Hannaneh Hajishirzi, Yejin Choi, Asli Celikyilmaz
Extensive work has shown that the performance and interpretability of commonsense reasoning can be improved via knowledge-augmented reasoning methods, where the knowledge that underpins the reasoning process is explicitly verbalized and utilized.
1 code implementation • 3 Oct 2023 • Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, Jianfeng Gao
To bridge this gap, we present MathVista, a benchmark designed to combine challenges from diverse mathematical and visual tasks.
no code implementations • 26 Sep 2023 • Jiacheng Liu, Andrew Cohen, Ramakanth Pasunuru, Yejin Choi, Hannaneh Hajishirzi, Asli Celikyilmaz
The key idea is not to throw out the value network, a byproduct of PPO training for evaluating partial output sequences, when decoding text out of the policy network.
no code implementations • 29 Aug 2023 • Wenxing Xu, Yuanchun Li, Jiacheng Liu, Yi Sun, Zhengyang Cao, Yixuan Li, Hao Wen, Yunxin Liu
Unlike cloud-based deep learning models that are often large and uniform, edge-deployed models usually demand customization for domain-specific tasks and resource-limited environments.
no code implementations • 20 Jun 2023 • Shuo Han, Jiacheng Liu, Jiayun Wu, Yinan Chen, Li Tao
The architecture of GTAGC encompasses graph embedding, integration of the Graph Transformer within the autoencoder structure, and a clustering component.
no code implementations • 15 Jun 2023 • Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, Alicia Parrish, Aaron Mueller, Ameya Prabhu, Euan McLean, Aaron Kirtland, Alexis Ross, Alisa Liu, Andrew Gritsevskiy, Daniel Wurgaft, Derik Kauffman, Gabriel Recchia, Jiacheng Liu, Joe Cavanagh, Max Weiss, Sicong Huang, The Floating Droid, Tom Tseng, Tomasz Korbak, Xudong Shen, Yuhui Zhang, Zhengping Zhou, Najoung Kim, Samuel R. Bowman, Ethan Perez
Here, we present evidence for the claim that LMs may show inverse scaling, or worse task performance with increased scale, e. g., due to flaws in the training objective and data.
no code implementations • 12 Jun 2023 • Shuo Han, Yinan Chen, Jiacheng Liu
Our approach bifurcates into a Price Portfolio Forecasting Model and a Mean-Variance Model with Transaction Costs, utilizing probability weights as the coefficients of laziness factors.
1 code implementation • 5 May 2023 • Jiacheng Liu, Wenya Wang, Dianzhuo Wang, Noah A. Smith, Yejin Choi, Hannaneh Hajishirzi
Despite the much discussed capabilities of today's language models, they are still prone to silly and unexpected commonsense failures.
3 code implementations • 21 Oct 2022 • Albert Q. Jiang, Sean Welleck, Jin Peng Zhou, Wenda Li, Jiacheng Liu, Mateja Jamnik, Timothée Lacroix, Yuhuai Wu, Guillaume Lample
In this work, we introduce Draft, Sketch, and Prove (DSP), a method that maps informal proofs to formal proof sketches, and uses the sketches to guide an automated prover by directing its search to easier sub-problems.
Ranked #3 on Automated Theorem Proving on miniF2F-valid (Pass@100 metric)
1 code implementation • 6 Oct 2022 • Jiacheng Liu, Skyler Hallinan, Ximing Lu, Pengfei He, Sean Welleck, Hannaneh Hajishirzi, Yejin Choi
Our work is the first to report that knowledge generated by models that are orders of magnitude smaller than GPT-3, even without direct supervision on the knowledge itself, can exceed the quality of commonsense knowledge elicited from GPT-3.
no code implementations • 28 Jul 2022 • Guijie Zhu, Zhun Fan, Jiacheng Liu, Duan Yuan, Peili Ma, Meihua Wang, Weihua Sheng, Kelvin C. P. Wang
In this paper, an efficient and effective end-to-end network for automatic pavement crack segmentation, called RHA-Net, is proposed to improve the pavement crack segmentation accuracy.
1 code implementation • 25 May 2022 • Sean Welleck, Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi, Yejin Choi
Theorem proving in natural mathematical language - the mixture of symbolic and natural language used by humans - plays a central role in mathematical advances and education, and tests aspects of reasoning that are core to intelligence.
1 code implementation • 16 Nov 2021 • Liang Xu, Jiacheng Liu, Xiang Pan, Xiaojing Lu, Xiaofeng Hou
However, we have not seen significant research progress in this field, especially in NLP.
1 code implementation • ACL 2022 • Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh Hajishirzi
It remains an open question whether incorporating external knowledge benefits commonsense reasoning while maintaining the flexibility of pretrained sequence models.
1 code implementation • 24 Mar 2021 • Sean Welleck, Jiacheng Liu, Ronan Le Bras, Hannaneh Hajishirzi, Yejin Choi, Kyunghyun Cho
Understanding and creating mathematics using natural mathematical language - the mixture of symbolic and natural language used by humans - is a challenging and important problem for driving progress in machine learning.
no code implementations • 19 Jan 2021 • Jiacheng Liu, Meghna Singh, Catherine ST. Hill, Vino Raj, Lisa Kirkland, Jaideep Srivastava
In this paper, we first verify the assumption that clinical variables could have time-varying effects on COVID-19 outcomes.
1 code implementation • IJCNLP 2019 • Zihan Wang, Jingbo Shang, Liyuan Liu, Lihao Lu, Jiacheng Liu, Jiawei Han
Therefore, we manually correct these label mistakes and form a cleaner test set.
Ranked #3 on Named Entity Recognition (NER) on CoNLL++ (using extra training data)
1 code implementation • IJCNLP 2019 • Jiacheng Liu, Julia Hockenmaier
In this paper, we formulate phrase grounding as a sequence labeling task where we treat candidate regions as potential labels, and use neural chain Conditional Random Fields (CRFs) to model dependencies among regions for adjacent mentions.
Ranked #8 on Phrase Grounding on Flickr30k Entities Test