Search Results for author: Yi Gu

Found 29 papers, 12 papers with code

LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models

1 code implementation8 Apr 2024 Shibo Hao, Yi Gu, Haotian Luo, Tianyang Liu, Xiyan Shao, Xinyuan Wang, Shuhua Xie, Haodi Ma, Adithya Samavedhi, Qiyue Gao, Zhen Wang, Zhiting Hu

(2) We develop LLM Reasoners, a library for standardized modular implementation of existing and new reasoning algorithms, under a unified formulation of the search, reward, and world model components.

Relative Preference Optimization: Enhancing LLM Alignment through Contrasting Responses across Identical and Diverse Prompts

1 code implementation12 Feb 2024 Yueqin Yin, Zhendong Wang, Yi Gu, Hai Huang, Weizhu Chen, Mingyuan Zhou

In the field of large language models (LLMs), aligning models with the diverse preferences of users is a critical challenge.

Automatic hip osteoarthritis grading with uncertainty estimation from computed tomography using digitally-reconstructed radiographs

1 code implementation30 Dec 2023 Masachika Masuda, Mazen Soufi, Yoshito Otake, Keisuke Uemura, Sotaro Kono, Kazuma Takashima, Hidetoshi Hamada, Yi Gu, Masaki Takao, Seiji Okada, Nobuhiko Sugano, Yoshinobu Sato

However, as the classification is subjective, we aimed to develop an automated approach to classify the disease severity based on the two grades using digitally-reconstructed radiographs (DRRs) from CT images.


Deep Reinforcement Learning-Based Battery Conditioning Hierarchical V2G Coordination for Multi-Stakeholder Benefits

no code implementations1 Aug 2023 Yubao Zhang, Xin Chen, Yi Gu, Zhicheng Li, Wu Kai

On the grid side, load fluctuations and renewable energy consumption are considered, while on the EVA side, energy constraints and charging costs are considered.


Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography

1 code implementation21 Jul 2023 Yi Gu, Yoshito Otake, Keisuke Uemura, Mazen Soufi, Masaki Takao, Hugues Talbot, Seiji Okada, Nobuhiko Sugano, Yoshinobu Sato

The proposed method achieved high accuracy in BMD estimation, where Pearson correlation coefficients of 0. 880 and 0. 920 were observed for DXA-measured BMD and QCT-measured BMD estimation tasks, respectively, and the root mean square of the coefficient of variation values were 3. 27 to 3. 79% for four measurements with different poses.

Density Estimation

MSKdeX: Musculoskeletal (MSK) decomposition from an X-ray image for fine-grained estimation of lean muscle mass and muscle volume

no code implementations31 May 2023 Yi Gu, Yoshito Otake, Keisuke Uemura, Masaki Takao, Mazen Soufi, Yuta Hiasa, Hugues Talbot, Seiji Okata, Nobuhiko Sugano, Yoshinobu Sato

We propose a method (named MSKdeX) to estimate fine-grained muscle properties from a plain X-ray image, a low-cost, low-radiation, and highly accessible imaging modality, through musculoskeletal decomposition leveraging fine-grained segmentation in CT. We train a multi-channel quantitative image translation model to decompose an X-ray image into projections of CT of individual muscles to infer the lean muscle mass and muscle volume.

Computed Tomography (CT) Image-to-Image Translation +1

Reasoning with Language Model is Planning with World Model

3 code implementations24 May 2023 Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, Zhiting Hu

RAP on LLAMA-33B surpasses CoT on GPT-4 with 33% relative improvement in a plan generation setting.

Language Modelling Math

Language Models Meet World Models: Embodied Experiences Enhance Language Models

1 code implementation NeurIPS 2023 Jiannan Xiang, Tianhua Tao, Yi Gu, Tianmin Shu, ZiRui Wang, Zichao Yang, Zhiting Hu

While large language models (LMs) have shown remarkable capabilities across numerous tasks, they often struggle with simple reasoning and planning in physical environments, such as understanding object permanence or planning household activities.

Learning Markov Random Fields for Combinatorial Structures via Sampling through Lovász Local Lemma

1 code implementation1 Dec 2022 Nan Jiang, Yi Gu, Yexiang Xue

Contrastive divergence is then applied to separate these samples from those in the training set.

LEMMA valid

JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions

1 code implementation18 Oct 2022 Mo Yu, Yi Gu, Xiaoxiao Guo, Yufei Feng, Xiaodan Zhu, Michael Greenspan, Murray Campbell, Chuang Gan

Hence, in order to achieve higher performance on our tasks, models need to effectively utilize such functional knowledge to infer the outcomes of actions, rather than relying solely on memorizing facts.

Reading Comprehension

Revisiting the Roles of "Text" in Text Games

no code implementations15 Oct 2022 Yi Gu, Shunyu Yao, Chuang Gan, Joshua B. Tenenbaum, Mo Yu

Text games present opportunities for natural language understanding (NLU) methods to tackle reinforcement learning (RL) challenges.

Natural Language Understanding Passage Retrieval +2

MaskRange: A Mask-classification Model for Range-view based LiDAR Segmentation

no code implementations24 Jun 2022 Yi Gu, Yuming Huang, Chengzhong Xu, Hui Kong

To answer this question, we propose a unified mask-classification model, MaskRange, for the range-view based LiDAR semantic and panoptic segmentation.

Classification Data Augmentation +3

Learning Moving-Object Tracking with FMCW LiDAR

no code implementations2 Mar 2022 Yi Gu, Hongzhi Cheng, Kafeng Wang, Dejing Dou, Chengzhong Xu, Hui Kong

In this paper, we propose a learning-based moving-object tracking method utilizing our newly developed LiDAR sensor, Frequency Modulated Continuous Wave (FMCW) LiDAR.

Contrastive Learning Object +1

Piecing and Chipping: An effective solution for the information-erasing view generation in Self-supervised Learning

no code implementations29 Sep 2021 Jingwei Liu, Yi Gu, Shentong Mo, Zhun Sun, Shumin Han, Jiafeng Guo, Xueqi Cheng

In self-supervised learning frameworks, deep networks are optimized to align different views of an instance that contains the similar visual semantic information.

Data Augmentation Self-Supervised Learning

Degree Counting Theorems for 2x2 non-symmetric singular Liouville Systems

no code implementations16 Dec 2020 Yi Gu

Let $(M, g)$ be a compact Riemann surface with no boundary and $u=(u_1, u_2)$ be a solution of the following singular Liouville system: $$\Delta_g u_i+\sum_{j=1}^2 a_{ij}\rho_j(\frac{h_je^{u_j}}{\int_M h_je^{u_j}dV_g}-1)=\sum_{l=1}^{N}4\pi\gamma_l(\delta_{p_l}-1), $$ where $h_1, h_2$ are positive smooth functions, $p_1,\cdots, p_N$ are distinct points on $M$, $\delta_{p_l}$ are Dirac masses, $\rho=(\rho_1,\rho_2)(\rho_i\geq 0)$ and $(\gamma_1,\cdots,\gamma_N)(\gamma_l > -1)$ are constant vectors.

Analysis of PDEs 35A01, 35B44, 35B45

Generative and Discriminative Learning for Distorted Image Restoration

no code implementations11 Nov 2020 Yi Gu, Yuting Gao, Jie Li, Chentao Wu, Weijia Jia

Due to the uncertainty in the distortion variation, restoring distorted images caused by liquify filter is a challenging task.

Image Restoration

VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics

no code implementations27 May 2020 Zichao Wang, Yi Gu, Andrew Lan, Richard Baraniuk

We propose VarFA, a variational inference factor analysis framework that extends existing factor analysis models for educational data mining to efficiently output uncertainty estimation in the model's estimated factors.

Bayesian Inference Variational Inference

Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertainty

no code implementations14 Mar 2020 Chao Han, Yi Gu, Guohua Wu, Xinwei Wang

We are the first to address multiple agile EOSs scheduling problem under cloud coverage uncertainty where the objective aims to maximize the entire observation profit.

Earth Observation Scheduling

A novel image tag completion method based on convolutional neural network

no code implementations2 Mar 2017 Yanyan Geng, Guohui Zhang, Weizhi Li, Yi Gu, Ru-Ze Liang, Gaoyuan Liang, Jingbin Wang, Yanbin Wu, Nitin Patil, Jing-Yan Wang

In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN).

Image Retrieval Retrieval +1

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