Search Results for author:
Found 38 papers, 13 papers with code
How Does the Experimental Setting Affect the Conclusions of Neural Encoding Models?
Based on these results, we suggest a block-wise cross-validation training method and an adequate data size for increasing the performance of linear encoding models.
AG-NeRF: Attention-guided Neural Radiance Fields for Multi-height Large-scale Outdoor Scene Rendering
Existing neural radiance fields (NeRF)-based novel view synthesis methods for large-scale outdoor scenes are mainly built on a single altitude.
A Cause-Effect Look at Alleviating Hallucination of Knowledge-grounded Dialogue Generation
Recently, knowledge-grounded dialogue generation models, that intentionally invoke external knowledge resources to more informative responses, are also proven to be effective in reducing hallucination.
AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
Large language models (LLMs) have fueled many intelligent agent tasks, such as web navigation -- but most existing agents perform far from satisfying in real-world webpages due to three factors: (1) the versatility of actions on webpages, (2) HTML text exceeding model processing capacity, and (3) the complexity of decision-making due to the open-domain nature of web.
ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline
Large language models (LLMs) have shown excellent mastering of human language, but still struggle in real-world applications that require mathematical problem-solving.
ChatGLM-RLHF: Practices of Aligning Large Language Models with Human Feedback
The work presents our practices of aligning LLMs with human preferences, offering insights into the challenges and solutions in RLHF implementations.
MapGuide: A Simple yet Effective Method to Reconstruct Continuous Language from Brain Activities
In contrast, we propose a simple yet effective method that guides text reconstruction by directly comparing them with the predicted text embeddings mapped from brain activities.
FORCE: Dataset and Method for Intuitive Physics Guided Human-object Interaction
Our key insight is that human motion is dictated by the interrelation between the force exerted by the human and the perceived resistance.
PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features
Three-dimensional point cloud anomaly detection that aims to detect anomaly data points from a training set serves as the foundation for a variety of applications, including industrial inspection and autonomous driving.
MulCogBench: A Multi-modal Cognitive Benchmark Dataset for Evaluating Chinese and English Computational Language Models
Results show that language models share significant similarities with human cognitive data and the similarity patterns are modulated by the data modality and stimuli complexity.
Token-Level Contrastive Learning with Modality-Aware Prompting for Multimodal Intent Recognition
To establish an optimal multimodal semantic environment for text modality, we develop a modality-aware prompting module (MAP), which effectively aligns and fuses features from text, video and audio modalities with similarity-based modality alignment and cross-modality attention mechanism.
Ranked #2 on
Multimodal Intent Recognition
on MIntRec
AlignBench: Benchmarking Chinese Alignment of Large Language Models
We will provide public APIs for evaluating AlignBench with CritiqueLLM to facilitate the evaluation of LLMs' Chinese alignment.
CharacterGLM: Customizing Chinese Conversational AI Characters with Large Language Models
In this paper, we present CharacterGLM, a series of models built upon ChatGLM, with model sizes ranging from 6B to 66B parameters.
Robust Tumor Segmentation with Hyperspectral Imaging and Graph Neural Networks
Despite the limited dataset, the GNN-based model significantly outperforms context-agnostic approaches, accurately distinguishing between healthy and tumor tissues, even in images from previously unseen patients.
Tuning In to Neural Encoding: Linking Human Brain and Artificial Supervised Representations of Language
To understand the algorithm that supports the human brain's language representation, previous research has attempted to predict neural responses to linguistic stimuli using embeddings generated by artificial neural networks (ANNs), a process known as neural encoding.
GeoDTR+: Toward generic cross-view geolocalization via geometric disentanglement
We attribute this deficiency to the lack of ability to extract the geometric layout of visual features and models' overfitting to low-level details.
Human Emotion Recognition Based On Galvanic Skin Response signal Feature Selection and SVM
A novel human emotion recognition method based on automatically selected Galvanic Skin Response (GSR) signal features and SVM is proposed in this paper.
KoLA: Carefully Benchmarking World Knowledge of Large Language Models
The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations.
Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds
Each situation corresponds to a state instance wherein a robot is potentially unable to complete a task using a solution that normally works.
LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
LLM+P takes in a natural language description of a planning problem, then returns a correct (or optimal) plan for solving that problem in natural language.
Spatial-Language Attention Policies for Efficient Robot Learning
We see a 4x improvement over baseline in mobile manipulation setting.
GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation
We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using a search engine to access the Internet knowledge.
Intra-operative Brain Tumor Detection with Deep Learning-Optimized Hyperspectral Imaging
Surgery for gliomas (intrinsic brain tumors), especially when low-grade, is challenging due to the infiltrative nature of the lesion.
Time-sensitive Learning for Heterogeneous Federated Edge Intelligence
A joint optimization algorithm is proposed to minimize the overall time consumption of model training by selecting participating edge servers, local epoch number.
Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence
We attribute this deficiency to the lack of ability to extract the spatial configuration of visual feature layouts and models' overfitting on low-level details from the training set.
Cross-View Image Sequence Geo-localization
In this paper, we present the first cross-view geo-localization method that works on a sequence of limited FOV images.
Robotic Table Wiping via Reinforcement Learning and Whole-body Trajectory Optimization
This problem is challenging, as it requires planning wiping actions while reasoning over uncertain latent dynamics of crumbs and spills captured via high-dimensional visual observations.
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization
While (Lu and Sa, 2021) have recently provided an optimal rate for non-convex stochastic decentralized optimization with weight matrices defined over linear graphs, the optimal rate with general weight matrices remains unclear.
Robot Task Planning and Situation Handling in Open Worlds
This paper introduces a novel algorithm (COWP) for open-world task planning and situation handling that dynamically augments the robot's action knowledge with task-oriented common sense.
COUCH: Towards Controllable Human-Chair Interactions
In this work, we study the problem of synthesizing scene interactions conditioned on different contact positions on the object.
Deep Class Incremental Learning from Decentralized Data
In this paper, we focus on a new and challenging decentralized machine learning paradigm in which there are continuous inflows of data to be addressed and the data are stored in multiple repositories.
Visual and Object Geo-localization: A Comprehensive Survey
The concept of geo-localization refers to the process of determining where on earth some `entity' is located, typically using Global Positioning System (GPS) coordinates.
Actor-Critic Algorithm for High-dimensional PDEs
Our model advances the state-of-the-art machine learning PDE solvers in a few aspects: 1) the trainable parameters are reduced by $N$ times, where $N$ is the number of steps to discretize the PDE in time, 2) the model convergence rate is an order of magnitude faster, 3) our model has fewer tuning hyperparameters.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
Object Tracking and Geo-localization from Street Images
Geo-localizing static objects from street images is challenging but also very important for road asset mapping and autonomous driving.
Episodic memory governs choices: An RNN-based reinforcement learning model for decision-making task
Typical methods to study cognitive function are to record the electrical activities of animal neurons during the training of animals performing behavioral tasks.
Efficient Golf Ball Detection and Tracking Based on Convolutional Neural Networks and Kalman Filter
The detection is performed on small image patches instead of the entire image to increase the performance of small ball detection.
Actor-Critic Algorithm for High-dimensional Partial Differential Equations
We develop a deep learning model to effectively solve high-dimensional nonlinear parabolic partial differential equations (PDE).
Scale Calibrated Training: Improving Generalization of Deep Networks via Scale-Specific Normalization
Experiment results show that SCT improves accuracy of single Resnet-50 on ImageNet by 1. 7% and 11. 5% accuracy when testing on image sizes of 224 and 128 respectively.
