Search Results for author:
Found 24 papers, 15 papers with code
LLMs with Chain-of-Thought Are Non-Causal Reasoners
We further examine the factors influencing the causal structure of the implied SCM, revealing that in-context learning, supervised fine-tuning, and reinforcement learning on human feedback significantly impact the causal relations.
Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
The rise of large foundation models, trained on extensive datasets, is revolutionizing the field of AI.
Self-supervised Event-based Monocular Depth Estimation using Cross-modal Consistency
In this paper, to lower the annotation cost, we propose a self-supervised event-based monocular depth estimation framework named EMoDepth.
Efficient Quantization Strategies for Latent Diffusion Models
By treating the quantization discrepancy as relative noise and identifying sensitive part(s) of a model, we propose an efficient quantization approach encompassing both global and local strategies.
HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs
We validate the new protocol in the domains where proprietary LLMs like ChatGPT perform relatively poorly, such as Traditional Chinese Medicine.
Co-training and Co-distillation for Quality Improvement and Compression of Language Models
2) The enhanced performance of the larger model further boosts the performance of the smaller model.
An Early Evaluation of GPT-4V(ision)
In this paper, we evaluate different abilities of GPT-4V including visual understanding, language understanding, visual puzzle solving, and understanding of other modalities such as depth, thermal, video, and audio.
Multi-step prediction of chlorophyll concentration based on Adaptive Graph-Temporal Convolutional Network with Series Decomposition
The adaptive graph convolution learns the relationship between different water quality parameters, updates the state information of each parameter, and improves the learning ability of the update relationship between nodes.
Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation
Further analysis also shows that our representation learning framework can fill the semantic gap by coagulating representations of both text and graph knowledge.
Injecting Knowledge into Biomedical Pre-trained Models via Polymorphism and Synonymous Substitution
This gives us a hint that relational knowledge might not be redundant to the stored knowledge of PLMs, but rather be complementary.
HuatuoGPT, towards Taming Language Model to Be a Doctor
Experimental results demonstrate that HuatuoGPT achieves state-of-the-art results in performing medical consultation among open-source LLMs in GPT-4 evaluation, human evaluation, and medical benchmark datasets.
CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured Knowledge Aggregation
In this paper, we propose a novel context-aware graph-attention model (Context-aware GAT), which can effectively incorporate global features of relevant knowledge graphs based on a context-enhanced knowledge aggregation process.
Phoenix: Democratizing ChatGPT across Languages
This paper presents our efforts to democratize ChatGPT across language.
Natural Language Reasoning, A Survey
This survey paper proposes a clearer view of natural language reasoning in the field of Natural Language Processing (NLP), both conceptually and practically.
FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation
The great potential of unsupervised monocular depth estimation has been demonstrated by many works due to low annotation cost and impressive accuracy comparable to supervised methods.
Terminology-aware Medical Dialogue Generation
In this paper, we propose a novel framework to improve medical dialogue generation by considering features centered on domain-specific terminology.
GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots
In this work, we introduce a framework for training generalized locomotion (GenLoco) controllers for quadrupedal robots.
Up-to-Down Network: Fusing Multi-Scale Context for 3D Semantic Scene Completion
An efficient 3D scene perception algorithm is a vital component for autonomous driving and robotics systems.
Ranked #6 on
3D Semantic Scene Completion
on SemanticKITTI
Semantic Segmentation-assisted Scene Completion for LiDAR Point Clouds
Specifically, the network takes a raw point cloud as input, and merges the features from the segmentation branch into the completion branch hierarchically to provide semantic information.
Ranked #4 on
3D Semantic Scene Completion
on SemanticKITTI
SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure
LiDAR-based SLAM system is admittedly more accurate and stable than others, while its loop closure detection is still an open issue.
Uncertainty-aware INVASE: Enhanced Breast Cancer Diagnosis Feature Selection
In this paper, we present an uncertainty-aware INVASE to quantify predictive confidence of healthcare problem.
Open-set Intersection Intention Prediction for Autonomous Driving
We capture map-centric features that correspond to intersection structures under a spatial-temporal graph representation, and use two MAAMs (mutually auxiliary attention module) that cover respectively lane-level and exitlevel intentions to predict a target that best matches intersection elements in map-centric feature space.
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving.
Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets
Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning.
