Search Results for author:
Found 15 papers, 8 papers with code
CoinRobot: Generalized End-to-end Robotic Learning for Physical Intelligence
Physical intelligence holds immense promise for advancing embodied intelligence, enabling robots to acquire complex behaviors from demonstrations.
RoboGrasp: A Universal Grasping Policy for Robust Robotic Control
Imitation learning and world models have shown significant promise in advancing generalizable robotic learning, with robotic grasping remaining a critical challenge for achieving precise manipulation.
Spatially Visual Perception for End-to-End Robotic Learning
Our results demonstrate that our approach significantly boosts the success rate across diverse camera exposures, where previous models experience performance collapse.
Bridge-IF: Learning Inverse Protein Folding with Markov Bridges
To fill these gaps, we propose Bridge-IF, a generative diffusion bridge model for inverse folding, which is designed to learn the probabilistic dependency between the distributions of backbone structures and protein sequences.
Generalized Robot Learning Framework
Imitation based robot learning has recently gained significant attention in the robotics field due to its theoretical potential for transferability and generalizability.
Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPs
In our framework, a tensorized, rapidly trained GBDT feature gate, a DNN architecture pruning approach, as well as a vanilla back-propagation optimizer collaboratively train a randomly initialized MLP model.
Personalized Heart Disease Detection via ECG Digital Twin Generation
Heart diseases rank among the leading causes of global mortality, demonstrating a crucial need for early diagnosis and intervention.
Making Pre-trained Language Models Great on Tabular Prediction
Condensing knowledge from diverse domains, language models (LMs) possess the capability to comprehend feature names from various tables, potentially serving as versatile learners in transferring knowledge across distinct tables and diverse prediction tasks, but their discrete text representation space is inherently incompatible with numerical feature values in tables.
SERVAL: Synergy Learning between Vertical Models and LLMs towards Oracle-Level Zero-shot Medical Prediction
Recent development of large language models (LLMs) has exhibited impressive zero-shot proficiency on generic and common sense questions.
Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification
Deep learning approaches exhibit promising performances on various text tasks.
Battle of the Large Language Models: Dolly vs LLaMA vs Vicuna vs Guanaco vs Bard vs ChatGPT -- A Text-to-SQL Parsing Comparison
The success of ChatGPT has ignited an AI race, with researchers striving to develop new large language models (LLMs) that can match or surpass the language understanding and generation abilities of commercial ones.
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels
Since annotating medical images for segmentation tasks commonly incurs expensive costs, it is highly desirable to design an annotation-efficient method to alleviate the annotation burden.
Sample-efficient Multi-objective Molecular Optimization with GFlowNets
Many crucial scientific problems involve designing novel molecules with desired properties, which can be formulated as a black-box optimization problem over the discrete chemical space.
ExcelFormer: A neural network surpassing GBDTs on tabular data
In this paper, we delve into this question: Can we develop a deep learning model that serves as a "sure bet" solution for a wide range of tabular prediction tasks, while also being user-friendly for casual users?
T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction
Recent development of deep neural networks (DNNs) for tabular learning has largely benefited from the capability of DNNs for automatic feature interaction.
