Search Results for author:
Found 12 papers, 4 papers with code
COMBO: Compositional World Models for Embodied Multi-Agent Cooperation
In this paper, we investigate the problem of embodied multi-agent cooperation, where decentralized agents must cooperate given only partial egocentric views of the world.
Scientific Large Language Models: A Survey on Biological & Chemical Domains
Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension, representing a significant stride toward artificial general intelligence.
InstructProtein: Aligning Human and Protein Language via Knowledge Instruction
To address this challenge, we propose InstructProtein, an innovative LLM that possesses bidirectional generation capabilities in both human and protein languages: (i) taking a protein sequence as input to predict its textual function description and (ii) using natural language to prompt protein sequence generation.
Dynamic event-triggered control for multi-agent systems with adjustable inter-event time: a moving average approach
This extended abstract presents our recent work on the leader-following consensus control for generic linear multi-agent systems.
A Hybrid Trim Strategy for Coaxial Compound Helicopter
The corresponding control variables obtained locate in a reasonable control range, with a maximum power reduced of 13% at 100m/s, which showcases the potential of the Hybrid Trim strategy.
Prompt-Guided Injection of Conformation to Pre-trained Protein Model
To incorporate conformational knowledge to PTPMs, we propose an interaction-conformation prompt (IC prompt) that is learned through back-propagation with the protein-protein interaction task.
Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models
Attacks are only performed on selected key regions and key frames to reduce the high computation cost of searching adversarial perturbations on a video due to its high dimensionality.
Learning Order Parameters from Videos of Dynamical Phases for Skyrmions with Neural Networks
The main purposes of this paper are to use neural networks for classifying the dynamical phases of some videos and to demonstrate that neural networks can learn physical concepts from them.
Cooperative Bi-path Metric for Few-shot Learning
Given base classes with sufficient labeled samples, the target of few-shot classification is to recognize unlabeled samples of novel classes with only a few labeled samples.
AMI-Net+: A Novel Multi-Instance Neural Network for Medical Diagnosis from Incomplete and Imbalanced Data
In medical real-world study (RWS), how to fully utilize the fragmentary and scarce information in model training to generate the solid diagnosis results is a challenging task.
Attention-based Multi-instance Neural Network for Medical Diagnosis from Incomplete and Low Quality Data
However, in many real-world cases, data is often of low-quality due to a variety of reasons, such as data consistency, integrity, completeness, accuracy, etc.
CNN based Multi-Instance Multi-Task Learning for Syndrome Differentiation of Diabetic Patients
Inspired from it, we employ multi-instance multi-task learning combined with the convolutional neural network (MIMT-CNN) for syndrome differentiation, which takes region proposals as input and output image labels directly.
