Imagen de Portada

Pareto-Optimal Energy Alignment for Designing Nature-Like Antibodies

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Wen, Yibo, Xu, Chenwei, Hu, Jerry Yao-Chieh, Ding, Kaize, Liu, Han
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

POLO: Preference-Guided Multi-Turn Reinforcement Learning for Lead Optimization
por: Wang, Ziqing, et al.
Publicado: (2025)

Provably Optimal Memory Capacity for Modern Hopfield Models: Transformer-Compatible Dense Associative Memories as Spherical Codes
por: Hu, Jerry Yao-Chieh, et al.
Publicado: (2024)

MolMem: Memory-Augmented Agentic Reinforcement Learning for Sample-Efficient Molecular Optimization
por: Wang, Ziqing, et al.
Publicado: (2026)

BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model
por: Xu, Chenwei, et al.
Publicado: (2024)

Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey
por: Xu, Ruiyao, et al.
Publicado: (2024)

Genome-Factory: A Library for Tuning, Deploying, and Interpreting Genomic Foundation Models
por: Wu, Weimin, et al.
Publicado: (2025)

Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphs
por: Liu, Shuhan, et al.
Publicado: (2024)

A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization
por: Wang, Ziqing, et al.
Publicado: (2025)

Attention Mechanism, Max-Affine Partition, and Universal Approximation
por: Liu, Hude, et al.
Publicado: (2025)

On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)
por: Hu, Jerry Yao-Chieh, et al.
Publicado: (2024)

On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis
por: Hu, Jerry Yao-Chieh, et al.
Publicado: (2024)

Adaptive Combinatorial Experimental Design: Pareto Optimality for Decision-Making and Inference
por: Xie, Hongrui, et al.
Publicado: (2026)

In-Context Algorithm Emulation in Fixed-Weight Transformers
por: Hu, Jerry Yao-Chieh, et al.
Publicado: (2025)

In-Context Deep Learning via Transformer Models
por: Wu, Weimin, et al.
Publicado: (2024)

Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models
por: Wu, Dennis, et al.
Publicado: (2024)

Transformer Approximations from ReLUs
por: Hu, Jerry Yao-Chieh, et al.
Publicado: (2026)

GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback
por: Xu, Ruiyao, et al.
Publicado: (2026)

AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning
por: Lou, Chenwei, et al.
Publicado: (2025)

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions
por: Hu, Jerry Yao-Chieh, et al.
Publicado: (2025)

On Flow Matching KL Divergence
por: Su, Maojiang, et al.
Publicado: (2025)

Universal Approximation with Softmax Attention
por: Hu, Jerry Yao-Chieh, et al.
Publicado: (2025)

Self-Improvement Towards Pareto Optimality: Mitigating Preference Conflicts in Multi-Objective Alignment
por: Li, Moxin, et al.
Publicado: (2025)

On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality
por: Hu, Jerry Yao-Chieh, et al.
Publicado: (2024)

Pareto-Optimal Learning from Preferences with Hidden Context
por: Bahlous-Boldi, Ryan, et al.
Publicado: (2024)

Differentially Private Kernel Density Estimation
por: Liu, Erzhi, et al.
Publicado: (2024)

On Pareto Optimality for Parametric Choice Bandits
por: Zuo, Jierui, et al.
Publicado: (2025)

Uncertainty-Aware Robust Learning on Noisy Graphs
por: Chen, Shuyi, et al.
Publicado: (2023)

Let's Ask GNN: Empowering Large Language Model for Graph In-Context Learning
por: Hu, Zhengyu, et al.
Publicado: (2024)

On Differentially Private String Distances
por: Hu, Jerry Yao-Chieh, et al.
Publicado: (2024)

Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models
por: Hu, Jerry Yao-Chieh, et al.
Publicado: (2024)

Cross-Domain Conditional Diffusion Models for Time Series Imputation
por: Zhang, Kexin, et al.
Publicado: (2025)

Nonparametric Modern Hopfield Models
por: Hu, Jerry Yao-Chieh, et al.
Publicado: (2024)

syftr: Pareto-Optimal Generative AI
por: Conway, Alexander, et al.
Publicado: (2025)

On Structured State-Space Duality
por: Hu, Jerry Yao-Chieh, et al.
Publicado: (2025)

A Theoretical Analysis of Discrete Flow Matching Generative Models
por: Su, Maojiang, et al.
Publicado: (2025)

e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration
por: Cheong, Hyunmin, et al.
Publicado: (2025)

Are Hallucinations Bad Estimations?
por: Liu, Hude, et al.
Publicado: (2025)

Tackling Fake Forgetting through Uncertainty Quantification
por: Shi, Yingdan, et al.
Publicado: (2025)

Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency
por: Hu, Jerry Yao-Chieh, et al.
Publicado: (2024)

Pareto Optimal Algorithmic Recourse in Multi-cost Function
por: Chen, Wen-Ling, et al.
Publicado: (2025)