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Found 10 papers, 3 papers with code
OmniGenBench: A Modular Platform for Reproducible Genomic Foundation Models Benchmarking
The code of nature, embedded in DNA and RNA genomes since the origin of life, holds immense potential to impact both humans and ecosystems through genome modeling.
ChiseLLM: Unleashing the Power of Reasoning LLMs for Chisel Agile Hardware Development
Our datasets and models are publicly available, providing high-performance, cost-effective models for HCL-Based AHDM, and offering an effective baseline for future research.
RTLSquad: Multi-Agent Based Interpretable RTL Design
Optimizing Register-Transfer Level (RTL) code is crucial for improving hardware PPA performance.
Bridging Sequence-Structure Alignment in RNA Foundation Models
The alignment between RNA sequences and structures in foundation models (FMs) has yet to be thoroughly investigated.
A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments
Many real-world problems are usually computationally costly and the objective functions evolve over time.
Data-Driven Evolutionary Multi-Objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts
Data-driven evolutionary multi-objective optimization (EMO) has been recognized as an effective approach for multi-objective optimization problems with expensive objective functions.
Batched Data-Driven Evolutionary Multi-Objective Optimization Based on Manifold Interpolation
Data-driven evolutionary optimization can be used to search for a set of non-dominated trade-off solutions, where the expensive objective functions are approximated as a surrogate model.
Interactive Decomposition Multi-Objective Optimization via Progressively Learned Value Functions
In the preference elicitation session, the preference information learned in the consultation module is translated into the form that can be used in a decomposition-based EMO algorithm, i. e., a set of reference points that are biased toward to the ROI.
Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization
When solving constrained multi-objective optimization problems, an important issue is how to balance convergence, diversity and feasibility simultaneously.
Dynamic Multi-Objectives Optimization with a Changing Number of Objectives
Existing studies on dynamic multi-objective optimization focus on problems with time-dependent objective functions, while the ones with a changing number of objectives have rarely been considered in the literature.
