ICML 2024PastLarge language modelsEfficiency
ICML 2024 Workshop on Efficient and Accessible Foundation Models for Biological Discovery
AccMLBio
- Submission deadline
- May 30, 2024, 12:01 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (37)
Fetched from OpenReview (v2) on 2026-06-10.
2Bits of Protein: Efficient Protein Language Models at the Scale of 2-bits
A generative foundation model for antibody sequence understanding
ABodyBuilder3: Improved and scalable antibody structure predictions
Are Protein Language Models Compute Optimal?
BioinformaticsBench: A collaboratively built large language model benchmark for Bioinformatics reasoning
Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling
Compressing the Latent Space of Single-Sequence Protein Predictors for Multimodal Generation
Cramming Protein Language Model Training in 24 GPU Hours
Enhancing Single-Cell VAE Latent Space via Semi-Supervision
Fine-tuning the ESM2 protein language model to understand the functional impact of missense variants
FusOn-pLM: A Fusion Oncoprotein-Specific Language Model via Focused Probabilistic Masking
Generative Model for Small Molecules with Latent Space RL Fine-Tuning to Protein Targets
Geometric Algebra based encoding for graph prompting
Graph2Token: Make LLMs Understand Molecule Graphs
High-Resolution In Silico Painting with Generative Models
Identifying Biological Priors and Structure in Single-Cell Foundation Models
Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry Prediction
Interactome-scale comparison of co-immunoprecipitation and yeast two-hybrid assays for protein interaction prediction
Learning Generative Population Models From Multiple Clinical Datasets Via Probabilistic Programming
Likelihood-based fine-tuning of protein language models for few-shot fitness prediction and design
MiniMol: A Parameter-Efficient Foundation Model for Molecular Learning
MolEval: An Evaluation Toolkit for Molecular Embeddings via LLMs
MSA Pairing Transfomer: protein interaction partner prediction with few-shot contrastive learning
Multi-Task Training Increases Native Sequence Recovery of Antigen-Specific T-cell Receptor Sequences
One-Versus-Others Attention: Scalable Multimodal Integration for Biomedical Data
PLUTO: Pathology-Universal Transformer
Pre-training of Single-cell Language Models through Genetic Pathway Learning
Prot2Token: A multi-task framework for protein language processing using autoregressive language modeling
ProtMamba: a homology-aware but alignment-free protein state space model
Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Enhanced Goal Directed Generation
RFamLlama: an efficient conditional language model for RNA sequence generation across diverse structural families
scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data
Simple and Effective Masked Diffusion Language Models
SWUS: Active Learning with Structure Weighted Uncertainty Score
Towards Generalizable Particle Picking in Cryo-EM Images by Leveraging Masked AutoEncoder
Training Compute-Optimal Protein Language Models
xMINT: A Multimodal Integration Transformer for Xenium Gene Imputation