13 citations · 24 across the 20 of their papers we have counts for
10 papers · 1 filter
Learning Task-Specific Antibody Representations via Function-Aware Masking
Ayan Goel, Thomas A. Walton, Amirali Aghazadeh
Antibody-specific language models pretrained via masked language modeling (MLM) learn representations that are critical for downstream sequence design and property prediction tasks…
Circuit Tracing in Autoregressive Protein Language Models
Darin Tsui, William Deinzer, Daniel Saeedi +1
Protein language models (pLMs) can generate novel protein sequences with properties beyond those observed in nature, yet the mechanisms underlying protein generation remain poorly…
On the Error-Correcting Effects of Stochasticity in Discrete Diffusion
William Yuan, Sungwon Jeong, Amirali Aghazadeh
Discrete diffusion models achieve strong performance in text and image generation, but their inference remains slow and must inherently balance sampling efficiency and sample quali…
Structure-Aware Masking for Protein Representation Learning
Thomas Walton, Ayan Goel, Amirali Aghazadeh
Masked language modeling (MLM) is the standard objective for training protein language models, typically implemented by randomly masking individual residues at a fixed rate (e.g.,…
Protein Circuit Tracing via Cross-layer Transcoders
Darin Tsui, Kunal Talreja, Daniel Saeedi +1
Protein language models (pLMs) have emerged as powerful predictors of protein structure and function. However, the computational circuits underlying their predictions remain poorly…
SpecMER: Fast Protein Generation with K-mer Guided Speculative Decoding
Thomas Walton, Darin Tsui, Aryan Musharaf +1
Autoregressive models have transformed protein engineering by enabling the generation of novel protein sequences beyond those found in nature. However, their sequential inference i…
Sparse Autoencoders for Low- Protein Function Prediction and Design
Darin Tsui, Kunal Talreja, Amirali Aghazadeh
Predicting protein function from amino acid sequence remains a central challenge in data-scarce (low-) regimes, limiting machine learning-guided protein design when only small a…
SHAP zero Explains Biological Sequence Models with Near-zero Marginal Cost for Future Queries
Darin Tsui, Aryan Musharaf, Yigit Efe Erginbas +2
The growing adoption of machine learning models for biological sequences has intensified the need for interpretable predictions, with Shapley values emerging as a theoretically gro…
Group-Structured Adversarial Training
Farzan Farnia, Amirali Aghazadeh, James Zou +1
Robust training methods against perturbations to the input data have received great attention in the machine learning literature. A standard approach in this direction is adversari…
BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear Memory
Amirali Aghazadeh, Vipul Gupta, Alex DeWeese +2
We consider feature selection for applications in machine learning where the dimensionality of the data is so large that it exceeds the working memory of the (local) computing mach…