7 citations · 16 across the 13 of their papers we have counts for
4 papers · 1 filter
Unsupervised Latent Space Alignment with Hyperspherical Geodesic Matching
Cameron Ryan, Vivek Sivaraman Narayanaswamy, Kowshik Thopalli +1
Independently trained neural networks tend to encode the same data with similar latent geometries. These latent geometries are not directly compatible, yet they can be nearly the s…
ProtAlign: Contrastive learning paradigm for Sequence and structure alignment
Aditya Ranganath, Hasin Us Sami, Kowshik Thopalli +2
Protein language models often take into consideration the alignment between a protein sequence and its textual description. However, they do not take structural information into co…
Interpretable and Steerable Concept Bottleneck Sparse Autoencoders
Akshay Kulkarni, Tsui-Wei Weng, Vivek Narayanaswamy +3
Sparse autoencoders (SAEs) promise a unified approach for mechanistic interpretability, concept discovery, and model steering in LLMs and LVLMs. However, realizing this potential r…
Revisiting Deep Subspace Alignment for Unsupervised Domain Adaptation
Kowshik Thopalli, Jayaraman J Thiagarajan, Rushil Anirudh +1
Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledge from a labeled source domain to an unlabeled target domain. Traditionally, subspace-based methods form an…