4 papers
Agent in a Bottle: Can LLM Agents Turn Their Capabilities Into Cheap, Scalable Artifacts?
Ankit Sonthalia, Haritz Puerto, Alexander Rubinstein +2
Large language models (LLMs) can solve many narrow tasks, but querying them separately for millions of related instances can be prohibitively expensive. Can LLM agents autonomously…
Sparse Autoencoders enable Robust and Interpretable Fine-tuning of CLIP models
Fabian Morelli, Arnas Uselis, Ankit Sonthalia +1
Large-scale pre-trained vision-language models like CLIP demonstrate remarkable zero-shot performance across diverse tasks. However, fine-tuning these models to improve downstream…
On the rankability of visual embeddings
Ankit Sonthalia, Arnas Uselis, Seong Joon Oh
We study whether visual embedding models capture continuous, ordinal attributes along linear directions, which we term _rank axes_. We define a model as _rankable_ for an attribute…
Do Deep Neural Network Solutions Form a Star Domain?
Ankit Sonthalia, Alexander Rubinstein, Ehsan Abbasnejad +1
It has recently been conjectured that neural network solution sets reachable via stochastic gradient descent (SGD) are convex, considering permutation invariances (Entezari et al.,…