1 citations · 1 across the 5 of their papers we have counts for
5 papers
When Does Continual Learning Require Learning
Anne Harrington, Nayan Saxena, Michael Murphy +7
As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn? Today, the field largely frames this as a problem o…
LFM2 Technical Report
Alexander Amini, Anna Banaszak, Harold Benoit +30
We present LFM2, a family of Liquid Foundation Models designed for efficient on-device deployment and strong task capabilities. Using hardware-in-the-loop architecture search under…
Don't Think of the White Bear: Ironic Negation in Transformer Models Under Cognitive Load
Logan Mann, Nayan Saxena, Sarah Tandon +3
Negation instructions such as 'do not mention ' can paradoxically increase the accessibility of in human thought, a phenomenon known as ironic rebound. Large language models…
Inference-Time Chain-of-Thought Pruning with Latent Informativeness Signals
Sophie Li, Nicholas Huang, Nayan Saxena +4
Large language models (LLMs) improve reasoning accuracy when generating multiple candidate solutions at test time, but standard methods like Best-of-N (BoN) incur high computationa…
ToDo: Token Downsampling for Efficient Generation of High-Resolution Images
Ethan Smith, Nayan Saxena, Aninda Saha
Attention mechanism has been crucial for image diffusion models, however, their quadratic computational complexity limits the sizes of images we can process within reasonable time…