263 citations · 805 across the 20 of their papers we have counts for
17 papers · 1 filter
Fast and Simplex: 2-Simplicial Attention in Triton
Aurko Roy, Timothy Chou, Sai Surya Duvvuri +5
Recent work has shown that training loss scales as a power law with both model size and the number of tokens, and that achieving compute-optimal models requires scaling model size…
A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs
Ankit Singh Rawat, Veeranjaneyulu Sadhanala, Afshin Rostamizadeh +12
A primary challenge in large language model (LLM) development is their onerous pre-training cost. Typically, such pre-training involves optimizing a self-supervised objective (such…
Layerwise Bregman Representation Learning with Applications to Knowledge Distillation
Ehsan Amid, Rohan Anil, Christopher Fifty +1
In this work, we propose a novel approach for layerwise representation learning of a trained neural network. In particular, we form a Bregman divergence based on the layer's transf…
Learning from Randomly Initialized Neural Network Features
Ehsan Amid, Rohan Anil, Wojciech Kotłowski +1
We present the surprising result that randomly initialized neural networks are good feature extractors in expectation. These random features correspond to finite-sample realization…
Step-size Adaptation Using Exponentiated Gradient Updates
Ehsan Amid, Rohan Anil, Christopher Fifty +1
Optimizers like Adam and AdaGrad have been very successful in training large-scale neural networks. Yet, the performance of these methods is heavily dependent on a carefully tuned…
Efficiently Identifying Task Groupings for Multi-Task Learning
Christopher Fifty, Ehsan Amid, Zhe Zhao +3
Multi-task learning can leverage information learned by one task to benefit the training of other tasks. Despite this capacity, naively training all tasks together in one model oft…