306 citations · 371 across the 10 of their papers we have counts for
6 papers · 1 filter
Scalable Optimization in the Modular Norm
Tim Large, Yang Liu, Minyoung Huh +3
To improve performance in contemporary deep learning, one is interested in scaling up the neural network in terms of both the number and the size of the layers. When ramping up the…
The Platonic Representation Hypothesis
Minyoung Huh, Brian Cheung, Tongzhou Wang +1
We argue that representations in AI models, particularly deep networks, are converging. First, we survey many examples of convergence in the literature: over time and across multip…
Training Neural Networks from Scratch with Parallel Low-Rank Adapters
Minyoung Huh, Brian Cheung, Jeremy Bernstein +2
The scalability of deep learning models is fundamentally limited by computing resources, memory, and communication. Although methods like low-rank adaptation (LoRA) have reduced th…
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks
Minyoung Huh, Brian Cheung, Pulkit Agrawal +1
This work examines the challenges of training neural networks using vector quantization using straight-through estimation. We find that a primary cause of training instability is t…
Learning to Ground Multi-Agent Communication with Autoencoders
Toru Lin, Minyoung Huh, Chris Stauffer +2
Communication requires having a common language, a lingua franca, between agents. This language could emerge via a consensus process, but it may require many generations of trial a…
The Low-Rank Simplicity Bias in Deep Networks
Minyoung Huh, Hossein Mobahi, Richard Zhang +3
Modern deep neural networks are highly over-parameterized compared to the data on which they are trained, yet they often generalize remarkably well. A flurry of recent work has ask…