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Probabilistic Invariant Learning with Randomized Linear Classifiers
Leonardo Cotta, Gal Yehuda, Assaf Schuster +1
Designing models that are both expressive and preserve known invariances of tasks is an increasingly hard problem. Existing solutions tradeoff invariance for computational or memor…
Identifying the Risks of LM Agents with an LM-Emulated Sandbox
Yangjun Ruan, Honghua Dong, Andrew Wang +6
Recent advances in Language Model (LM) agents and tool use, exemplified by applications like ChatGPT Plugins, enable a rich set of capabilities but also amplify potential risks - s…
The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit
Lorenzo Noci, Chuning Li, Mufan Bill Li +4
In deep learning theory, the covariance matrix of the representations serves as a proxy to examine the network's trainability. Motivated by the success of Transformers, we study th…
Benchmarking Neural Network Training Algorithms
George E. Dahl, Frank Schneider, Zachary Nado +22
Training algorithms, broadly construed, are an essential part of every deep learning pipeline. Training algorithm improvements that speed up training across a wide variety of workl…