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02/15/2023
The Capacity for Moral Self-Correction in Large Language Models
We test the hypothesis that language models trained with reinforcement l...
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12/15/2022
Constitutional AI: Harmlessness from AI Feedback
As AI systems become more capable, we would like to enlist their help to...
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11/04/2022
Measuring Progress on Scalable Oversight for Large Language Models
Developing safe and useful general-purpose AI systems will require us to...
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09/06/2021
Delving into Macro Placement with Reinforcement Learning
In physical design, human designers typically place macros via trial and...
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05/26/2021
A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators
The rapidly-changing deep learning landscape presents a unique opportuni...
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10/21/2020
Transferable Graph Optimizers for ML Compilers
Most compilers for machine learning (ML) frameworks need to solve many c...
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04/22/2020
Chip Placement with Deep Reinforcement Learning
In this work, we present a learning-based approach to chip placement, on...
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03/18/2020
Placement Optimization with Deep Reinforcement Learning
Placement Optimization is an important problem in systems and chip desig...
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10/16/2019
Generalized Clustering by Learning to Optimize Expected Normalized Cuts
We introduce a novel end-to-end approach for learning to cluster in the ...
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09/28/2019
GDP: Generalized Device Placement for Dataflow Graphs
Runtime and scalability of large neural networks can be significantly af...
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03/02/2019
GAP: Generalizable Approximate Graph Partitioning Framework
Graph partitioning is the problem of dividing the nodes of a graph into ...
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03/11/2017
Massive Exploration of Neural Machine Translation Architectures
Neural Machine Translation (NMT) has shown remarkable progress over the ...
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01/11/2017
