research
∙
07/20/2023
Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification
Recent work has shown that language models' (LMs) prompt-based learning ...
share
research
∙
02/15/2023
The Capacity for Moral Self-Correction in Large Language Models
We test the hypothesis that language models trained with reinforcement l...
research
∙
12/15/2022
Constitutional AI: Harmlessness from AI Feedback
As AI systems become more capable, we would like to enlist their help to...
research
∙
11/04/2022
Measuring Progress on Scalable Oversight for Large Language Models
Developing safe and useful general-purpose AI systems will require us to...
research
∙
01/21/2022
Representing Long-Range Context for Graph Neural Networks with Global Attention
Graph neural networks are powerful architectures for structured datasets...
research
∙
12/07/2021
A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules
Multi-Chip-Modules (MCMs) reduce the design and fabrication cost of mach...
research
∙
09/06/2021
Delving into Macro Placement with Reinforcement Learning
In physical design, human designers typically place macros via trial and...
research
∙
05/26/2021
A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators
The rapidly-changing deep learning landscape presents a unique opportuni...
research
∙
01/06/2021
FLGUARD: Secure and Private Federated Learning
Recently, a number of backdoor attacks against Federated Learning (FL) h...
research
∙
10/21/2020
Transferable Graph Optimizers for ML Compilers
Most compilers for machine learning (ML) frameworks need to solve many c...
research
∙
04/22/2020
Chip Placement with Deep Reinforcement Learning
In this work, we present a learning-based approach to chip placement, on...
research
∙
03/18/2020
Placement Optimization with Deep Reinforcement Learning
Placement Optimization is an important problem in systems and chip desig...
research
∙
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 ...
research
∙
09/28/2019
GDP: Generalized Device Placement for Dataflow Graphs
Runtime and scalability of large neural networks can be significantly af...
research
∙
06/16/2019
Reinforcement Learning Driven Heuristic Optimization
Heuristic algorithms such as simulated annealing, Concorde, and METIS ar...
research
∙
03/02/2019
GAP: Generalizable Approximate Graph Partitioning Framework
Graph partitioning is the problem of dividing the nodes of a graph into ...
research
∙
06/05/2018
Deep Mixture of Experts via Shallow Embedding
Larger networks generally have greater representational power at the cos...
research
∙
06/13/2017
Device Placement Optimization with Reinforcement Learning
The past few years have witnessed a growth in size and computational req...
research
∙
01/23/2017
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
The capacity of a neural network to absorb information is limited by its...
research
∙
05/19/2015