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Found 8 papers, 3 papers with code
PaLM 2 Technical Report
Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM.
Ranked #1 on
Question Answering
on TriviaQA
(using extra training data)
PaLM: Scaling Language Modeling with Pathways
To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM.
Ranked #1 on
Natural Language Inference
on RTE
Pathways: Asynchronous Distributed Dataflow for ML
We present the design of a new large scale orchestration layer for accelerators.
Dynamic Control Flow in Large-Scale Machine Learning
We describe the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system.
TensorFlow: A system for large-scale machine learning
TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.
A Multi-View Embedding Space for Modeling Internet Images, Tags, and their Semantics
This paper investigates the problem of modeling Internet images and associated text or tags for tasks such as image-to-image search, tag-to-image search, and image-to-tag search (image annotation).
Continuously-adaptive discretization for message-passing algorithms
Continuously-Adaptive Discretization for Message-Passing (CAD-MP) is a new message-passing algorithm employing adaptive discretization.
