A survey on distributed machine learning
The demand for artificial intelligence has grown significantly over the past decade, and this
growth has been fueled by advances in machine learning techniques and the ability to …
growth has been fueled by advances in machine learning techniques and the ability to …
Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Smarter applications are making better use of the insights gleaned from data, having an
impact on every industry and research discipline. At the core of this revolution lies the tools …
impact on every industry and research discipline. At the core of this revolution lies the tools …
On the opportunities and risks of foundation models
AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
SCENIC: single-cell regulatory network inference and clustering
S Aibar, CB González-Blas, T Moerman… - Nature …, 2017 - nature.com
We present SCENIC, a computational method for simultaneous gene regulatory network
reconstruction and cell-state identification from single-cell RNA-seq data (http://scenic …
reconstruction and cell-state identification from single-cell RNA-seq data (http://scenic …
{TensorFlow}: a system for {Large-Scale} machine learning
TensorFlow is a machine learning system that operates at large scale and in heterogeneous
environments. Tensor-Flow uses dataflow graphs to represent computation, shared state …
environments. Tensor-Flow uses dataflow graphs to represent computation, shared state …
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 computation expressed using TensorFlow …
implementation for executing such algorithms. A computation expressed using TensorFlow …
Apache spark: a unified engine for big data processing
Apache Spark: a unified engine for big data processing Page 1 56 COMMUNICATIONS OF THE
ACM | NOVEMBER 2016 | VOL. 59 | NO. 11 contributed articles DOI:10.1145/2934664 This …
ACM | NOVEMBER 2016 | VOL. 59 | NO. 11 contributed articles DOI:10.1145/2934664 This …
Ray: A distributed framework for emerging {AI} applications
The next generation of AI applications will continuously interact with the environment and
learn from these interactions. These applications impose new and demanding systems …
learn from these interactions. These applications impose new and demanding systems …
Big data: A survey
In this paper, we review the background and state-of-the-art of big data. We first introduce
the general background of big data and review related technologies, such as could …
the general background of big data and review related technologies, such as could …
Splitwise: Efficient generative llm inference using phase splitting
Generative large language model (LLM) applications are growing rapidly, leading to large-
scale deployments of expensive and power-hungry GPUs. Our characterization of LLM …
scale deployments of expensive and power-hungry GPUs. Our characterization of LLM …