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Explainable AI (XAI): Core ideas, techniques, and solutions
As our dependence on intelligent machines continues to grow, so does the demand for more
transparent and interpretable models. In addition, the ability to explain the model generally …
transparent and interpretable models. In addition, the ability to explain the model generally …
Distributed artificial intelligence empowered by end-edge-cloud computing: A survey
As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …
also supports artificial intelligence evolving from a centralized manner to a distributed one …
Ring attention with blockwise transformers for near-infinite context
Transformers have emerged as the architecture of choice for many state-of-the-art AI
models, showcasing exceptional performance across a wide range of AI applications …
models, showcasing exceptional performance across a wide range of AI applications …
Towards understanding biased client selection in federated learning
Federated learning is a distributed optimization paradigm that enables a large number of
resource-limited client nodes to cooperatively train a model without data sharing. Previous …
resource-limited client nodes to cooperatively train a model without data sharing. Previous …
Federated learning on non-IID data: A survey
Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …
preservation. However, models trained in federated learning usually have worse …
Privacy-preserving Byzantine-robust federated learning via blockchain systems
Federated learning enables clients to train a machine learning model jointly without sharing
their local data. However, due to the centrality of federated learning framework and the …
their local data. However, due to the centrality of federated learning framework and the …
[HTML][HTML] RNN-LSTM: From applications to modeling techniques and beyond—Systematic review
Abstract Long Short-Term Memory (LSTM) is a popular Recurrent Neural Network (RNN)
algorithm known for its ability to effectively analyze and process sequential data with long …
algorithm known for its ability to effectively analyze and process sequential data with long …
[PDF][PDF] Internlm: A multilingual language model with progressively enhanced capabilities
ILM Team - 2023 - static.aminer.cn
We present InternLM, a multilingual foundational language model with 104B parameters.
InternLM is pre-trained on a large corpora with 1.6 T tokens with a multi-phase progressive …
InternLM is pre-trained on a large corpora with 1.6 T tokens with a multi-phase progressive …
When will RNA get its AlphaFold moment?
The protein structure prediction problem has been solved for many types of proteins by
AlphaFold. Recently, there has been considerable excitement to build off the success of …
AlphaFold. Recently, there has been considerable excitement to build off the success of …
The evolution of distributed systems for graph neural networks and their origin in graph processing and deep learning: A survey
Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …
neural network architecture is capable of processing graph structured data and bridges the …