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Articles récents
- Clustering Time Series with PCA and DBSCAN
- Deploy Enterprise-Ready AI with Dell PowerEdge and Intel® Gaudi® 3
- Roofline AI’s Role in Advancing Compiler Technology with oneAPI
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Neural networks news
Intel NN News
- Clustering Time Series with PCA and DBSCAN
This article shows how to perform clustering of time series data using PCA and DBSCAN.
- Deploy Enterprise-Ready AI with Dell PowerEdge and Intel® Gaudi® 3
Learn about the newly launched Dell Generative AI Solutions with Intel, powered by Dell PowerEdge […]
- Roofline AI's Role in Advancing Compiler Technology with oneAPI
Intel® Liftoff member, Roofline AI took the stage at the oneAPI DevSummit to showcase its […]
- Clustering Time Series with PCA and DBSCAN
Archives mensuelles : juillet 2022
OpenVINO™ Execution Provider + Model Caching = Better First Inference Latency for your ONNX Models
Developers can now leverage model caching through the OpenVINO™ Execution Provider for ONNX Runtime
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NEMO: A Novel Multi-Objective Optimization Method for AI Challenges
Neuroevolution-Enhanced Multi-Objective Optimization (NEMO) for Mixed-Precision Quantization delivers state-of-the-art compute speedups and memory improvements for artificial intelligence (AI) applications.
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AttentionLite: Towards Efficient Self-Attention Models for Vision
Intel Labs has created a novel framework for producing a class of parameter- and compute-efficient models called AttentionLite, which leverages recent advances in self-attention as a substitute for convolutions.
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On the Geometry of Generalization and Memorization in Deep Neural Networks
Our latest work, presented recently at the 2021 International Conference on Learning Representations (ICLR), forces a deep network to memorize some of the training examples by randomly changing their labels.
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Seat of Knowledge: AI Systems with Deeply Structured Knowledge
This blog will outline the third class in this classification and its promising role in supporting machine understanding, context-based decision making, and other aspects of higher machine intelligence.
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Best Practices for Text-Classification with Distillation Part (3/4) – Word Order Sensitivity (WOS)
In this post, I introduce a metric for estimating the complexity level of your dataset and task, and I describe how to utilize it to optimize distillation performance.
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Best Practices for Text Classification with Distillation (Part 1/4) – How to achieve BERT results by
Model distillation is a powerful pruning technique, and in many use cases, it yields significant speedup and memory size reduction.
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Learning to Optimize Memory Allocation on Hardware using Reinforcement Learning
We describe a scalable framework that combines Deep RL with genetic algorithms to search in extremely large combinatorial spaces to solve a critical memory allocation problem in hardware.
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Best Practices for Text Classification with Distillation (Part 2/4) – Challenging Use Cases
In this blog, I intend to explore this method further and investigate other test classification datasets and sub-tasks in an effort to duplicate these results.
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Bring your own dataset and retrain a TensorFlow model with OpenVINO™ toolkit
Machine learning requires us to have existing data — not the data our application will use when we run it, but data to learn from.
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