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- Beewant’s Multimodal AI: Smarter Solutions for Training, Travel, and Safety
Beewant’s cutting-edge multimodal AI redefines multimedia, driving innovative applications across […]
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Catch up on the latest Intel Innovation developer and technical content with demos, tech talks and […]
- Building AI for Low-Resource Languages: Bezoku's Innovative Approach
Bezoku, a member of the Intel® Liftoff program, is addressing the challenges of low-resource […]
- Beewant’s Multimodal AI: Smarter Solutions for Training, Travel, and Safety
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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