Can you explain how integral neural networks
Posted: Thu Apr 17, 2025 9:24 am
This led me to understand that automation in AI optimization is about ensuring speed without sacrificing quality. One of the algorithms I developed and patented became essential for Huawei, especially when they had to switch from Kirin to Qualcomm processors due to sanctions. It allowed the team to quickly adapt neural networks to Qualcomm architecture without losing performance or accuracy.
By simplifying and automating the process, we reduced development time from over a year to just a few months. This had a huge impact on a product used by millions of people and shaped my approach to optimization, focusing on speed, efficiency, and minimal loss of quality. This is the mindset I bring to ANNA today.
Your research has been presented at CVPR and ECCV: what are the main innovations in AI efficiency that you are most proud of?
When asked about my successes in AI efficiency, I always think back to our paper that was selected for an oral presentation at CVPR 2023. Being chosen for an oral presentation at such a conference is rare, as only 12 papers are selected. This is in addition to the fact that generative AI generally dominates the scene, and our paper took a different approach, focusing on the mathematical side, specifically on neural network analysis and compression.
We developed a method that helped us understand how many parameters a neural network actually needs to work efficiently. By applying functional analysis techniques and switching from a discrete to a continuous formulation, we were able to obtain good compression results, while maintaining the ability to integrate these changes into the model. The paper also introduced several new algorithms not yet used by the community and which have found further application.
This was one of my first papers in AI and, importantly, it was the result of a collective effort from our team, including my co-founders. It was a significant milestone for all of us.
(INNs) work and why they represent an important innovation in the 99 acres database field of deep learning?
Traditional neural networks use fixed matrices, similar to Excel tables, where the dimensions and parameters are predetermined. INNs, on the other hand, describe networks as continuous functions, offering much more flexibility. Think of a blanket with pins at different heights: this represents the continuous wave.
What makes INNs interesting is their ability to dynamically "squeeze" or "expand" based on available resources, similar to how an analog signal is digitized into sound. You can shrink the network without sacrificing quality, and when necessary, expand it again without having to retrain.
We have tested this solution and while traditional compression methods result in significant quality loss, INNs maintain close to the original quality even under extreme compression. The underlying math is less conventional for the AI community, but the real value lies in its ability to provide tangible, actionable results with minimal effort.
TheStage AI has been working on quantum annealing algorithms: how do you think quantum computing will play a role in AI optimization in the near future?
When it comes to quantum computing and its role in AI optimization, the key point is that quantum systems offer a completely different approach to solving problems like optimization. While we haven’t invented quantum annealing algorithms from scratch, companies like D-Wave provide Python libraries for developing quantum algorithms specifically for discrete optimization tasks, which are ideal for quantum computers.
By simplifying and automating the process, we reduced development time from over a year to just a few months. This had a huge impact on a product used by millions of people and shaped my approach to optimization, focusing on speed, efficiency, and minimal loss of quality. This is the mindset I bring to ANNA today.
Your research has been presented at CVPR and ECCV: what are the main innovations in AI efficiency that you are most proud of?
When asked about my successes in AI efficiency, I always think back to our paper that was selected for an oral presentation at CVPR 2023. Being chosen for an oral presentation at such a conference is rare, as only 12 papers are selected. This is in addition to the fact that generative AI generally dominates the scene, and our paper took a different approach, focusing on the mathematical side, specifically on neural network analysis and compression.
We developed a method that helped us understand how many parameters a neural network actually needs to work efficiently. By applying functional analysis techniques and switching from a discrete to a continuous formulation, we were able to obtain good compression results, while maintaining the ability to integrate these changes into the model. The paper also introduced several new algorithms not yet used by the community and which have found further application.
This was one of my first papers in AI and, importantly, it was the result of a collective effort from our team, including my co-founders. It was a significant milestone for all of us.
(INNs) work and why they represent an important innovation in the 99 acres database field of deep learning?
Traditional neural networks use fixed matrices, similar to Excel tables, where the dimensions and parameters are predetermined. INNs, on the other hand, describe networks as continuous functions, offering much more flexibility. Think of a blanket with pins at different heights: this represents the continuous wave.
What makes INNs interesting is their ability to dynamically "squeeze" or "expand" based on available resources, similar to how an analog signal is digitized into sound. You can shrink the network without sacrificing quality, and when necessary, expand it again without having to retrain.
We have tested this solution and while traditional compression methods result in significant quality loss, INNs maintain close to the original quality even under extreme compression. The underlying math is less conventional for the AI community, but the real value lies in its ability to provide tangible, actionable results with minimal effort.
TheStage AI has been working on quantum annealing algorithms: how do you think quantum computing will play a role in AI optimization in the near future?
When it comes to quantum computing and its role in AI optimization, the key point is that quantum systems offer a completely different approach to solving problems like optimization. While we haven’t invented quantum annealing algorithms from scratch, companies like D-Wave provide Python libraries for developing quantum algorithms specifically for discrete optimization tasks, which are ideal for quantum computers.