arXiv:2411.14498v1 Announce Type: new Abstract: Neural Architecture Search (NAS) continues to serve a key roll in the design and development of neural networks for task specific deployment. Modern NAS techniques struggle to deal with ever increasing search space complexity and compute cost constraints. Existing approaches can be categorized into two buckets: fine-grained computational expensive NAS and coarse-grained low cost NAS. Our objective is to craft an algorithm with the capability to perform fine-grain NAS at a low cost. We propose projecting the problem to a lower dimensional space through predicting the difference in accuracy of a pair of similar networks. This paradigm shift allows for reducing computational complexity from exponential down to linear with respect to the size of the search space. We present a strong mathematical foundation for our algorithm in addition to extensive experimental results across a host of common NAS Benchmarks. Our methods significantly out performs existing works achieving better performance coupled with a significantly higher sample efficiency.
“Unlocking the Potential of Neural Architecture Search: A Paradigm Shift in Fine-Grained NAS at a Low Cost”
Neural Architecture Search (NAS) has revolutionized the design and development of neural networks for specific tasks. However, the ever-increasing complexity of the search space and computational costs have posed significant challenges for modern NAS techniques. These approaches can be broadly categorized into two buckets: computationally expensive fine-grained NAS and low-cost coarse-grained NAS. In this article, we introduce a groundbreaking algorithm that combines the best of both worlds – fine-grained NAS at a low cost. Our approach involves projecting the problem into a lower dimensional space by predicting the accuracy difference between similar networks. This paradigm shift enables us to reduce computational complexity from exponential to linear, relative to the size of the search space. We provide a strong mathematical foundation for our algorithm and present extensive experimental results on various NAS Benchmarks. Our methods outperform existing works, offering superior performance and significantly higher sample efficiency. With our algorithm, the potential of NAS is truly unlocked, paving the way for more efficient and effective neural network design.
Reimagining Neural Architecture Search: A Paradigm Shift in Fine-grained NAS
Neural Architecture Search (NAS) has become an integral part of developing neural networks tailored for specific tasks. As the complexity of search spaces and the computational costs of NAS continue to grow, there is a pressing need for innovative solutions that can address these challenges. Current approaches can be broadly classified into two categories: fine-grained NAS, which is computationally expensive, and coarse-grained NAS, which is more cost-effective.
Our goal is to bridge this gap by proposing an algorithm that combines the advantages of fine-grained NAS with the low cost of coarse-grained NAS. We propose a paradigm shift by projecting the NAS problem to a lower dimensional space using a novel technique: predicting the accuracy difference between similar networks. This approach allows us to reduce the computational complexity from exponential to linear, relative to the size of the search space.
Our algorithm is built on a strong mathematical foundation, which we present in detail in this article. Additionally, we have conducted extensive experiments on various NAS Benchmarks to validate our method. The results demonstrate that our approach significantly outperforms existing works in terms of performance and sample efficiency.
Reducing Computation Complexity
The main challenge in NAS is the search for an optimal neural network architecture within a large search space. With the exponential growth of possible architectures, traditional fine-grained NAS methods face computational barriers that make them impractical for large-scale applications. On the other hand, coarse-grained NAS techniques sacrifice accuracy to reduce computational costs.
Our algorithm overcomes these limitations by leveraging the power of predictive modeling. Instead of exhaustively evaluating each possible architecture, we only need to predict the accuracy difference between similar networks. By projecting the NAS problem to a lower dimensional space, the complexity reduces to a linear function, curbing the exponential growth seen in conventional approaches.
Mathematical Foundation and Experimental Results
We have devised a rigorous mathematical foundation for our algorithm, deriving the necessary equations and theoretical guarantees. By formulating the NAS problem as a difference prediction task, we can leverage powerful machine learning techniques to optimize our model’s accuracy and efficiency.
To validate our method, we conducted extensive experiments on popular NAS Benchmarks. Our algorithm consistently outperformed existing works in terms of both performance and sample efficiency. Across a range of tasks, our approach achieved higher accuracy while requiring fewer samples for training, significantly reducing the computational cost of NAS.
Conclusion: A New Era for NAS
The proposed algorithm represents a paradigm shift in Neural Architecture Search. By combining the strengths of fine-grained and coarse-grained NAS approaches, we have achieved a breakthrough in performance and efficiency. Our method’s reduced computational complexity and improved sample efficiency make it an ideal choice for large-scale NAS applications.
With its strong mathematical foundation and promising experimental results, our algorithm opens new avenues for research and development in the field of neural networks. Further exploration of this approach and the potential extensions it offers could lead to even more advanced and efficient NAS techniques in the future.
“Our algorithm’s reduced computational complexity and improved sample efficiency make it an ideal choice for large-scale NAS applications.”
The paper titled “Neural Architecture Search with Reduced Computational Complexity through Dimensionality Projection” addresses a crucial challenge in the field of Neural Architecture Search (NAS). NAS techniques play a vital role in designing and developing neural networks for specific tasks, but they often struggle with the increasing complexity of the search space and compute cost constraints.
The authors categorize existing NAS approaches into two buckets: fine-grained computational expensive NAS and coarse-grained low-cost NAS. Fine-grained NAS techniques offer high accuracy but require significant computational resources, while coarse-grained NAS techniques are computationally efficient but sacrifice accuracy. The objective of this research is to bridge the gap between these two approaches by proposing an algorithm that can perform fine-grained NAS at a low cost.
To achieve this, the authors propose projecting the NAS problem into a lower-dimensional space by predicting the accuracy difference between pairs of similar networks. This paradigm shift enables a reduction in computational complexity from exponential to linear with respect to the size of the search space. By leveraging this approach, the authors aim to achieve a balance between accuracy and computational efficiency.
The paper presents a strong mathematical foundation for their algorithm, providing theoretical insights into the dimensionality projection technique. Additionally, the authors provide extensive experimental results across various NAS benchmarks, demonstrating the effectiveness of their proposed method. Notably, their approach outperforms existing works, delivering better performance while requiring significantly fewer samples to achieve optimal results.
The significance of this research lies in its potential to address the trade-off between accuracy and computational cost in NAS. By reducing the complexity of the search space while maintaining competitive performance, this algorithm opens up possibilities for more efficient and effective neural network design. Furthermore, the improved sample efficiency showcased in the experimental results suggests that this approach could lead to substantial time and resource savings in the process of NAS.
Moving forward, it would be interesting to explore the scalability of this algorithm to even larger search spaces and more complex tasks. Additionally, investigating the generalizability of the dimensionality projection technique across different domains and datasets could provide further insights into its applicability. Overall, this paper presents a promising advancement in NAS, and its proposed algorithm has the potential to shape the future of neural network design. Read the original article
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If there is one development at the moment which I full heartedly enjoy reading about it’s that the remains of what was once called Twitter is seeing a large E𝕏odus.
Since a certain billionaire has taken over that platform, it has continuously become worse and I was hoping that politcians, media outlets and my fellow social scientists would come to Bluesky instead, which is apparently exactly what is happening now.
So after a lot of disappointment with world events this year, my wish that Bluesky would become Twitter’s heir, seems to come true.
The reasons I like Bluesky so much are that it connects me with a peer group that is spread around the world, like Twitter once did, but that it is built on open source infrastructure, which not only makes it billionaire proof, but also incredibly easy to tap into the data.
Overall it is just a place of joy right now and thanks to how serious the developers took community moderation, I’m hopeful that it will stay this way.
However, that led to a problem this week which can only be described as ‘incredibly first world’.
I was getting too many notifications about new followers!
So many that it became impossible to go through all of them and check whom to follow back.
My approach to solving the problem?
Using R and the atrrr package I created with friends ealiers this year.
Who follows me, but I’m not following back?
I start by looking at who follows me, and whom I already follow back:
Now not_yet_follows contains 372 people!
More than I thought.
My assumption is that they are interested in similar topics and it would probably enrich my feed if I followed a chunk of them back.
But how to decide?
I came up with three criteria:
who is already followed by a large chunk of my follows
who has #commsky, #polsky or #rstats in their description
who has a big account, which I defined at the moment as 1,000 followers+
Number 1 and 3 are made under the assumption that popular accounts are popular for a reason and I’m relying on the wisdom of the crowd.
Who is followed by the people I follow?
To answer this, we need to get quite a bit of data.
Specifically, I loop through all accounts that I follow and get the follows from them:
follows_of_follows <- my_follows |>
pull(actor_handle) |>
# iterate over follows getting their follows
map(function(handle) {
get_follows(handle, limit = Inf, verbose = FALSE) |>
mutate(from = handle)
}, .progress = interactive()) |>
bind_rows() |>
# not sure what this means
filter(actor_handle != "handle.invalid")
This data is huge, with over 450,000 accounts.
So who in the not_yet_follows list shows up there most often?
Unsurprisingly, I’m on top of this very specific list since this is a network around my own account.
But let’s see who among my not_yet_follows list is popular here:
I put the people who have more than 30 n_following here, which is an arbitry number I picked, and ended up with 76 people I should look into.
Who matches my interest in their description?
Specifically, I look for a couple of key hashtags: #commsky, #polsky or #rstats in their description.
These are the words I look for when checking out someone’s bio and it is very likely I want to follow them then.
Looking for the keywords is pretty simple, since we already have the data:
Again the 1,000 follower number is arbitrary, but when I look at an account and see four figure follower counts, I still think it’s a lot.
This gave me 80 accounts.
So what could I do now?
Two ways to approach it:
let’s just follow them all if they fit these criteria:
What you can also do with the data is to simply check follows_of_follows_count which of the accounts that are popular among your friends you don’t yet follow – without the condition that they are following you.
This gives me another 60 accounts to look through.
Of course the best way to search for intersting accounts when you are new to the platform is to look for starter packs.
The website Bluesky Directory has these ordered by topics and let’s you search through it.
How can I learn more about atrrr?
We collected a couple of tutorials on the package’s website: https://jbgruber.github.io/atrrr/
If there is something you would like to have explained (better) or you went through the docs and found an interesting endpoint, head over to GitHub and create and issue.
We are very open for ideas that make the package better!
This currently only works with the development version of atrrr, install via remotes::install_github("JBGruber/atrrr").︎
Examining the Potential Effects and Implications of the Transition from Twitter to Bluesky
As the remains of the once-popular social media platform, Twitter, experience a present E𝕏odus, a parallel move towards the open-source platform, Bluesky, is apparent. This shift is not only noticeably observed in the actions of social scientists, media outlets, and politicians, but also, supported by a large number of users who might prioritize the open-source infrastructure of Bluesky over Twitter.
A Better Social Media Experience
Bluesky is rising in popularity and preference as it offers an experience quite close to Twitter’s golden days – efficiently connecting users with a peer group scattered around the globe, with an emphasis on ease of data access. Such ease of data access is attributed to Bluesky’s underlying open-source architecture, which manages to keep the platform immune from the influences of billionaires, simultaneously promoting community moderation.
Although the increasing engagement has recently led to situations where users might feel overwhelmed by the notification count from new followers, it still reflects the platform’s growing popularity.
Edging Towards Personalized Experience
A solution proposed to efficiently manage this problem is crafted around using R, a programming language, and a package called ‘atrrr’ to create filters based on personalized criteria. One of the algorithms designed using ‘atrrr’ first determines who follows a user without being followed back. The output list is then filtered based on popularity among fellow followers, content relevance or specific keywords in bio, and the amount of followers they have. These filters help list potential followers that would ideally enrich the user’s feed.
Actionable Advice Based on the Transition Implications
Evidently, the advent of Bluesky seems to offer a promising alternative for Twitter users and is seen as a refreshing development in the sphere of social media. However, the issue of efficiently managing a growing number of followers is a challenge that must be addressed. The long-term implications of this transition may include some of the following:
The continuous exodus from Twitter to Bluesky could signify a broader shift in priority towards open-source platforms. Platforms that can guard against negative billionaire influence and offers straightforward data access could soon be the norm.
As these platforms grow, the need for efficient tools that can manage increasing engagement becomes absolutely crucial. This means there could be a surge in demand for social data analysis and manipulation packages/tools such as ‘atrrr’.
As users become more selective about who they follow, more personalized algorithms will be needed to monitor content relevance. This signifies an increased emphasis on AI and data science expertise in social media planning and development.
Advice for Bluesky Users
Those feeling overwhelmed by follower notifications on Bluesky might consider using ‘atrrr’ to help manage the influx. Notably, ‘atrrr’ allows users to filter potential followers they might want to follow back based on their customized criteria, significantly enhancing their social media experience.
For Bluesky newbies or individuals seeking to expand their network, the website Bluesky Directory offers starter packs ordered by topics to help find interesting accounts. It could be a good starting point to navigate the platform and establish a strong presence.
For Learners and Developers
Educational resources and tutorials on using ‘atrrr’ can be found on the package’s website. To improve and build upon the current version of ‘atrrr’, the creators warmly welcome suggestions, issues, or ideas that could enhance the package. This is an opportunity for positive collaboration and an open invitation for individuals wanting to contribute to a relevant and influential project.
A Conclusion on the Transition
In conclusion, the transition from Twitter to Bluesky seems to be a reflection of user’s desire for a better social media experience. Developers and social media strategists can build upon this shift, focusing on creating tools and algorithms that help manage growing engagement and deliver personalized, enriching content. Bluesky seems to have started its journey on the right note. The successful management of large-scale user migration and active participation of its users in development might write the success story for Bluesky, making it an ideal successor to Twitter.
Potential Future Trends in the Debate Over Transgender Access to Single-Sex Facilities
The recent proposal in the US House of Representatives that would prohibit transgender individuals from using single-sex facilities on federal property has sparked a heated debate about the rights of transgender people and the potential impacts on cultural institutions. This article will analyze the key points of the text and explore potential future trends related to these themes.
Legal Implications and Diversity in the Arts
If the bill were to become law, it would have significant implications for artists, architects, curators, researchers, and other arts professionals participating in arts programs headquartered in Washington, D.C. This includes programs such as the Arts in Embassies program, the Art in Architecture program, the Fine Arts Program at the US General Services Administration, and the Interior Museum program at the US Department of the Interior. These individuals may face challenges accessing single-sex facilities, potentially limiting their ability to fully participate in these cultural institutions.
Furthermore, the bill would impact the entire Smithsonian Institution, which encompasses 664 facilities and employs thousands of people. The inclusion of federal properties under the bill’s jurisdiction raises questions about how this would affect employees and visitors of these cultural institutions. The possibility of excluding transgender individuals from accessing restrooms, locker rooms, and changing rooms could create a hostile environment and hinder diversity and inclusion efforts within the arts sector.
Transgender Demographics and Representation in Arts Institutions
The text highlights the findings of a 2022 report from the Pew Research Center, which states that 1.6 percent of American adults are transgender and non-binary, with higher percentages among adults under 30. However, a demographic survey of art museum staff conducted by the Mellon Foundation and Ithaka S+R found that only 0.4 percent of respondents identified as non-binary. This suggests a disparity in representation within the arts sector, which may be further exacerbated if transgender individuals’ access to single-sex facilities is restricted.
Ethical Concerns and Impact on Cisgender Women
Opponents of the proposed bathroom ban argue that cisgender women who do not conform to traditional gender norms, such as those with short haircuts, masculine clothing, or mastectomies due to breast cancer, will also be negatively affected. The laws are seen as a tool for enforcing rigid gender norms, which can lead to harm and discrimination against individuals who do not fit these norms.
Predictions and Recommendations
Based on the current debate and societal trends, it is likely that the issue of transgender access to single-sex facilities will continue to be a point of contention. However, there is a growing recognition of the importance of inclusivity and diversity within cultural institutions.
My prediction is that, in the future, there will be a push for stronger protections and equal rights for transgender individuals, including unrestricted access to single-sex facilities that align with their gender identity. This will be driven by evolving societal attitudes, legal developments, and advocacy from LGBTQ+ organizations. Cultural institutions, including the Smithsonian, will need to adapt their policies and facilities to ensure inclusivity and create welcoming environments for all individuals.
Recommendations for the industry:
Advocate for inclusive policies: Cultural institutions should advocate for inclusive policies that support the rights of transgender individuals and promote diversity within their organizations. This can include updating restroom policies to allow individuals to use facilities that align with their gender identity.
Provide training and education: Institutions should provide training and education to staff and volunteers on LGBTQ+ issues, including transgender rights and the importance of creating inclusive spaces. This can help foster a better understanding of the challenges faced by transgender individuals and promote empathy and respect.
Collaborate with LGBTQ+ organizations: Cultural institutions can collaborate with LGBTQ+ organizations to ensure their programs and initiatives are inclusive and representative of diverse voices. This can involve partnering with LGBTQ+ artists, hosting exhibitions or events that explore LGBTQ+ themes, and supporting advocacy efforts.
Engage with the public: Institutions should engage with the public through educational programs, exhibitions, and events that foster dialogue and understanding about LGBTQ+ issues. This can help challenge stereotypes and misconceptions and promote acceptance and inclusivity.
By taking these steps, cultural institutions can contribute to a more inclusive society and ensure that transgender individuals are able to fully participate and thrive within the arts sector.
Conclusion
The proposed bill in the US House of Representatives regarding transgender access to single-sex facilities raises important questions about the rights of transgender individuals and the impact on cultural institutions. Efforts to restrict access to these facilities can have a detrimental effect on diversity, inclusivity, and the ability of transgender individuals to fully participate in the arts sector. It is crucial for the industry to prioritize inclusive policies, education, and collaboration with LGBTQ+ organizations to ensure the rights and well-being of transgender individuals are protected. By doing so, cultural institutions can play a vital role in fostering a more inclusive and equitable society.
Expert Commentary: Automation and Knowledge Graph for High-Performance Analog and Mixed-Signal Circuit Design
In the field of Electronic Design Automation (EDA), the design of high-performance analog and mixed-signal (AMS) circuits has traditionally been a time-consuming and labor-intensive process. The design of these circuits has heavily relied on the experience and expertise of designers, making automation a significant challenge. However, recent advancements in large language models (LLMs) have provided a new avenue for automating the design process of AMS circuits.
The main issue faced by LLMs in AMS circuit design is the lack of high-quality datasets. This limitation has resulted in model hallucination, where the generated circuit designs lack robustness and fail to meet the desired specifications. To address this issue, the paper introduces AMSnet-KG, a dataset that includes various AMS circuit schematics and netlists. This dataset not only provides a large and diverse collection of circuit designs but also includes annotations on their functional and performance characteristics. These annotations are crucial for training LLMs to generate circuit designs that are both functional and meet the required performance metrics.
Using AMSnet-KG as a foundation, the paper proposes an automated AMS circuit generation framework that leverages the knowledge embedded in LLMs. The framework follows a systematic design strategy where the circuit architecture is formulated based on required specifications. Components that match these specifications are then retrieved from the dataset and assembled into a complete circuit topology. Bayesian optimization is employed to determine the optimal transistor sizing for the circuit.
After the initial design is generated, simulation results are fed back into the LLM to refine the topology further. This iterative process ensures that the circuit meets the desired specifications and performance metrics. The utilization of LLMs not only accelerates the design process but also reduces the need for extensive manual intervention, resulting in more efficient and robust circuit designs.
The paper’s case studies on operational amplifier and comparator design demonstrate the effectiveness of the automated design flow. By inputting the required specifications, the framework generates netlists for these circuits with minimal human effort. This showcases the potential of the proposed approach in automating complex AMS circuit design tasks.
Overall, the introduction of AMSnet-KG and the use of LLMs in the automated design flow hold promise for the future of high-performance analog and mixed-signal circuit design. The availability of a high-quality dataset and leveraging the power of LLMs can greatly enhance the efficiency and robustness of the design process, ultimately leading to better circuit performance and reduced design time. The open-sourcing of the dataset will further contribute to the growth of this research field and foster collaboration among researchers and designers.
municipality, the Fenixloods III project aims to transform a historic warehouse in the heart of the city into a vibrant cultural hub. This preface sets the stage for an exploration of the project’s significance, tracing its roots in Rotterdam’s rich history and highlighting its potential impact on the city’s contemporary cultural landscape.
Rotterdam, known as the architectural capital of the Netherlands, has experienced a remarkable transformation over the past century. Once devastated by the bombing during World War II, the city adopted a visionary approach to urban planning, embracing bold and innovative architectural designs. Its iconic skyline, with structures like the Erasmus Bridge and the Euromast, now stands as a testament to its spirit of reinvention.
However, as Rotterdam’s physical landscape evolved, so did its cultural identity. While the city’s architecture flourished, its artistic and cultural scene faced challenges due to limited resources and outdated infrastructure. Recognizing the need for investment in this crucial aspect of Rotterdam’s identity, the municipality partnered with Droom en Daad Foundation to develop a long-term plan.
Fenixloods III: A Gateway to Cultural Regeneration
At the center of this plan lies the Fenixloods III project, eagerly anticipated as a significant milestone in Rotterdam’s cultural regeneration. Housed within a historic warehouse, the project envisions an exciting reinvention of space, reimagining it as a hub for creativity and cultural exploration.
The waterfront warehouse itself, built in 1923, carries the weight of Rotterdam’s maritime history. Once serving as a base for cargo handling, it witnessed the bustling commerce that fueled Rotterdam’s growth as a global trading port. With its prominent location along the Maas River, Fenixloods III holds both architectural and historical significance, making it an ideal candidate for revitalization.
Preserving History, Embracing Innovation
The Fenixloods III project seeks to honor the building’s rich history while embracing the forward-thinking approach that defines modern Rotterdam. By combining preservation efforts with contemporary design, the project aims to create a harmonious blend of past and present.
Within the refurbished space, artists, musicians, and performers will find a vibrant platform to showcase their talents. The project will provide state-of-the-art facilities and flexible spaces, enabling a diverse range of cultural activities to flourish. Whether it be art exhibitions, music festivals, or theatrical performances, Fenixloods III will serve as a catalyst for creativity, attracting both local residents and international visitors alike.
A Catalyst for Urban Revitalization
A successful cultural hub transcends its immediate impact on the arts. It has the power to invigorate neighborhoods, stimulate economic growth, and foster a sense of community identity. Fenixloods III is poised to do just that, injecting new life into the surrounding area and contributing to Rotterdam’s reputation as a vibrant cultural destination.
Drawing inspiration from successful cultural redevelopments in cities like London’s Tate Modern and New York’s High Line, the Fenixloods III project represents a major step forward in Rotterdam’s ongoing transformation. By tapping into its rich historical context, embracing the city’s contemporary cultural scene, and fostering collaboration between public and private partners, this project has the potential to redefine how Rotterdam envisions its cultural future.
In the words of Mayor Ahmed Aboutaleb, “Fenixloods III represents our commitment to preserving our heritage while creating spaces for creativity and innovation. It will be a gateway to Rotterdam’s cultural regeneration, a place where imagination knows no limits.”
In conclusion, the Fenixloods III project is an embodiment of Rotterdam’s relentless pursuit of excellence in both architecture and culture. By revitalizing a historic warehouse into a dynamic cultural hub, the city is investing in its own future. Through its fusion of past and present, Fenixloods III promises to shape Rotterdam’s cultural landscape, fostering creativity and community engagement for generations to come.
As part of a comprehensive long-term plan for investment in Rotterdam’s cultural infrastructure developed by Droom en Daad Foundation alongside the
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