Dynamic-Max-Value ReLU Functions for Adversarially Robust Machine Learning Models

AI is everywhere now, from self-driving cars to medical diagnosis tools, doing things faster and sometimes better than humans. But there’s a big catch: AI models can be tricked. Ever heard of an adversarial attack? It’s where sneaky, small changes in input data—almost invisible to the human eye—can fool even the smartest AI into making the wrong call. Picture a stop sign with a tiny sticker that makes an AI-powered car see it as a speed limit sign instead. Scary, right?

Why ReLU? And What’s Wrong With It?

ReLU is one of those tried-and-true parts of AI—it’s super simple and makes training efficient. But it’s also a bit too open-ended, which means when adversarial noise hits, it can cause a chain reaction that messes up predictions. Previous fixes, like Static-Max-Value ReLU (S-ReLU or capped ReLU), put limits on how much an output can be, but they struggled with more complex tasks and bigger datasets.

The D-ReLU Difference

So, what’s special about D-ReLU? It’s like a smart cap for the ReLU function that adjusts itself based on the data going through the network. Think of it as a flexible safety net that can hold strong against sudden changes or “attacks” in input data without weighing the model down.

Highlights of What D-ReLU Brings to the Table:

Automatic Adjustment: Instead of a fixed output cap, D-ReLU’s limits are learnable and adapt during training. This means it finds that sweet spot where the model is strong but still accurate.
Stronger Defense, Same Great Taste: Tested against attacks like FGSM, PGD, and even Carlini-Wagner, D-ReLU came out on top. It boosts a model’s defense while keeping performance on regular data steady.

Better for Bigger Datasets: We tested it on big names like CIFAR-10, CIFAR-100, and TinyImagenet, and D-ReLU shined. Unlike older methods that struggled as data size grew, this function held up well and scaled beautifully.
No Extra Training Hassle: Unlike methods that need models to practice with a bunch of adversarial examples during training (a time-consuming process), D-ReLU doesn’t. It learns to be robust naturally, which means faster, simpler training.
Solid Performance in Real Life: Not just theoretical wins—D-ReLU performed strongly even in tests that mimic real-world conditions, where attackers don’t know all the ins and outs of the model (black-box attacks).

Why This Matters

In industries where safety and reliability are key—like autonomous vehicles, financial systems, or medical imaging—having models that are resilient to sneaky input changes can make a big difference. D-ReLU is like giving your AI an immune system upgrade: it helps fend off attacks without slowing down its regular work.

What’s Next?

We’re excited to see where this goes. D-ReLU’s ability to handle diverse data and defend against attacks opens up a lot of doors. We’re thinking about:

Tuning it for even better results,
Trying it out in NLP and audio tasks, where adversarial issues are also a thing,
Combining it with other robust training methods for even stronger models.

Want the full scoop with all the details? Check out our paper here. And if you’re working on AI models, give D-ReLU a spin and let us know what you think!

About the Authors

Korn Sooksatra is a Ph.D. student at Baylor University, specializing in Adversarial Machine Learning.

Learning Robust Observable to Address Noise in Quantum Machine Learning

In the rapidly evolving field of Quantum Machine Learning (QML), one of the most pressing challenges is handling noise—the errors that naturally arise in quantum systems, particularly in the Noisy Intermediate-Scale Quantum (NISQ) era. But what if we could teach quantum systems to “learn” and address noise head-on? Our paper “Learning Robust Observable to Address Noise in Quantum Machine Learning” explores an approach to mitigating this issue by focusing on learning robust observables. These observables can withstand the effects of noise, improving the performance of QML models in noisy environments.

Understanding the Problem of Noise in QML

In quantum systems, noise comes from imperfections in quantum gates, interactions with the environment, and decoherence—making quantum computations highly error-prone. When applying QML, this noise leads to inaccuracies in predictions and model training. This research aims to identify observables that remain invariant or change minimally even in the presence of noise, thus offering more reliable outputs from quantum systems.

The Framework: Learning Robust Observables

We propose a machine learning-based framework to find observables that are inherently resistant to various types of noise. To tackle this, we propose training a machine learning model to identify observables that remain invariant or less susceptible to noise. The model learns from the behavior of quantum states passing through noisy channels and adjusts to find robust observables that maintain their integrity despite noise. We illustrate the problem using a Bell state (a well-known quantum state), subjecting it to a depolarization channel to simulate noise.

The process can be formalized as an optimization problem where the goal is to minimize the change in the expectation value of the observable when the quantum state is subject to noise. Mathematically, this can be expressed as minimizing:

$\min⁡_{\mathcal{O}}\mathbb{E}[ \left | \langle{\psi}| \mathcal{O} |{\psi} \rangle - \langle{\psi} | \mathcal{O}_n |{\psi} \rangle ]$

Here, $\mathcal{O}$ is a Pauli-Z observable, and $\mathcal{O}_n$ is an observable we are trying to learn. The expectation value is computed before and after noise is introduced. The goal is to find an observable that minimizes this difference, effectively learning a robust observable.

A Toy Example

In our framework, we train QML models by simulating quantum systems across different noise channels, including depolarization, amplitude damping, phase damping, bit flip, and phase flip channels. The objective is to learn observables for various quantum circuits—such as Bell state circuits, Quantum Fourier Transform circuits, and highly entangled random circuits—that can remain robust across different noise levels. The framework demonstrated that it could identify an observable that better retains the state’s properties under noisy conditions, proving that robust observables can be learned effectively.

Consider the following example:

(1) $\begin{equation*} O_{optimized} = \begin{pmatrix} 0.804 & 0.086 + 0.138i & 0.739 + 0.050i & 0.070 + 0.132i\\ 0.086 - 0.138i & 0.302 & 0.087 - 0.122i & 0.277 + 0.019i \\0.739 - 0.050i & 0.087 + 0.122i & 1.253 & 0.133 + 0.215i \\ 0.070 - 0.132i & 0.277 - 0.019i & 0.133 - 0.215i & 0.470\end{pmatrix}\end{equation*}$

We computed its expectation value for Bell’s states under varying degrees of depolarization, $p \in [0,1)$ . The expectation values of the observable $O_{optimized}$ on the depolarized Bell state as a function of the depolarization rate $p$ are plotted in the following figure.

In this figure, $Z$ is the Pauli-Z matrix, $X$ is the Pauli-X matrix, $H$ is the Hadamard gate, $A$ is an arbitrary observable, and $O_{optimized}$ is a learned single qubit Hermitian measurement operator. This toy example shows that the expectation value of the custom observable $O_{optimized}$ on the depolarized Bell state remains constant as the depolarization rate $p$ increases.

Key Findings

Custom observables designed through this method demonstrated remarkable stability against noise, especially when compared to traditional observables like Pauli matrices.
In noisy channels like depolarization, the learned observables maintained a more consistent expectation value, while traditional observables exhibited greater variance.
The approach can be applied to various types of quantum circuits, making it versatile and broadly applicable in enhancing the reliability of QML models.

Implications for Quantum Machine Learning

This study offers a promising avenue for improving the accuracy and stability of QML in real-world applications. By learning robust observables, QML systems can perform more reliably, even as we contend with the inherent noise in current quantum computers. By using learned observables, the performance of quantum machine learning models can be made more stable, even when operating in the inherently noisy NISQ regime. This has implications for advancing practical applications of quantum computing, especially as we seek to scale up quantum algorithms in the near-term.

Looking Ahead: The Future of Noise-Resistant QML

The results from this paper open up exciting possibilities for future work. Imagine a future where every quantum machine learning algorithm can autonomously adjust to different noisy environments by learning which observables to trust. This would make QML models more resilient and, ultimately, more practical for real-world applications.

One immediate future direction is testing the framework on larger systems and more complex noise models. Additionally, combining this method with error correction techniques could further enhance the stability of QML algorithms.

For a detailed exploration of the methodology and findings, read the full paper at:

https://arxiv.org/pdf/2409.07632

References

Khanal, Bikram, and Pablo Rivas. “Learning Robust Observable to Address Noise in Quantum Machine Learning.” arXiv preprint arXiv:2409.07632 (2024).

About the Author

Bikram Khanal is a Ph.D. student at Baylor University, specializing in Quantum Machine Learning and Natural Language Processing.

Efficient Quantum Machine Learning with a Modified Depolarization Approach

As the quantum computing community navigates the NISQ (Noisy Intermediate-Scale Quantum) era, managing noise poses a prominent challenge, particularly in Quantum Machine Learning (QML). Quantum systems inherently exhibit noise, which can drastically impact computational accuracy. Notably, depolarization noise, a prevalent noise model in quantum computing, presents a formidable obstacle in developing efficient QML models. The paper “A Modified Depolarization Approach for Efficient Quantum Machine Learning” introduces a modified representation of the depolarization channel for a single-qubit. The proposed modified channel uses two Kraus operators based only on X and Z Pauli matrices. The approach reduces the computational complexity from six to four matrix multiplications per channel execution.

What’s Depolarization, and Why Does It Matter?

Depolarization is a noise process where a quantum state collapses, with some probability, into a mixed state, essentially scrambling the information. For example, imagine working with a quantum bit (qubit) represented by a density matrix $\rho$ . In the traditional depolarization model, noise can be introduced by applying the three Pauli matrices — X, Y, and Z to $\rho$ with equal probability. Mathematically, this looks like:

(1) $\begin{equation*} \rho \rightarrow (1 - p) \rho + \frac{p}{3} (X \rho X + Y \rho Y + Z \rho Z)\end{equation*}$

where $p$ is the probability of depolarization. Each of the Pauli operators represents a potential disturbance to the qubit. The more noise we apply, the more the system deteriorates. However, simulating this noise is computationally expensive, especially in large quantum systems, as it requires a substantial number of matrix multiplications. This is where the paper’s novel contribution shines.

The Power of Modified Depolarization Channel and Two Kraus Operators

The central innovation of this paper is an alternative representation of the depolarization channel characterized by reduced matrix multiplication operations that only use the $X$ and $Z$ Pauli matrices.

(2) $\begin{equation*} \rho_{m}' = (1 - \frac{2p}{3}) \rho + \frac{2p}{3} Z((\rho X)^T X) Z\end{equation*}$

Traditionally, depolarization uses three Kraus operators, each corresponding to one of the Pauli matrices. In practical terms, this means that when we’re simulating a quantum system with noise, we need to perform six matrix multiplications per qubit per step—this scales rapidly with the size of the system. The modified depolarization approach in the paper proposes reducing the number of Kraus operators to two by cleverly using only the $X$ and $Z$ Pauli matrices, allowing for more efficient simulation without significantly compromising the accuracy of the noise model. The two Kraus operators are defined as:

$\begin{array}{cc}K_0 = \sqrt{1 -\frac{2p}{3}} \mathbb{I}, &K_1 = i \sqrt{\frac{2p}{3}} ZX .\end{array}$

The author provides meticulous proof to assert the proposed modified expression’s authenticity and Kraus operators’ authenticity. This seemingly small change reduces the number of required matrix multiplications from six to four, a non-trivial improvement in computational cost. This reduction becomes especially significant as quantum circuits grow deeper and larger—common in QML algorithms, where we often have to run complex iterative procedures.

Experimenting with Quantum Machine Learning on the Iris Dataset

To validate their approach, the authors experimented with a well-known machine learning problem: classifying the Iris dataset using the Iris dataset by training a variational quantum circuit under a modified depolarization noise channel. Their results verify that the modified depolarization channel accurately represents channel evolution for different values of $p$ , and these results are consistent with simulation results. Once the Iris dataset was encoded into quantum states, the author trained the QML model under noisy conditions using the modified depolarization method. Thanks to Pennylane Library, the authors claim to implement the modified channel efficiently. The findings were fascinating: the computational load was reduced by 1.5 to 2 times compared to the traditional depolarization method, while classification accuracy remained comparable. This is a big deal for QML. Efficiency in quantum simulations is crucial—especially given the already limited coherence times and high noise levels of NISQ devices. Reducing computational cost allows for quicker experimentation and larger models, accelerating the development of quantum machine learning algorithms. The following figure provides the decision boundaries for readers’ reference. We request that the readers to refer to the original manuscript for in-depth analysis.

An increase in circuit depth may enhance the model’s expressivity, but it also increases its vulnerability to noise, which adversely affects the quality of the decision boundary.

Why This Matters for the NISQ Era

We’re still far from having fault-tolerant quantum computers (except google’s latest work) that can operate indefinitely without errors. For now, we must work with what we’ve got: noisy, small to mid-scale quantum devices. This means any improvement in the efficiency of noise simulation or error mitigation has a direct and significant impact on the feasibility of using quantum systems for practical problems.

The reduction in computational overhead offered by this modified depolarization approach is particularly relevant for QML, where deep quantum circuits and iterative optimization processes require substantial computational resources. This is a step toward making QML more scalable and closer to real-world applications, even within the limitations of today’s quantum technology.

Looking Ahead

As quantum hardware continues to evolve, so too will the need for more efficient noise models and error mitigation techniques. The modified depolarization approach presented in this paper offers a glimpse into how we can make QML more computationally feasible. While the improvement in noise simulation efficiency may seem small, these incremental advancements will enable the quantum systems of the future to handle more complex and meaningful tasks.

I’m excited to see how this approach will be applied to larger quantum systems and more complex QML models. The road to fully realizing quantum machine learning’s potential is long, but innovations like this bring us one step closer.

For a detailed exploration of the methodology and findings, read the full paper at: https://www.mdpi.com/2227-7390/12/9/1385

References

Khanal, Bikram, and Pablo Rivas. “A Modified Depolarization Approach for Efficient Quantum Machine Learning.” Mathematics 12.9 (2024): 1385.

About the Author

Bikram Khanal is a Ph.D. student at Baylor University, specializing in Quantum Machine Learning and Natural Language Processing.

Enhancing AI Safety: Improving Adversarial Robustness in Vision Language Models

The Research Question

How can we improve the adversarial robustness of Vision Language Models (VLMs) to ensure their safe deployment in critical applications? This question drives our exploration into focused adversarial training techniques that improve the security of these models without excessive computational costs.

Adversarial Robustness and AI Safety

Adversarial attacks involve subtle manipulations of input data designed to deceive machine learning models into making incorrect predictions. In the context of VLMs, these attacks can have severe implications, especially when these models are deployed in sensitive areas such as autonomous driving, healthcare, and content moderation.

Enhancing the adversarial robustness of VLMs is crucial for AI safety. Robust models can withstand adversarial inputs, ensuring reliable performance and preventing malicious exploitation. Our research focuses on a novel approach to achieve this robustness by selectively re-training components of the multimodal architecture.

Our Approach

Traditional methods to improve model robustness often involve adversarial training, which integrates adversarial examples into the training process. However, this can be computationally intensive, particularly for complex models like VLMs that process images and text.

Our study introduces a more efficient strategy: adversarially re-training only the language model component of the VLM. This targeted approach leverages the Fast Gradient Sign Method (FGSM) to generate adversarial examples and incorporates them into the training of the text decoder. We maintain computational efficiency by keeping the image encoder fixed while significantly enhancing the model’s overall robustness.

Key Findings

Adversarial Training Efficiency: Adversarially re-training only the language model yields robustness comparable to full adversarial training, with reduced computational demands.
Selective Training Impact: Freezing the image encoder and focusing on the text decoder maintains high performance and robustness. In contrast, training only the image encoder results in a significant performance drop.
Benchmark Results: Experiments on the Flickr8k and COCO datasets demonstrate that our selective adversarial training approach effectively mitigates the impact of adversarial attacks, as evidenced by improved BLEU scores and model performance under adversarial conditions.

Implications for Ethical AI

Our findings support the development of more robust and secure AI systems, which is crucial for ethical AI deployment. By focusing on adversarial robustness, we contribute to the broader goal of AI safety, ensuring that multimodal models can be trusted in real-world applications.

For a detailed exploration of our methodology and findings, read the full paper pre-print: https://arxiv.org/abs/2407.21174

References

Rashid, M.B., & Rivas, P. (2024). AI Safety in Practice: Enhancing Adversarial Robustness in Multimodal Image Captioning. 3rd Workshop on Ethical Artificial Intelligence: Methods and Applications, ACM SIGKDD’24. https://arxiv.org/abs/2407.21174

About the Author

Maisha Binte Rashid is a Ph.D. student at Baylor University, specializing in AI safety and multimodal machine learning.

Standard IEEE 7014

Sharing that IEEE 7014-2024: IEEE Standard for Ethical Considerations in Emulated Empathy in Autonomous and Intelligent Systems has been published!

This standard is the result of five years of dedication and collaboration by a diverse group of global experts, and Dr. Rivas has contributed at different stages. The journey was marked by passionate discussions, varied perspectives, and a unified goal of fostering ethical and responsible AI development.

As AI technology becomes increasingly powerful and integral to our daily lives, IEEE 7014-2024 represents a crucial step towards ensuring that these systems are developed with ethical considerations at the forefront.

Accessing the Standard

The full text of IEEE 7014-2024 can be viewed and purchased here: IEEE 7014. Additionally, free access may soon be available via the IEEE GET Program: IEEE GET Program, although this is currently to be confirmed.

Acknowledgments

A huge thank you to Ben Bland and all the wonderful people who contributed to this project. We worked together to reach a consensus and have made a significant contribution to the future of AI technology.

This publication is a testament to the power of collaboration and the shared vision of building a brighter technological future for humanity and our planet.

Final Thoughts

The publication of IEEE 7014-2024 is a proud moment for all who have been involved, including our very own Dr. Rivas. It underscores the importance of considering ethical implications in AI development and sets a precedent for future advancements in the field. We look forward to seeing how this standard will influence the development of AI systems that are not only intelligent but also empathetic and ethically sound.

Uncovering Patterns in Car Parts – A Step Towards Combating a Cybercrime

The black market for stolen car parts is a significant problem, exacerbated by the rise of online marketplaces like Craigslist or OfferUp, where stolen goods are often sold under the radar. In response to this growing issue, our research team at Baylor University has been leveraging cutting-edge AI techniques to detect patterns in car part sales that could signal illicit activity. This work is part of the NSF-funded Research Experiences for Undergraduates (REU) program, which provides undergraduate students with hands-on research experience in critical areas like artificial intelligence. Our project, supported by NSF Grant #2210091, investigates the potential of deep learning models to analyze vast amounts of data from online listings, offering a new tool in the fight against stolen car parts.

Why This Research Matters

The theft and resale of car parts not only affect vehicle owners but also contribute to organized crime. Detecting patterns in how stolen parts are sold online can help law enforcement track and dismantle these criminal networks. This project also presents a unique challenge to the AI research community: the complexity of analyzing unstructured, noisy data from real-world platforms. By utilizing the Vision Transformer (ViT) for image analysis, our research offers a different approach compared to previous works that employed multimodal models like ImageBind and OpenFlamingo.

Dataset and Embedding Extraction

Our dataset comprises thousands of car parts advertisements scraped from Craigslist and OfferUp, each including images and textual descriptions. To process the image data, we used the Vision Transformer (ViT), a model pre-trained on ImageNet-21k. ViT processes images by splitting them into 16×16-pixel patches, allowing for the extraction of key features from each image. These features were converted into embeddings—high-dimensional vectors that represent each image’s content in a form that the model can analyze.

We extracted embeddings for nearly 85,000 images, which were then compiled into a CSV file for further analysis, including clustering and visualization. Unlike prior works by Hamara & Rivas (2024) and Rashid & Rivas (2024), which utilized multimodal models like ImageBind and OpenFlamingo to fuse image and text data, we focused solely on image embeddings in this phase to assess the effectiveness of ViT in capturing visual patterns related to illicit activities.

Clustering and Evaluation

With the embeddings extracted, we used UMAP (Uniform Manifold Approximation and Projection) to project the high-dimensional data into a more interpretable 2D space for visualization. We then applied K-Means clustering, a widely used algorithm for grouping data, and experimented with different embedding dimensions—16, 32, 64, and 128—to identify the optimal number of clusters.

Among these, 64 dimensions proved to be the best suited for our dataset, as determined by three key clustering performance metrics:

Silhouette Score: Measures how similar an object is to its own cluster compared to other clusters. A value of 0.015 indicated that some clusters were poorly defined.
Calinski-Harabasz Index: Evaluates the variance ratio between clusters versus within clusters.
Davies-Bouldin Index: Measures the average similarity between each cluster and its most similar cluster.

Although 128 dimensions performed well in some tests, 64 dimensions provided the clearest balance between cluster purity and computational efficiency. The low silhouette score, while indicating some overlap between clusters, helped confirm that most clusters were well-defined, despite several outliers—posts that displayed mixed or unclear features, such as images showing both powertrains and vehicle exteriors.

Findings and Analysis

Using the K-Means algorithm, we identified 20 distinct clusters, each representing different categories of car parts. Here are some key findings:

Cluster 0: Primarily contained exterior shots of full vehicles.
Cluster 1: Featured exterior components like mirrors and bumpers.
Cluster 2: Focused on powertrain parts such as engines and transmissions.
Cluster 3: Showcased body panels including doors, trunks, and hoods.
Cluster 4: Grouped images of towing accessories like trailer hitches.

After clustering, we applied K-Nearest Neighbors (KNN) to identify the top 10 posts nearest to each cluster centroid, which allowed us to analyze representative posts and confirm the coherence of each cluster. Despite the general success of this approach, outliers emerged in the UMAP visualization, indicating the need for further refinement to handle posts with mixed features. This challenge is common in image analysis, particularly when models rely solely on visual data without the contextual information that multimodal models can provide.

Comparative Analysis with Prior Work

Our approach contrasts with that of Hamara & Rivas (2024) and Rashid & Rivas (2024), who utilized multimodal models like ImageBind and OpenFlamingo to integrate image and text data for enhanced analysis. While their methods leveraged the fusion of multiple data types to capture richer context, we aimed to assess the capabilities of ViT in isolating visual patterns indicative of illicit activity. This comparison highlights the trade-offs between focusing on single-modality models versus multimodal approaches in detecting complex patterns within unstructured data.

Broader Impact

This research demonstrates the potential of AI in analyzing large, unstructured datasets from online marketplaces, providing law enforcement with new tools to monitor and track stolen car parts. From a technical perspective, our project highlights the effectiveness of using ViT for image analysis in this context. As we continue refining our models and consider integrating multimodal approaches inspired by prior work, our collaboration with crosdisciplinary partners will ensure that this system becomes a valuable tool for combating the sale of stolen goods online.

As stated previously, the silhouette score for the dataset proved to be notably small, which was supported by the visualization containing numerous outliers. This may be attributed to clusters lacking clear definition, meaning that several posts contained images without many distinguishable features. This is understandable considering that while clusters emphasized a focus on specific car parts, many images still displayed various other vehicle components. For instance, although Cluster 2 primarily featured images of powertrains, the posts in this cluster also included shots of the exterior and body panels of the vehicle. This is logical as sellers often aim to showcase multiple facets of the vehicle when listing it, explaining the lack of focus on specific car parts.

About the Author

Cameron Armijo is a Computer Science undergraduate student at Baylor University, specializing in data mining.

International Conference on Emergent and Quantum Technologies (ICEQT’24)

July 22-25, 2024 — Las Vegas, NV

Dear Esteemed Colleagues,

Quantum computing is an expeditiously evolving field of interdisciplinary research, drawing upon fundamental principles from mathematics, physics, and engineering. To maintain scientific rigor and foster advancement, this domain necessitates a collaborative effort across various STEM disciplines.

We are delighted to announce the International Conference on Emergent and Quantum Technologies (ICEQT’24), scheduled for July 22-25, 2024, in Las Vegas, NV. The conference is designed to serve as a platform for researchers specializing in quantum machine learning and machine learning professionals exploring the application of AI in enhancing quantum computing algorithms. It aims to facilitate the exchange of insights and developments within these dynamic areas of study.

The burgeoning interest among machine learning practitioners in leveraging AI for quantum computing endeavors, and vice versa, underscores the relevance of this conference. Thus, we warmly welcome the submission of original research papers that contribute novel insights and state-of-the-art developments in the following areas of interest:

Foundations of Quantum Computing and Quantum Machine Learning

Quantum computing models and paradigms, e.g., Grover, Shor, and others
Quantum algorithms for Linear Systems of Equations
Quantum Tensor Networks and their Applications in QML

Quantum Machine Learning Algorithms

Quantum Neural Networks
Quantum Hidden Markov Models
Quantum PCA
Quantum SVM
Quantum Autoencoders
Quantum Transfer Learning
Quantum Boltzmann machines
Theory of Quantum-enhanced Machine Learning

AI for Quantum Computing

Machine learning for improved quantum algorithm performance
Machine learning for quantum control
Machine learning for building better quantum hardware

Quantum Algorithms and Applications

Quantum computing: models and paradigms
Quantum algorithms for hyperparameter tuning (Quantum computing for AutoML)
Quantum-enhanced Reinforcement Learning
Quantum Annealing
Quantum Sampling
Applications of Quantum Machine Learning

Fairness and Ethics in Quantum Machine Learning

We look forward to receiving your submissions and to welcoming you to ICEQT’24.

All submissions that are accepted for presentation will be included in the proceedings published by IEEE CPS. To ensure consistency in formatting, authors should follow the general typesetting instructions available on the IEEE’s website, including single-line spacing and a 2-column format. Additionally, authors of accepted papers must agree to the IEEE CPS standard statement regarding copyrights and policies on electronic dissemination.

Prospective authors are encouraged to submit their papers through the conference’s evaluation website at CMT. More information about the conference, including submission guidelines, can be found on our website at https://baylor.ai/iceqt/.

Important Deadlines

March 22, 2024: Submission of papers: https://cmt3.research.microsoft.com/ICEQT2024
– Full/Regular Research Papers (maximum of 8 pages)
– Short Research Papers (maximum of 5 pages)
– Abstract/Poster Papers (maximum of 3 pages)

April 15, 2024: Notification of acceptance (+/- two days)

May 1, 2024: Final papers + Registration

June 21, 2024: Last day for hotel room reservation at a discounted price.

July 22-25, 2024: The 2024 World Congress in Computer Science, Computer Engineering, and Applied Computing (CSCE’24: USA)
Which includes the International Conference on Emergent and Quantum Technologies (ICEQT’24)

Chairs:
Pablo Rivas, PhD, Baylor University
Bikram Khanal, PhD Candidate, Baylor University

Celebrating Love and Innovation at The Lab: Welcome, PoderOso!

This Valentine’s Day at Baylor.AI, we’re not just celebrating love in the air but also the arrival of our latest powerhouse, affectionately named PoderOso. This state-of-the-art equipment is a testament to the unwavering support and vision of Dr. Greg Hamerly, the department chair of Computer Science at Baylor, and Dr. Daniel Pack, the dean of the School of Engineering and Computer Science. Their dedication to advancing research and innovation within our department has been instrumental in acquiring PoderOso, and for that, we are profoundly grateful.

The name ‘PoderOso’ is derived from Spanish, where ‘Poder’ means ‘Power’ and ‘Oso’ means ‘Bear’. Combined, ‘Poderoso’ translates to ‘Powerful’. Therefore, ‘PoderOso’ creatively merges these concepts to symbolize something that embodies both power and the strength of a bear, aptly reflecting the capabilities of machine.

PoderOso is a technological marvel boasting dual EPYC 7662 processors, a whopping 1024GB of DDR4-3200 ECC memory, cutting-edge storage solutions, and six NVIDIA L40S GPUs. It’s a beast designed for in-house AI research, setting a new benchmark for what we can achieve.

With PoderOso’s impressive specs, our team is poised to push the boundaries of deep learning faster than ever before. From advancing language models that can understand and generate human-like text to developing computer vision systems that can perceive the world as we do; from enhancing adversarial robustness to securing AI against malicious attacks to exploring the burgeoning field of quantum machine learning and driving forward multimodal AI research that integrates multiple types of data, PoderOso will be at the heart of our endeavors. Moreover, it will enable us to delve deeper into AI ethics, ensuring our advancements are aligned with our values and societal needs.

As we unbox PoderOso and get it up and running, we’re filled with anticipation for future breakthroughs. Below are photos of the unboxing and our dedicated IT team in front of the rack.

Our journey into the next frontier of AI research has just gotten a significant boost, thanks to PoderOso and the incredible support of our leaders. Here’s to a future where our research leads to technological advancements and fosters a more ethical, understanding, and inclusive world.

Happy Valentine’s Day to our Baylor.AI family and everyone supporting us on this exciting journey!

(Left to right) Brian Sitton, Mike Hutcheson, Pablo Rivas

Creation and Analysis of an NLU Dataset for DoD Cybersecurity Policies

Comprehending and implementing robust policies is crucial in cybersecurity. In our lab, Ernesto Quevedo et al. recently released a paper, Creation and Analysis of a Natural Language Understanding Dataset for DoD Cybersecurity Policies (CSIAC-DoDIN V1.0), which introduces a groundbreaking dataset to aid in this endeavor. This dataset bridges a significant gap in Legal Natural Language Processing (NLP) by providing structured data specifically focused on cybersecurity policies.

Dataset Overview

The CSIAC-DoDIN V1.0 dataset encompasses a wide array of cybersecurity-related policies, responsibilities, and procedures from the Department of Defense (DoD). Unlike existing datasets that focus primarily on privacy policies, this dataset includes detailed guidelines, strategies, and procedures essential for cybersecurity.

Key Contributions

Novel Dataset: This dataset is the first to include comprehensive cybersecurity policies, guidelines, and procedures.
Baseline Models: The paper provides baseline performance metrics using transformer-based models such as BERT, RoBERTa, Legal-BERT, and PrivBERT.
Open Access: The dataset and code are publicly available, encouraging further research and collaboration.

Experiments and Results

Our team of researchers evaluated several transformer-based models on this dataset:

BERT: Demonstrated strong performance across various tasks.
RoBERTa: Showed competitive results, particularly in classification tasks.
Legal-BERT: Excelled in domain-specific tasks, benefiting from its legal data pre-training.
PrivBERT: Provided insights into the transferability of models across different policy subdomains.

Download

Access the CSIAC-DoDIN V1.0 dataset here to explore it and contribute to the advancement of Legal NLP. Join the effort to enhance cybersecurity policy understanding and implementation using cutting-edge NLP models. Download the paper here to learn more about the process.

Gabor Filters as Initializers for Convolutional Neural Networks: A Study on Inductive Bias and Performance on Image Classification

Rivas, Pablo, and Mehang Rai. 2023. “Enhancing CNNs Performance on Object Recognition Tasks with Gabor Initialization” Electronics 12, no. 19: 4072. https://doi.org/10.3390/electronics12194072

Our latest journal article, authored by Baylor graduate and former Baylor.AI lab member Mehang Rai, MS, marks an advancement in Convolutional Neural Networks (CNNs). The paper, titled “Enhancing CNNs Performance on Object Recognition Tasks with Gabor Initialization,” has not only garnered attention in academic circles but also achieved the prestigious Best Poster Award at the LXAI workshop at ICML 2023, a top-tier conference in the field.

Pablo Rivas and Mehang Rai, ” Gabor Filters as Initializers for Convolutional Neural Networks: A Study on Inductive Bias and Performance on Image Classification “, in The LXAI Workshop @ International Conference on Machine Learning (ICML 2023), 7/2023.

A Journey from Concept to Recognition Our journey with this research began with early discussions and progress shared here. The idea was simple yet profound: exploring the potential of Gabor filters, known for their exceptional feature extraction capabilities, in enhancing the performance of CNNs for object recognition tasks. This exploration led to a comprehensive study comparing the performance of Gabor-initialized CNNs against traditional CNNs with random initialization across six object recognition datasets.

Key Findings and Contributions The results were fascinating to us. The Gabor-initialized CNNs consistently outperformed traditional models in accuracy, area under the curve, minimum loss, and convergence speed. These findings provide robust evidence in favor of using Gabor-based methods for initializing the receptive fields of CNN architectures, a technique that was explored before with little success because researchers had been constraining Gabor filters during training, precluding gradient descent to optimize the filters as needed for general purpose object recognition, until now.

Our research contributes significantly to the field by demonstrating:

Improved performance in object classification tasks with Gabor-initialized CNNs.
Superior performance of random configurations of Gabor filters in the receptive layer, especially with complex datasets.
Enhanced performance of CNNs in a shorter time frame when incorporating Gabor filters.

Implications and Future Directions This study reaffirms the historical success of Gabor filters in image processing and opens new avenues for their application in modern CNN architectures. The impact of this research is vast, suggesting potential enhancements in various applications of CNNs, from medical imaging to autonomous vehicles.

As we celebrate this achievement, we also look forward to further research. Future studies could explore initializing other vision architectures, such as Vision Transformers (ViTs), with Gabor filters.

It’s a proud moment for us at the lab to see our research recognized on a global platform like ICML 2023 and published in a journal. This accomplishment is a testament to our commitment to pushing the boundaries of AI and ML research. We congratulate Mehang Rai for this remarkable achievement and thank the AI community for their continued support and recognition.