First UT Graduate in SLD Group

Congratulations to Dr. Guihong Li!

“What starts here changes the world.”

New Publication in IEEE T-PAMI from SLD!

We’re thrilled to announce the publication of our latest paper, “Zero-Shot Neural Architecture Search: Challenges, Solutions, and Opportunities,” in IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI). This work represents a significant step forward in the evolution of Neural Architecture Search (NAS).

Highlights:

  • Zero-Shot NAS: Our paper explores zero-shot NAS, which predicts neural network performance without training, saving time and increasing efficiency.
  • Comprehensive Review: We examine current zero-shot NAS methods, emphasizing their predictive accuracy and applications in hardware-aware scenarios.
  • Theoretical and Practical Insights: Our research delves into the theoretical basis of these methods, providing insights into the performance and interpretability of different architectures.

📢 We invite the AI community to engage with our findings and discuss how these innovations can be propelled forward. Share your thoughts and let’s collaborate on making AI more efficient and accessible!

#AI #MachineLearning #NeuralNetworks #IEEE #TPAMI #Research #Innovation #Technology #ArtificialIntelligence

Our ‘Unlearning’ Research for GenAI Highlighted in News of Cockrell School of Engineering

Excited to see our collaborative research with JPMorgan Chase on an AI unlearning algorithm spotlighted in news of Cockrell School of Engineering! Check out the article at OpenReview.

Key Points

  • Unlearning for AI: We have created an algorithm that allows generative AI models to “unlearn” information. This is especially important for trustworthy and reliable GenAI models.
  • Why it’s significant: This is a novel direction in AI research, which focuses on teaching AI systems to forget. This unlearning approach helps ensure AI doesn’t store potentially harmful or unnecessary data.
  • How it works: The algorithm teaches the AI model to forget unwanted details, allowing for alterations to images while still maintaining the image’s core identity.
  • Potential applications: This technology could be used to:
    • Remove sensitive information from images before sharing them.
    • Protect privacy
    • Combat the spread of misinformation or deepfakes
    • Avoid copyright infringement

Our ‘Unlearning’ Research for GenAI Featured in The Daily Texan Post

Excited to see our collaborative research with JPMorgan Chase on an AI unlearning algorithm spotlighted in The Daily Texan! Check out the article at OpenReview.

 

Key Points

  • Unlearning for AI: We have created an algorithm that allows generative AI models to “unlearn” information. This is especially important for trustworthy and reliable GenAI models.
  • Why it’s significant: This is a novel direction in AI research, which focuses on teaching AI systems to forget. This unlearning approach helps ensure AI doesn’t store potentially harmful or unnecessary data.
  • How it works: The algorithm teaches the AI model to forget unwanted details, allowing for alterations to images while still maintaining the image’s core identity.
  • Potential applications: This technology could be used to:
    • Remove sensitive information from images before sharing them.
    • Protect privacy
    • Combat the spread of misinformation or deepfakes
    • Avoid copyright infringement

Call for Papers: Special Section on Emerging Edge AI for Human-in-the-Loop Cyber Physical Systems

Authors are invited to submit manuscripts to the special section on Emerging Edge AI for Human-in-the-Loop Cyber Physical Systems. Relevant topics of interest to this special section include (but are not limited to):

  • Foundations of Human-in-the-Loop Cyber Physical Systems
  • EdgeAI-Human-IoT Interactions
  • Federated Learning for EdgeAI
  • Context-Aware Applications and Services
  • Cloud-edge Continuum
  • Security and Privacy
  • Prototypes, Field Experiments, Testbeds

Link: https://www.computer.org/digital-library/journals/ec/cfp-emerging-edge-ai

Cover Feature in November Special Issue from IEEE Computer

Excited to announce our latest publication: “Teaching Edge AI at the Undergraduate Level: A Hardware–Software Co-Design Approach“, our unique course taught @UTAustin as the cover feature in the November special issue from IEEE Computer @ComputerSociety.