Chace Caven’s Post

🧠 Unveiling the Mysteries of Emotional Response Modeling 🌟 Dive into the realm of computational modeling of emotional responses with a groundbreaking study using neural language models to produce synthetic self-report data. 🤯 Key Findings: - Exploration of emotional responses through the PANAS questionnaire using GPT-3 variants. - The size of the model directly impacts the human-likeness of the generated data. - The Davinci model variant stands out for its exceptional human-like data generation. Discover how modern technology is reshaping our understanding of emotional behavior and paving the way for innovative advancements in user interaction modeling. 💡 #EmotionalResponse #NeuralNetworks #UserModeling #TechInnovation https://lnkd.in/eFjVcTZH

I am happy to share that our work on Parameter Efficient Vision Transformers for audio-visual speaker verification has been accepted at the Workshop on Efficient Natural Language and Speech Processing (ENLSP) at the 38th Neural Information processing Systems (NeurIPS) #NeurIPS. In this work, we have explored the prospect of leveraging Vision Transformers (pretrained on image data) using audio-visual adapters for speaker verification. The link for the paper will be provided soon. #NeurIPS2024 #audiovisual #speakerverification #ENLSP #neurips2024 #machinelearning #artificialintelligence

Engaging in a hands-on session on Natural Language Processing, organized by the Technebiz association of the Department of Computer Science and Business Systems. A step forward in mastering the evolving world of AI-driven technologies."

Mirage: A Multi-Level Tensor Algebra Super-Optimizer that Automates GPU Kernel Generation for PyTorch Applications With the increasing growth of artificial intelligence—introduction of large language models

Stuff I'm learning more about how to build lately: -Large language models and neural networks -Entity component systems (ECS) for game engines -Rust language procedural macros -Vector embedding models -Data quality evaluation systems

AI is coming home

🚀 Excited to Share My Latest Research on Transforming Attention Mechanisms! I recently worked on something interesting. The work is published as a preprint on TechRxiv introducing the Energy-Well Based Distance-Aware Attention Mechanism – a novel approach to attention modeling that goes beyond conventional self-attention by integrating energy-based configurations for both intra- and inter-cluster focus. 🔍 What Sets the Work Apart? The method introduces an energy-well-inspired framework that dynamically tunes attentional focus across data clusters, allowing for a flexible, distance-aware approach. This approach incorporates several mathematical configurations, including Gaussian, Lorentzian, and Softmax Exponential energy wells, each designed to adapt attention based on spatial and contextual relationships among tokens. The result? Enhanced interpretability, accuracy, and computational efficiency across tasks in NLP and computer vision. Key Innovations: Distance-Based Influence: Attention that adapts based on spatial and conceptual "distance," similar to physical energy fields. Configurable Spread: Different energy well models enable everything from localized to wide-reaching attention, tailored for task-specific needs. Cross-Cluster Interaction: Our model enhances inter-cluster dynamics, offering new levels of interpretability and insight into complex data structures. 📝 Paper (Preprint): Check out the full details of the approach, including the mathematical formulations and experimental results, on TechRxiv: https://lnkd.in/gbzSRAUR Full Working Code: For those interested in implementation, explore my GitHub repository, where I’ve shared the code, configurations, and instructions to replicate our results: https://lnkd.in/gSsuif6E PS: The paper is under review at IEEE Transactions on Neural Networks and Learning Systems. I welcome any suggestions that could enhance this work or provide further validation for this technique. Looking forward to seeing how this approach can be applied across new domains and to connecting with anyone interested in further discussions! #AI #MachineLearning #DeepLearning #AttentionMechanism #Transformers #EnergyBasedModels #Interpretability #NLP #ComputerVision #TechResearch #NeuralNetworks #XAI #DataScience #TechRxiv #AcademicResearch #AIResearch #Innovation #ResearchAndDevelopment #PatternRecognition #ScienceInnovation

Join us at #ICML 2024 in Vienna, Austria, where Vithu Thangarasa will present our accepted paper, "Sparse-IFT: Sparse Iso-FLOP Transformations for Maximizing Training Efficiency," during the poster session. This paper introduces a novel approach to enhancing neural network efficiency by leveraging sparsity to boost accuracy while maintaining the same computational cost as dense models. Our simple-to-use Sparse-IFT method delivers significant performance gains across both computer vision and natural language processing tasks, all without tuning any training hyperparameters. Don't miss this opportunity to learn about our recent sparsity research! Read our paper here: https://lnkd.in/gnHqw85Z Come visit us at Booth 205B or contact us to learn more: https://lnkd.in/gmWFBAKh

🌟 *Calling all pioneers of mathematics and artificial intelligence!* ✨ Prepare to dive into the depths of ingenuity with the latest venture of MoraMaths: *Lexicon*! 🤖 Don't miss out on this opportunity to push the boundaries of Mathematics and AI through ground-breaking challenges that fuse the precision of math with cutting-edge advancements in Natural Language Processing. 🧮💻 Gear up for an intellectual odyssey that will redefine the frontiers of mathematical exploration! 🛠️📚 #Lexicon #MathAI #UoM #LearningNeverStops #MoraMath

It's kind of wild that "Sequence to Sequence Learning with Neural Networks" is only a decade old. NeurIPS 2024 just announced the 2024 Test of Time Award for this seminal work. This paper was part of a paradigm shift in how we approach artificial intelligence and machine learning. The encoder-decoder architecture has become ubiquitous and has solved problems from NLP and computer vision to time series analysis and reinforcement learning. ChatGPT and large language models are direct descendants of this approach, showcasing the profound impact of sequence-to-sequence models on technology and society. It's a testament to the power of general methods in AI. Another Vindication of Rich Sutton's "Bitter Lesson" about the effectiveness of leveraging computation over handcrafted solutions. I'm curious to see where the next decade in AI takes us!