Speaker: Jason Eshraghian, UC Santa Cruz Abstract: This talk will show you what neuromorphic computing can do when an academic lab accidentally pulls $2-million of GPU-hours. We will showcase a series of frontier reasoning LLMs developed out of an academic lab, from data curation and pre-training to post-training and alignment. These models surpass leading LLMs fromContinue reading "TILOS Seminar: Neuromorphic LLMs"
Speaker: Bharath Sriperumbudur, Pennsylvania State University Abstract: Wasserstein gradient flows have become a popular tool in machine learning with applications in sampling, variational inference, generative modeling, and reinforcement learning, among others. The Wasserstein gradient flow (WGF) involves minimizing a probability functional over the Wasserstein space (by taking into account the intrinsic geometry of the Wasserstein space).Continue reading "Optimization for ML and AI Seminar: (De)regularized Wasserstein Gradient Flows via Reproducing Kernels"