2022 - 2023 Course Highlights DissertationsMachine Learning:Autoregressive Diffusion Neural ProcessesData Compression with Variational Implicit Neural RepresentationsDisease Subtyping and Biomarker Discovery using High-Dimensional Bayesian Mixture Models with Feature SelectionDiffusion Models for Peptide BondingEstablishing a Unified Framework for Iterative Machine TeachingImproving Uncertainty Quantification in Regression Problems through Conformal TrainingLarge Language Model Self-Critique for Task-Oriented Text GenerationLarge Language Models for Reliable Information ExtractionOptimal PAC-Bayes Bounds and their Variational ApproximationsPre-Training Meta-Models for InterpretabilitySim2Real With Neural ProcessesSpeech and Language Processing:Distilling and Forgetting in Large Pre-Trained ModelsGraph Neural Stochastic Differential EquationsIncorporating Vision Encoders into Retrieval Augmented Visual Question Answering (1)Incorporating Vision Encoders into Retrieval Augmented Visual Question Answering (2)Multilingual Models in Neural Machine TranslationUtilizing Large Language Models for Question Answering in Task-Oriented DialoguesComputer Vision and Robotics:3D Pose Estimation and Topology Reconstruction Using Foundation Models and Render and CompareAdapting Pre-Trained Vision-Language Models in Medical Domains Breaking the Limits of Diffusion Models via Continuous Dynamical SystemsEvaluating Benefits of Heterogeneity in Constrained Multi-Agent LearningEvaluating the Capabilities of Large Language Models for Spatial and Situational UnderstandingEliciting Latent Knowledge from Language Reward ModelsFunction Constrained Program Synthesis Posters from the Advanced Machine Learning ModuleConditional and Latent Neural ProcessesInfoGAN and BeyondThe Forward-Forward Algorithm: Some Preliminary InvestigationsToy Models of SuperpositionVariational Continual LearningWeight Uncertainty in Neural Networks (1)Weight Uncertainty in Neural Networks (2)
