Library · Machine learning & AI
What Autoencoders & Generative Modeling got wrong, from 68 theses
Summary written by a model from the records below: The records document various practical challenges encountered when applying autoencoders and generative architectures across diverse domains. Across these studies, models frequently suffer from training instabilities, severe baseline underperformance, and latent space representation flaws. These records come from graduate theses, 2021 to 2026, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 10 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Generative adversarial networks suffer from mode collapse and training instabilities
Adversarial frameworks frequently suffer from mode collapse and vanishing gradients during optimization across image, molecular, and tabular domains. Removing or destabilizing discriminator feedback causes generative outputs to collapse into low-variance modes or random noise.
Considered and rejected
Considered and rejected: Decided against using GANs for generative spectral modeling because of training instability, choosing Variational Autoencoders (VAEs) instead.
Information Content and Analysis of X-ray Absorption Spectroscopy and X-ray Emission Spectroscopy Using Machine Learning · University of Washington · PhD
Tried and failed
generative adversarial network without variational autoencoder applied to 3D voxel shape generation. Outcome: unstable. Reason: mode collapse prevented learning the latent probability distribution, generating only a few shapes
Deep Learning Based Manufacturing Capability Modeling for Process Planning Automation · Georgia Tech · PhD
Tried and failed
Classical generative adversarial network applied to decay histogram distribution modeling. Outcome: unstable. Reason: Mode collapse and vanishing gradients during training on histogram data
Development of the single-molecule tracking and fluorescence lifetime techniques for biophysical measurements and biomedical applications · UT Austin · PhD
Tried and failed
training generator ensemble without discriminator applied to generative adversarial network training. Outcome: did not converge. Reason: generator suffered severe mode collapse and converged to a single low-variance mode without adversarial feedback
Memory of Motion for Initializing Optimization in Robotics · EPFL · PhD
Tried and failed
factored latent codes for scene decomposition applied to generative image synthesis. Outcome: unstable. Reason: foreground latent code collapsed into background representation during training
Unsupervised learning of human movement from images · Imperial · PhD
Tried and failed
training GAN generator without discriminator applied to binary image generation. Outcome: did not converge. Reason: removing the adversarial feedback caused generated patterns to collapse into random noise
Algorithmic design of photonic structures with deep learning · Georgia Tech · PhD
Tried and failed
single-stage physics-constrained generative adversarial network applied to structural topology optimization design generation. Outcome: unstable. Reason: Mode collapse and severe constraint violation from combining complex physics loss with adversarial training in one stage.
Generative Design Using Deep Learning Methods for Functionality and Manufacturability · Georgia Tech · PhD
Tried and failed
differentially private generative adversarial networks applied to tabular synthetic data generation. Outcome: unstable. Reason: mode collapse caused failure to match one-way marginals across most variables
Causal Inference Under Privacy Constraints · MIT · PhD
Considered and rejected
Considered and rejected: Rejected Generative Adversarial Networks (GANs) and standard INNs/MDNs due to mode collapse, vanishing gradients, lower precision, and training instabilities on lower-dimensional sub-manifolds.
Towards Efficient and Robust Robot Planning · Duke · PhD
Considered and rejected
Considered and rejected: Rejected Generative Adversarial Networks (GANs) for structure generation due to training instability, mode collapse, and lack of explicit likelihood/interpretable latent space.
On-Demand Inverse Design of Phononic Metamaterials via Deep Learning · Leibniz Universität Hannover · PhD
Considered and rejected
Considered and rejected: Rejected pure gradient-based adversarial optimization for failure generation because local optima cause diversity collapse into a single failure mode.
Breaking things so you don’t have to: risk assessment and failure prediction for cyber-physical AI · MIT · PhD
Tried and failed
conditional recurrent neural network generative model applied to property-conditioned molecular sequence generation. Reason: mode collapse during generation leading to low uniqueness in generated outputs
Designing Macromolecules using Machine Learning and Simulations · MIT · PhD
Considered and rejected
Considered and rejected: Rejected generative adversarial networks (GANs) for inverse CTLE design due to training instability and inability to systematically model multi-modal parameter distributions.
High-speed Channel Analysis and Design using Polynomial Chaos Theory and Machine Learning · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected standard Conditional Generative Adversarial Networks (CGANs) for residential load scenario forecasting because training is unstable when conditioning on continuous vector-valued historical time series.
Learning and Optimization for Efficient and Optimal Operations in Sustainable Power Systems · University of Washington · PhD
Complex autoencoders and generative architectures fall behind simpler models or baselines
Deep autoencoders and variational architectures frequently underperform simpler alternatives such as linear regression, decision trees, random forests, or one-class SVMs. Adding complex variational objectives or deep compression often yields higher error than direct supervised baselines or raw uncompressed features.
Tried and failed
variational autoencoder applied to tabular financial earnings metrics. Outcome: worse than baseline. Reason: standard autoencoders offered simpler training and direct reconstruction without probabilistic complexity
Machine learning in finance: from earnings data analysis to algorithmic trading · Imperial · Doctorate
Tried and failed
semi-supervised variational autoencoders applied to protein fitness prediction with combinatorial mutations. Outcome: worse than baseline. Reason: jointly trained models failed to outperform simpler supervised baselines like linear regression
Optimizing Protein Fitness and Function with Sparse Experimental Data · Harvard · PhD
Tried and failed
sequence-to-sequence variational autoencoder applied to sketch to latent space mapping. Outcome: worse than baseline. Reason: failed to show significant improvement over simpler vector KNN regression despite higher training complexity
Tried and failed
surrogate modeling on variational autoencoder latent space applied to property prediction from spatial representations. Outcome: worse than baseline. Reason: nonlinear latent embedding degraded forward predictions and increased uncertainty compared to linear dimensionality reduction
Neural Inverse Microstructure Design with Bayesian Scale-Bridging · Georgia Tech · PhD
Tried and failed
supervised deep neural network regression applied to materials microstructure-to-property mapping. Outcome: worse than baseline. Reason: yielded higher error and lower R² values than a semi-supervised variational autoencoder
Linking properties to microstructure in liquid metal embedded elastomers via machine learning · Iowa State · Master's
Tried and failed
neural topic modeling using variational autoencoders applied to long academic documents. Outcome: worse than baseline. Reason: consistently produced negative normalized pointwise mutual information and low topic coherence scores
Topic Modeling for Heterogeneous Digital Libraries: Tailored Approaches Using Large Language Models · Virginia Tech · Master's
Lost to a baseline
Autoencoder (F2=0.8156, precision=0.469) was beaten by OCSVM (F2=0.9914, precision=0.958).
Data Analytics and Machine Learning Applications in Fermentation Processes and Molecular Property Prediction · Virginia Tech · Doctorate
Lost to a baseline
Random Forest beat Autoencoders on pointwise anomaly detection accuracy (RF: 0.963 vs AE: 0.937) and F1-score (RF: 0.967 vs AE: 0.953)
An AI and data-driven approach to unwanted network traffic inspection · Politecnico di Torino · PhD
Lost to a baseline
Neural Network fault isolation accuracy (0.9755, 6784.16 s) was beaten by Decision Tree (0.9946, 113.82 s) and Autoencoder (0.9933, 2468.60 s)
Fault diagnosis in aircraft fuel system components with machine learning algorithms · Cranfield · PhD
Lost to a baseline
Autoencoder fault identification accuracy (0.9873) was beaten by Decision Tree (0.9898) and Neural Network (0.9897)
Fault diagnosis in aircraft fuel system components with machine learning algorithms · Cranfield · PhD
Lost to a baseline
Matched filter alone slightly outperformed the U-Net autoencoder on pure QPSK + AWGN due to non-linear distortion introduced by the autoencoder
On the Use of Deep Learning Models for Interference Detection and Mitigation · Virginia Tech · Master's
Lost to a baseline
Attention autoencoder was outperformed by CNN autoencoder on non-sparse TEC reconstruction (R=0.9480 vs R=0.9604).
Employing Machine Learning Techniques to Increase the Quality of Ionospheric Modeling · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected deep learning models (e.g., autoencoders) for unknown attack detection because they require large training datasets and performed worse than tree-based models.
Diagnosis and Mitigation of Evolving Threats for Sustainable Security · University College Dublin · PhD
Variational priors and regularizations distort or hinder latent representation quality
Enforcing standard Gaussian priors, mutual information maximization, or specific parameter regularizations often warps dynamic trajectories and induces high KL divergence. These constraints fail to produce steady sparsity, fail to prevent posterior collapse, and degrade target downstream utility compared to unconstrained representations.
Tried and failed
autoregressive latent covariance structure applied to variational autoencoders. Reason: did not prevent posterior collapse when it occurred in standard VAE baseline
Deep Time: Deep Learning Extensions to Time Series Factor Analysis with Applications to Uncertainty Quantification in Economic and Financial Modeling · Virginia Tech · Master's
Tried and failed
L1 or L2 regularization on posterior parameters applied to variational autoencoder latent representations. Outcome: worse than baseline. Reason: Failed to increase representation sparsity compared to vanilla variational autoencoder baseline.
Injecting Inductive Biases into Distributed Representations of Text · Cambridge · PhD
Tried and failed
variational autoencoder for sparse latent representation applied to text sentence embeddings. Outcome: unstable. Reason: struggled to achieve steady and consistent sparsity across hyperparameter configurations
Injecting Inductive Biases into Distributed Representations of Text · Cambridge · PhD
Tried and failed
variational autoencoder for latent trajectory modeling applied to cell shape dynamics over time. Outcome: worse than baseline. Reason: prior regularisation warped latent dynamical trajectories compared to standard autoencoders
Computing Interpretable Representations of Cell Morphodynamics · Imperial · PhD
Tried and failed
variational autoencoder without auxiliary predictor network applied to structured latent space performance prediction. Outcome: no signal. Reason: unsupervised reconstruction objective failed to organize the latent space with respect to the downstream target metric
Incorporating prior knowledge to efficiently design deep learning accelerators · UT Austin · PhD
Tried and failed
variational autoencoder with explicit mutual information maximization applied to image representation learning. Outcome: worse than baseline. Reason: leads to very high KL divergence, failing to match the marginalized posterior to the prior
Novel approaches for learning representations · UT Austin · PhD
Considered and rejected
Considered and rejected: Standard Variational AutoEncoder (VAE) with KL-divergence regularization on a single latent variable was rejected for inverse continuous localization because stochastic sampling caused noisy loss landscapes.
Condition monitoring for dry cask storage using helical guided ultrsonic waves · UT Austin · PhD
Considered and rejected
Considered and rejected: Rejected using Autoencoders for primary dimension identification because latent representations optimize reconstruction error rather than statistical interpretability
Towards soundscape fingerprinting: development, analysis and assessment of underlying acoustic dimensions to describe acoustic environments · Leibniz Universität Hannover · PhD
Tried and failed
imposing latent space regularisation constraints applied to out-of-distribution detection via reconstruction error. Outcome: worse than baseline. Reason: latent constraints degrade reconstruction fidelity compared to unconstrained autoencoders, reducing anomaly detection discriminability
Learning Representations Toward the Understanding of Out-of-Distribution for Neural Networks · Georgia Tech · PhD
Tried and failed
conditional variational autoencoder with factorised latent space applied to 3D multi-object scene completion. Reason: failed to learn a structured prior, yielding incomplete reconstructions when sampling from Gaussian noise
Scene understanding for 3D multi-object scenes: labelling, reasoning and decomposing · Imperial · PhD
Tried and failed
variational autoencoders with Gaussian priors applied to systems with strongly non-Gaussian statistics. Outcome: did not generalise. Reason: standard Gaussian latent priors poorly represent strongly non-Gaussian statistics
Physics-Driven Machine Learning for Applications in Geophysical Fluid Dynamics · MIT · PhD
Tried and failed
i.i.d. latent sampling in variational autoencoders applied to set and graph generation. Reason: It optimizes an excessively loose lower bound on the true evidence lower bound.
Equivariant Neural Architectures for Representing and Generating Graphs · EPFL · PhD
Reconstruction based representations fail to detect anomalies or separate target concepts
Autoencoders trained for anomaly detection often overfit nominal training data or act as generic compressors that reconstruct textured defects and outlier signals. Consequently, reconstruction errors fail to reliably distinguish true anomalies from normal variations without supervised tuning.
Tried and failed
batch normalization applied to autoencoder anomaly detection. Reason: did not improve anomaly detection performance during model development
Developing AI Systems for Monitoring Heterogeneous Mental Health Disorders · Cornell · PhD
Tried and failed
denoising autoencoder confounder correction applied to detecting high-frequency biological outliers. Reason: autoencoder erroneously corrected true synthetic aberrations by treating them as technical noise or outliers
ON BIOINFORMATIC METHODS FOR THE DETECTION OF ALTERNATIVELY SPLICED VARIANTS FOR CLINICAL DIAGNOSTICS · Penn · PhD
Tried and failed
variational autoencoder anomaly detection applied to semiconductor fabrication process monitoring. Outcome: overfit. Reason: overfitting on nominal training data caused high reconstruction error on unseen nominal runs, degrading classification
Applications of Probabilistic Machine Learning Models to Semiconductor Fabrication · MIT · PhD
Tried and failed
reconstruction-based neural autoencoders applied to time series anomaly detection. Outcome: did not generalise. Reason: models over-smoothed extreme values or erroneously fit out-of-range outlier points
Machine Learning Systems for Unsupervised Time Series Anomaly Detection · MIT · PhD
Tried and failed
One-Class SVM and feedforward autoencoders applied to sequential anomaly detection in logs. Outcome: did not generalise. Reason: Unable to reliably detect anomalies without supervised attack knowledge for tuning hyperparameters
Data-driven Algorithms for Critical Detection Problems: From Healthcare to Cybersecurity Defenses · Virginia Tech · Doctorate
Tried and failed
validation loss for autoencoder model selection applied to sequential autoencoders on sparse count data. Outcome: overfit. Reason: loss does not penalize learning the identity mapping instead of underlying latent dynamics
General and interpretable models for inferring dynamical computation in biological neural networks · Georgia Tech · PhD
Tried and failed
autoencoder-based anomaly detection on textured backgrounds applied to surface anomaly detection. Outcome: no signal. Reason: The network acts as a general compressor, reconstructing textured surface variations indistinguishably from actual anomalies.
Detecting Anomalies and Obstacles in Road Scenes · EPFL · PhD
Considered and rejected
Considered and rejected: Rejected autoencoder latent-space partition for expert model selection because autoencoders learn generic input distributions rather than task-specific model fitness.
Continuous Learning for Lightweight Machine Learning Inference at the Edge · MIT · PhD
Considered and rejected
Considered and rejected: Rejected relying directly on raw noisy autoencoder reconstruction error for moving thresholds, adding an alternating binary convolutional filter for denoising.
A Hitchhikers Guide to Anomaly Detection: Machine Learning-based approaches to anomaly detection and Failure diagnosis in spacecraft avionics systems using low power devices & flight ready devices · University College Dublin · PhD
Generative architectures fail to generalize across out of distribution or unseen structures
Generative and autoencoder models overfit to source training distributions and struggle to adapt to unseen structural geometries or multimodal biological landscapes. Coarse latent representations often generate blurry outputs and fail to retain fine domain constraints under domain shifts.
Tried and failed
MLP-based variational autoencoder applied to cross-dataset structural anomaly detection. Outcome: did not generalise. Reason: model overfitted to source structure distributions and failed on unseen structural data
Machine learning tools for identifying structural artifacts in data · Imperial · Master's
Tried and failed
generative neural network for data augmentation applied to imbalanced 3D medical image segmentation. Outcome: did not generalise. Reason: generated augmentations caused heavy overfitting to validation data and failed to generalize to unseen test data
Learning strategies for improving neural networks for image segmentation under class imbalance · Imperial · PhD
Tried and failed
standard generative adversarial network inversion applied to image restoration under gross corruptions. Outcome: did not generalise. Reason: Restored outputs deviate heavily toward corrupted regions without prior knowledge of anomaly masks.
Robust Learning for Fine-Grained Anomaly Detection in a Data-Rich but Label-Rare Environment · Georgia Tech · PhD
Tried and failed
adversarial bias field augmentation applied to medical image segmentation domain generalization. Outcome: did not generalise. Reason: increased vulnerability to out-of-distribution spike noise artifacts, degrading segmentation performance
Improving the domain generalization and robustness of neural networks for medical imaging · Imperial · PhD
Tried and failed
compositional generative mixture models for classification applied to instance segmentation under occlusion. Outcome: did not generalise. Reason: overlapping objects with similar textures caused ambiguous boundaries and merged instance detections
Addressing Occlusion in Panoptic Segmentation · Virginia Tech · Master's
Tried and failed
continuous conditional variational autoencoder applied to recursive trajectory and motion generation. Outcome: did not generalise. Reason: Continuous latent space caused blurry generations and failed to follow conditioning velocity commands over recursive rollouts
Generative Latent Motion Planning and Reinforcement Learning for Legged Locomotion · MIT · PhD
Tried and failed
generalist generative models for molecular dynamics applied to protein conformational landscape sampling. Outcome: did not generalise. Reason: failed to accurately sample target conformational energy landscapes compared to ground truth
Learning on Graphs with Long-Range Dependencies: Methods and Applications · EPFL · PhD
Tried and failed
variational autoencoders for sequential modeling applied to multimodal agent state estimation. Outcome: did not generalise. Reason: failed to capture multimodal distributions under sparse and partial observations
Trajectory Modeling using Generative Approaches for Scheduling, Planning, and Multi-Agent Systems · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected using Trajectory DBSCAN and WebPPL Bayesian Programming as the core generative simulation engines because generated paths were constrained copies of training data that failed to generalize to unseen sites
Machine Learning Simulation of Pedestrians Exploring the Built Environment · MIT · PhD
Autoregressive generative forecasting accumulates compounding errors over temporal horizons
Deploying generative adversarial models and autoencoders in autoregressive sequential settings leads to rapid error compounding over extended prediction horizons. These errors degrade multi-step forecasting stability and produce severe trajectory drifts in temporal dynamics.
Considered and rejected
Considered and rejected: Rejected an autoencoder architecture with symmetric 4-layer ConvLSTM encoder/decoder predicting autoregressively across multiple stochastic steps due to loss convergence failure and severe overfitting
Integrating Machine Learning Techniques for Streamlined Predictive Modeling in Cosmological Applications · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected standard Variational Autoencoder (VAE) architecture for turbofan RUL estimation because decoders fail on temporal state projections; replaced decoder with a regressor network (RVE).
Uncertainty quantification of faults in rotating machines · Texas Tech · PhD
Tried and failed
latent space GAN autoregressive multi-step forecasting applied to deseasonalized time series prediction. Outcome: worse than baseline. Reason: severe error accumulation during recursive forecasting in the autoencoder latent space
Utilizing Recurrent Neural Networks for Temporal Data Generation and Prediction · Virginia Tech · Master's
Tried and failed
deep generative autoregressive forecasting without state estimation applied to spatiotemporal fluid and atmospheric dynamics. Outcome: unstable. Reason: rapid accumulation of autoregressive errors over long prediction horizons
Development of generative adversarial networks for spatiotemporal fluid flow, atmospheric and flood predictions · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected using Generative AI (standard GANs/GAIN) for time-based production data due to severe overfitting and inaccurate time-series imputation.
Enhanced Oil Field Data-Wrangling using Machine Learning · Texas Tech · PhD
Standard continuous autoencoder bottlenecks lack required fidelity or properties for downstream tasks
Continuous under-parameterized bottlenecks struggle to interpolate exact fixed points and fail to preserve fine-grained structural details. In several applications, standard autoencoder decoders introduce nonlinear distortions or risk trivial identity mappings that limit downstream task accuracy.
Considered and rejected
Considered and rejected: Rejected standard autoencoders (AE) in favor of variational autoencoders (VAE) because standard AE latent spaces are non-continuous and hinder smooth interpolation and balanced code generation
Gestalt Perception of Biological motion with a Generative Artificial Neural Network Model · Tübingen · PhD
Considered and rejected
Considered and rejected: Rejected standard under-parameterized autoencoders with bottleneck layers because they cannot interpolate training examples or store them as exact fixed points.
Foundations of Machine Learning: Over-parameterization and Feature Learning · MIT · PhD
Considered and rejected
Considered and rejected: Rejected using standard continuous latent space autoencoders in favor of vector quantization to convert sequence learning from regression to multi-class classification
Statistical Learning of Particle Dispersion in Turbulence and Modeling Turbulence via Deep Learning Techniques · Duke · PhD
Considered and rejected
Considered and rejected: Rejected standard VAE in favor of deterministic autoencoder because generative latent distribution modeling was unnecessary for embedding compression.
Neural Compression for Scalable Question-Answer Retrieval · Dalhousie · Master's
Tried and failed
latent diffusion models applied to high-resolution structured grayscale medical images. Outcome: worse than baseline. Reason: struggled with fine structures, failing to match generative adversarial network fidelity
Controllable synthetic algorithms and evaluations for annotated clinical image synthesis · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected autoencoder-based bottlenecks for PVN internal state prediction because compression limited prediction accuracy and risked trivial identity transformations without bottlenecks.
Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology · EPFL · PhD
Considered and rejected
Considered and rejected: Rejected fully unconstrained deep autoencoders for unsupervised dictionary learning because untying weights broke recovery of generative physical filters.
Generative models for neural time series with structured domain priors · MIT · PhD
Considered and rejected
Considered and rejected: Rejected likelihood-based and flow-based generative models (VAEs, PixelRNN) due to blurry outputs, slow generation, or excessive parameter counts
Deep Learning for Localized-Haptic Feedback in Tactile Surfaces · EPFL · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Perform additional error analysis on the variational autoencoder component of the SG-UREVA relation extraction model. Blocker: Lack of specific hypotheses, error categories, or targeted evaluation metrics to investigate
N-ary Cross-sentence Relation Extraction: From Supervised to Unsupervised Learning · Virginia Tech · Master's
Left open
Apply minimax Pareto fairness optimization to variational autoencoders to reduce reconstruction error disparities across demographic groups. Blocker: None
Minimax Fairness in Machine Learning · Duke · PhD
Left open
Improve CNN autoencoder surrogate model generalization to unseen geometries and features for finite element error indicator prediction. Blocker: Lacks specific target geometries, performance metrics, and a concrete architectural approach for out-of-distribution generalization
Development of Surrogate Model for FEM Error Prediction using Deep Learning · Virginia Tech · Master's
Left open
Validate AutoEKF on synthetic data and benchmark against particle filter methods, or replace linearization with variational autoencoders. Blocker: None
TOWARDS DATA DRIVEN NETWORK EPIDEMIC MODELING. · Penn · PhD
Left open
Evaluate alternative continuous-mapping models in place of convolutional autoencoders for GAN-based synthetic EHR generation. Blocker: EHR datasets such as MIMIC typically require credentialed data access agreements
Synthetic Electronic Medical Record Generation using Generative Adversarial Networks · Virginia Tech · Master's
Left open
Optimize hyperparameters for the input stress and output finite element error autoencoder architectures. Blocker: Lack of the original synthetic FEM stress/error training datasets and detailed baseline hyperparameter search space
Development of Surrogate Model for FEM Error Prediction using Deep Learning · Virginia Tech · Master's
Left open
Replace linear experts in the Generalized Variational Autoencoder with multilayer perceptrons or partially linear mixture-of-experts formulations. Blocker: None
Supervised Variational Autoencoders for Structural Learning and Statistical Inference with Heterogeneous Data · Virginia Tech · Doctorate
Left open
Develop uncertainty propagation from state to value uncertainty in partially observable environments using variational autoencoders for ADFQ. Blocker: Lacks specific analytical formulation or target POMDP environments for evaluating VAE-based uncertainty propagation.
Off-Policy Temporal Difference Learning For Robotics And Autonomous Systems · Penn · PhD
Left open
Extend the CRsAE model to handle non-concentrating posteriors by integrating variational autoencoders (VAEs) for uncertainty quantification. Blocker: None
Deep Learning for Inverse Problems in Engineering and Science · Harvard · PhD
Left open
Benchmark and systematically evaluate sparse autoencoders against clustering-based concept extraction methods across standard vision and video representation learning benchmarks. Blocker: None
Disentangling Visual Concepts Across Space and Time: From Image Hierarchies to Video Dynamics · York University · PhD
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- Nithya Subramanian · Cranfield · PhD · 2023
Fault diagnosis in aircraft fuel system components with machine learning algorithms - Siddharth Jain · Virginia Tech · Master's · 2022
Development of Surrogate Model for FEM Error Prediction using Deep Learning - Victor Prokhorov · Cambridge · PhD · 2021
Injecting Inductive Biases into Distributed Representations of Text - Bianca Champenois · MIT · PhD · 2026
Physics-Driven Machine Learning for Applications in Geophysical Fluid Dynamics - Van Than Tran · Leibniz Universität Hannover · PhD · 2026
On-Demand Inverse Design of Phononic Metamaterials via Deep Learning - Kushal Ramkumar · University College Dublin · PhD · 2026
Diagnosis and Mitigation of Evolving Threats for Sustainable Security - Ali Hariri · EPFL · PhD · 2026
Learning on Graphs with Long-Range Dependencies: Methods and Applications - Omodolapo David Ajayi · Texas Tech · PhD · 2026
Uncertainty quantification of faults in rotating machines - Jaeyoung Lee · Virginia Tech · Doctorate · 2026
Supervised Variational Autoencoders for Structural Learning and Statistical Inference with Heterogeneous Data - Dan Adler · Cornell · PhD · 2025
Developing AI Systems for Monitoring Heterogeneous Mental Health Disorders - Sarah Alnegheimish · MIT · PhD · 2025
Machine Learning Systems for Unsupervised Time Series Anomaly Detection - Liam Bruce Smith · Georgia Tech · PhD · 2025
Employing Machine Learning Techniques to Increase the Quality of Ionospheric Modeling
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