Models

DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models NeurIPS 2023 Disentangled CVAEs with Contrastive Learning for Explainable Recommendation AAAI 2023 Disentangled Generative Models for Robust Prediction of System Dynamics ICML 2023 Disentangled Multi-Fidelity Deep Bayesian Active Learning ICML 2023 Disentangled Representation for Causal Mediation Analysis AAAI 2023 Disentanglement of Latent Representations via Causal Interventions IJCAI 2023 Disentanglement via Latent Quantization NeurIPS 2023 Disentanglement with Biological Constraints: A Theory of Functional Cell Types ICLR 2023 Disentangling Learning Representations with Density Estimation ICLR 2023 Distantly Supervised Course Concept Extraction in MOOCs with Academic Discipline ACL 2023 Distilling ChatGPT for Explainable Automated Student Answer Assessment EMNLP 2023 Distilling Model Failures as Directions in Latent Space ICLR 2023 Distilling Neural Fields for Real-Time Articulated Shape Reconstruction CVPR 2023 Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models NeurIPS 2023 Distilling Reasoning Capabilities into Smaller Language Models ACL 2023 Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes ACL 2023 Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering ACL 2023 Distortion and Uncertainty Aware Loss for Panoramic Depth Completion ICML 2023 Distribution-Aligned Diffusion for Human Mesh Recovery ICCV 2023 Distributional Offline Policy Evaluation with Predictive Error Guarantees ICML 2023 Distribution-Consistent Modal Recovering for Incomplete Multimodal Learning ICCV 2023 Distribution Shift Inversion for Out-of-Distribution Prediction CVPR 2023 DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation NeurIPS 2023 Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning ICCV 2023 Diverse Inpainting and Editing with GAN Inversion ICCV 2023