Representation Learning

Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts CVPR 2024 OmniVec: Learning Robust Representations With Cross Modal Sharing WACV 2024 Once Read is Enough: Domain-specific Pretraining-free Language Models with Cluster-guided Sparse Experts for Long-tail Domain Knowledge NeurIPS 2024 On conditional diffusion models for PDE simulations NeurIPS 2024 On Differentially Private Subspace Estimation in a Distribution-Free Setting NeurIPS 2024 One For All: Towards Training One Graph Model For All Classification Tasks ICLR 2024 On Eliciting Syntax from Language Models via Hashing EMNLP 2024 One Meta-tuned Transformer is What You Need for Few-shot Learning ICML 2024 One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts ICML 2024 One-Shot Learning as Instruction Data Prospector for Large Language Models ACL 2024 One-Shot Structure-Aware Stylized Image Synthesis CVPR 2024 On Feature Learning in Structured State Space Models NeurIPS 2024 On learning history-based policies for controlling Markov decision processes AISTATS 2024 On Measuring Faithfulness or Self-consistency of Natural Language Explanations ACL 2024 On Sparse Canonical Correlation Analysis NeurIPS 2024 On Sufficient Graphical Models JMLR 2024 On the Ability of Developers' Training Data Preservation of Learnware NeurIPS 2024 On the Affinity, Rationality, and Diversity of Hierarchical Topic Modeling AAAI 2024 On the Calibration of Human Pose Estimation ICML 2024 On the Comparison between Multi-modal and Single-modal Contrastive Learning NeurIPS 2024 On the Concept Trustworthiness in Concept Bottleneck Models AAAI 2024 On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm CVPR 2024 On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning ICML 2024 On the Efficiency of ERM in Feature Learning NeurIPS 2024 On the estimation of persistence intensity functions and linear representations of persistence diagrams AISTATS 2024