embedding dimension

Compact Geometric Representations of Hierarchies COLT 2026 Learning to Select: Query-Aware Adaptive Dimension Selection for Dense Retrieval ACL 2026 When Can Transformers Count to n? UAI 2026 Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count ICLR 2025 Counting in Small Transformers: The Delicate Interplay between Attention and Feed-Forward Layers ICML 2025 How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction ICML 2025 Understanding Token Probability Encoding in Output Embeddings COLING 2025 DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models NeurIPS 2024 Embedding Dimension of Contrastive Learning and k-Nearest Neighbors NeurIPS 2024 Linguistic Collapse: Neural Collapse in (Large) Language Models NeurIPS 2024 Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions EMNLP 2024 The Effective Number of Shared Dimensions Between Paired Datasets AISTATS 2024 Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel AISTATS 2023 Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection AutoML 2023 Representational Strengths and Limitations of Transformers NeurIPS 2023 In defense of dual-encoders for neural ranking ICML 2022 Embedding Dimension of Polyhedral Losses COLT 2020 Low-Rank Bottleneck in Multi-head Attention Models ICML 2020 Query-Key Normalization for Transformers EMNLP 2020 The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning IJCAI 2019