Posters

Hopfield Boosting, a novel energy-based boosting approach, achieves state-of-the-art OOD detection by leveraging Hopfield energy to sharpen the decision boundary between in-distribution and out-of-dis…

Optical-flow-free video semantic segmentation excels in adverse weather by merging adjacent frame information via a fusion block and a novel temporal-spatial teacher-student learning strategy.

OLLM: An end-to-end LLM method builds ontologies from scratch, outperforming subtask approaches and improving semantic accuracy with novel evaluation metrics.

ELCRec: a novel intent learning model for recommendation, unites behavior representation learning with end-to-end learnable clustering, achieving superior performance and scalability.

EfficientNAT: a novel approach to token-based image synthesis boosts performance and slashes computational costs by cleverly disentangling and optimizing spatial-temporal interactions between image to…

DIVA: Evolutionary task generation for robust, adaptable AI agents in complex simulators.

EMVP: A novel PEFT pipeline boosts Visual Place Recognition accuracy by 97.6% using Centroid-Free Probing & Dynamic Power Normalization, saving 64.3% of parameters.

Large foundation models empower visible-infrared person re-identification by enriching infrared image representations with automatically generated textual descriptions, significantly improving cross-m…

This study introduces a novel active learning paradigm for 3D molecular graphs, significantly improving efficiency and accuracy by leveraging geometric graph isomorphisms and distributional representa…

Emotion-LLaMA: A new multimodal large language model excels at emotion recognition and reasoning, outperforming existing models and leveraging a newly created dataset, MERR.

Homogenized SGD reveals heavy-tailed neural network parameters, offering quantifiable bounds on tail-index and showcasing the interplay between optimization hyperparameters and model generalization.

EAGLE: Guiding LLMs using latent embeddings for controlled text generation.

Novel trajectory volatility score (TV Score) significantly improves out-of-distribution detection in mathematical reasoning by leveraging dynamic embedding trajectories, outperforming existing GLM met…

Discover optimal embedding dimensions for contrastive learning & k-NN using graph arboricity; achieve efficient model design & performance.

EM Distillation (EMD) efficiently trains one-step diffusion models by using an Expectation-Maximization approach, achieving state-of-the-art image generation quality and outperforming existing methods…

Elucidating Dataset Condensation (EDC) achieves state-of-the-art accuracy in dataset condensation by implementing soft category-aware matching and a smoothing learning rate schedule, improving model t…

Elo rating’s reliability for LLM evaluation is challenged, revealing inconsistencies and suggesting new, more robust methods are needed for accurate model ranking.

Elliptical Attention enhances transformers by using a Mahalanobis distance metric, stretching the feature space to focus on contextually relevant information, thus improving robustness and reducing re…

ElasTST: A novel time-series transformer enables robust forecasting across various horizons without per-horizon training, enhancing adaptability and accuracy.

Einspace: A novel neural architecture search space built from fundamental operations, enabling discovery of diverse high-performing network architectures and surpassing existing NAS methods.