Posters

LCSS, a novel score-matching method, enables efficient and high-quality image generation in score-based diffusion models by using Stein’s identity to bypass the computationally expensive Jacobian trac…

Greedy algorithms for linear contextual bandits achieve poly-logarithmic regret under the novel Local Anti-Concentration condition, expanding applicable distributions beyond Gaussians and uniforms.

LocalOMD: Adaptive OMD in extensive-form games achieves near-optimal sample complexity by using fixed sampling and local updates, reducing variance and generalizing well.

LM-HT SNN: A learnable multi-hierarchical threshold model dramatically improves SNN performance, achieving near-ANN accuracy through dynamic current regulation and seamless ANN-SNN conversion.

PathWeave: A novel framework enabling Multimodal LLMs to continually evolve on modality, achieving comparable state-of-the-art performance with 98.73% less training burden!

TEA-GLM leverages LLMs for zero-shot graph learning by aligning GNN representations with LLM token embeddings, achieving state-of-the-art performance on unseen datasets and tasks.

LLMDFA: A novel LLM-powered framework performs compilation-free and customizable dataflow analysis, achieving high accuracy in bug detection by decomposing the task into sub-problems and mitigating L…

LLM-Check efficiently detects LLM hallucinations in a single response, using internal model analysis, improving real-time applications.

LLM-based Skill Diffusion (LDuS) enables zero-shot robotic policy adaptation to various contexts specified in natural language by generating controllable skill trajectories via loss-guided diffusion a…

LLM-AutoDA: Automating data augmentation for long-tailed learning using large language models, significantly boosting model performance.

LLM Processes leverage LLMs to create probabilistic regression models guided by natural language, enabling seamless integration of expert knowledge and improving prediction accuracy.

LLM dataset inference reliably detects if a dataset was used in training, overcoming limitations of existing membership inference attacks.

LLM circuit analyses remain consistent across model scales and extensive training, enabling more efficient interpretability research.

LLaNA: A novel Multimodal Large Language Model directly processes NeRF weights to enable NeRF captioning and Q&A, outperforming traditional 2D/3D-based methods.

LLaMo, a novel Large Language Model-based Molecular Graph Assistant, uses multi-level graph projection and instruction tuning to achieve superior performance on diverse molecular tasks.

LiveScene: Language-embedded interactive radiance fields efficiently reconstruct and control complex scenes with multiple interactive objects, achieving state-of-the-art results.

LIVE, a novel learnable in-context vector, significantly improves visual question answering by reducing computational costs and enhancing accuracy compared to traditional ICL methods.

LiteVAE: A new autoencoder design for latent diffusion models boosts efficiency sixfold without sacrificing image quality, achieving faster training and lower memory needs via the 2D discrete wavelet …

LiDAR Translator (LiT) unifies diverse LiDAR data through a novel data-driven translation framework, enabling zero-shot and multi-domain joint learning, thus improving autonomous driving systems.

LMAC-ZS: First decoder-based method for explaining zero-shot audio classifiers, ensuring transparency and trustworthiness in AI.