Posters

Spectral graph pruning simultaneously mitigates over-squashing and over-smoothing in GNNs via edge deletion, improving generalization.

Spectral Editing of Activations (SEA) improves large language model truthfulness and fairness by projecting input representations to maximize covariance with positive demonstrations while minimizing c…

Spectral Adapter boosts parameter-efficient fine-tuning by incorporating pretrained weight matrices’ spectral information, enhancing efficiency and multi-adapter fusion.

SpecExec achieves massively parallel speculative decoding, enabling interactive 50B+ parameter LLM inference on consumer devices at 4-6 tokens/second.

Spec-Gaussian enhances 3D Gaussian splatting by using anisotropic spherical Gaussians for view-dependent appearance modeling, achieving superior real-time rendering of scenes with specular and anisotr…

SPEAR, a novel algorithm, precisely reconstructs entire data batches from gradients in federated learning, defying previous limitations and enhancing privacy risk assessment.

SpeAr: A novel spectral approach significantly improves zero-shot node classification by using inherent graph structure to reduce prediction bias and effectively identifying unseen node classes.

AI agents developed a communication system using spatial relationships, achieving over 90% accuracy in conveying relative positions of objects within a scene.

STIR: A novel spatio-temporal network reconstructs high-quality images from spiking camera data by jointly refining motion and intensity information for efficient and accurate high-speed imaging.

Spatio-Spectral GNNs synergistically combine spatial and spectral graph filters for efficient, global information propagation, overcoming limitations of existing methods.

SpatialRGPT enhances Vision-Language Models’ spatial reasoning by integrating 3D scene graphs and depth information, achieving significant performance gains on spatial reasoning tasks.

SpatialPIN boosts vision-language models’ spatial reasoning by cleverly combining prompting techniques with 3D foundation models, achieving zero-shot performance on various spatial tasks.

SparseLinUCB: First sparse regret bounds for adversarial action sets with unknown sparsity, achieving superior performance over existing methods!

SparseLLM globally prunes large language models efficiently by decomposing the problem into manageable subproblems, achieving significant performance improvements, especially at high sparsity.

SparseAGS: High-fidelity 3D reconstruction & camera pose estimation from sparse views via generative synthesis.

SµPar stabilizes sparse neural network training, slashing tuning costs and boosting performance, especially at high sparsity levels, via a novel parameterization technique.

A new learnable prior for compressive sensing solves the inverse problem using only a few corrupted data samples, enabling sparse signal recovery without ground-truth information and uncertainty quant…

SPARKLE: A single-loop primal-dual framework unifies decentralized bilevel optimization, enabling flexible heterogeneity-correction and mixed update strategies for improved convergence.

SpaFL: A communication-efficient federated learning framework that optimizes sparse model structures with low computational overhead by using trainable thresholds to prune model parameters.

SpaceByte: A novel byte-level decoder architecture achieving near-tokenized-model performance without tokenization!