Posters

Quantum algorithms achieve speedups in non-smooth, non-convex optimization, outperforming classical methods by a factor of ε⁻²/³ in query complexity for finding (δ,ε)-Goldstein stationary points.

Quantum speedup achieved for large-scale market equilibrium computation!

Accelerated Lie group optimization achieved via a novel momentum algorithm (Lie NAG-SC) with proven convergence rates, surpassing existing methods in efficiency.

Weakly supervised strong models outperform weak models; this gain is precisely quantified by the strong model’s misfit error on weak labels.

New APC metric precisely quantifies & optimizes global faithfulness in persona-driven role-playing, offering a fine-grained, explainable evaluation and improving AI character consistency.

New orthogonal learner quantifies treatment effect’s randomness, providing sharper insights beyond average effects.

QuanTA: Quantum-inspired Tensor Adaptation efficiently fine-tunes LLMs with high-rank updates, surpassing low-rank methods like LoRA for complex tasks while minimizing additional parameters.

This paper introduces a novel three-phase neural network framework that significantly enhances the quality of polarimetric images by complementarily fusing degraded noisy and blurry snapshots, preserv…

Researchers introduce QIM-compatibility, a novel framework for modeling qualitative relationships in probability distributions using directed hypergraphs, significantly expanding beyond standard condi…

Q2VMC, a novel quantum chemistry algorithm, drastically boosts the efficiency and accuracy of solving the Schrödinger equation using a quadratic update mechanism and neural network ansatzes.

QuadMamba: A novel vision model leveraging quadtree-based scanning for superior performance in visual tasks, achieving state-of-the-art results with linear-time complexity.

QT-ViT boosts Vision Transformer efficiency by using quadratic Taylor expansion to approximate self-attention, achieving state-of-the-art accuracy and speed.

QGFN boosts Generative Flow Networks (GFNs) by cleverly combining their sampling policy with an action-value estimate, creating controllable and efficient generation of high-reward samples.

QBB: A novel post-training quantization method for LLMs dramatically improves efficiency by replacing multiplications with summations, achieving state-of-the-art results with minimal accuracy loss.

Q-VLM: A novel post-training quantization framework significantly compresses large vision-language models, boosting inference speed without sacrificing accuracy.

Offline RL struggles with OOD action overestimation. QDQ tackles this by penalizing uncertain Q-values using a consistency model, enhancing offline RL performance.

Researchers improve two-sided matching market algorithms by prioritizing stability through novel bandit-learning algorithms, providing theoretical bounds on sample complexity and demonstrating intrigu…

PUREGEN uses generative model dynamics to purify poisoned training data, providing a universal, effective, and efficient train-time defense against various data poisoning attacks.

PURE: A novel method uses Graph ODE to adapt spatio-temporal forecasting models to various fluid dynamics scenarios, improving model adaptation to unseen parameters and long-term predictions.

Pure message passing in graph neural networks can accurately estimate common neighbor heuristics for superior link prediction.