Posters

Newton Losses enhance training of neural networks with complex objectives by using second-order information from loss functions, achieving significant performance gains.

Newton Informed Neural Operator efficiently solves nonlinear PDEs with multiple solutions by learning the Newton solver, enabling faster computation and the discovery of new solutions with limited dat…

Neuronal Competition Groups (NCGs) enhance supervised STDP training in spiking neural networks by promoting balanced competition and improved class separation, resulting in significantly higher classi…

NeuroGauss4D-PCI masters complex point cloud interpolation using 4D neural fields and Gaussian deformation fields, achieving superior accuracy in dynamic scenes.

NeuRodin: A two-stage neural framework achieves high-fidelity 3D surface reconstruction from posed RGB images by innovatively addressing limitations in SDF-based methods, resulting in superior reconst…

NeuroBOLT: Resting-state EEG-to-fMRI synthesis using multi-dimensional feature mapping.

Researchers enhanced brain recording-based visual reconstruction using a novel Vision Transformer 3D framework integrated with LLMs, achieving superior performance in visual reconstruction, captioning…

Neuro-symbolic framework generates high-quality mathematical datasets, enhancing LLMs’ mathematical reasoning capabilities and surpassing state-of-the-art counterparts.

NeuralSteiner uses deep learning to predict Steiner points for efficient, overflow-avoiding global routing in chip design, achieving up to a 99.8% overflow reduction on large benchmarks.

NeuralSolver: A novel recurrent solver efficiently and consistently extrapolates algorithms from smaller problems to larger ones, handling various problem sizes.

NeuralFuse: A novel add-on module learns input transformations to maintain accuracy in low-voltage DNN inference, achieving up to 57% accuracy recovery and 24% energy savings without retraining.

NeuralFluid: Design & control complex fluidic systems with dynamic boundaries using differentiable simulation, achieving superior results in benchmark tasks.

NeuralClothSim: A new quasistatic cloth simulator using thin shells represented by neural fields, enabling memory-efficient, resolution-independent, physically-accurate simulations.

Neural SDF inference is revolutionized by dynamically aligning 3D Gaussians to a neural SDF’s zero-level set, enabling accurate, smooth 3D surface reconstruction.

Neural-RDM: A novel framework for deep, scalable vision generation using residual diffusion models, achieving state-of-the-art results on image and video benchmarks.

Learn disentangled 3D object poses and transfer them between different object identities using a novel neural pose representation, boosting 3D shape generation!

Neural Persistence Dynamics learns collective behavior from topological features, accurately predicting parameters of governing equations without tracking individual entities.

Neural P³M enhances geometric GNNs by incorporating mesh points to model long-range interactions in molecules, achieving state-of-the-art accuracy in predicting energy and forces.

Accelerate molecular simulations using neural network reparametrization! This flexible method adjusts system complexity, enhances optimization, and maintains continuous access to fine-grained modes, o…

SGD can train neural networks to learn low-dimensional polynomials near the information-theoretic limit, surpassing previous correlational statistical query lower bounds.