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Posters

2024

Neural Model Checking
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AI Theory Safety 🏒 University of Birmingham
Neural networks revolutionize hardware model checking by generating formal proof certificates, outperforming state-of-the-art techniques in speed and scalability.
Neural Localizer Fields for Continuous 3D Human Pose and Shape Estimation
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Computer Vision 3D Vision 🏒 University of Tübingen
Neural Localizer Fields (NLF) revolutionizes 3D human pose and shape estimation by learning a continuous field of point localizer functions, enabling flexible training on diverse data and on-the-fly p…
Neural Isometries: Taming Transformations for Equivariant ML
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Computer Vision 3D Vision 🏒 PlayStation
Neural Isometries learns a latent space where geometric relationships in the observation space are represented as isometries in the latent space, enabling efficient handling of complex symmetries and …
Neural Gaffer: Relighting Any Object via Diffusion
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Computer Vision Image Generation 🏒 Cornell University
Neural Gaffer: Relighting any object via diffusion using a single image and an environment map to produce high-quality, realistic relit images.
Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling
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Machine Learning Deep Learning 🏒 University of Amsterdam
Neural Flow Diffusion Models (NFDM) revolutionize generative modeling by introducing a learnable forward process, resulting in state-of-the-art likelihoods and versatile generative dynamics.
Neural Experts: Mixture of Experts for Implicit Neural Representations
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Computer Vision 3D Vision 🏒 Roblox
Boosting implicit neural representations, Neural Experts uses a Mixture of Experts architecture to achieve faster, more accurate, and memory-efficient signal reconstruction across various tasks.
Neural Embeddings Rank: Aligning 3D latent dynamics with movements
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Machine Learning Deep Learning 🏒 Johns Hopkins University
Neural Embeddings Rank (NER) aligns 3D latent neural dynamics with movements, enabling cross-session decoding and revealing consistent neural dynamics across brain areas.
Neural decoding from stereotactic EEG: accounting for electrode variability across subjects
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Machine Learning Transfer Learning 🏒 Stanford University
Scalable SEEG decoding model, seegnificant, leverages transformers to decode behavior across subjects despite electrode variability, achieving high accuracy and transfer learning capability.
Neural Cover Selection for Image Steganography
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AI Generated Computer Vision Image Generation 🏒 University of Texas at Austin
This study introduces a neural cover selection framework for image steganography, optimizing latent spaces in generative models to improve message recovery and image quality.
Neural Conditional Probability for Uncertainty Quantification
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Machine Learning Deep Learning 🏒 CSML, Istituto Italiano Di Tecnologia
Neural Conditional Probability (NCP) offers a new operator-theoretic approach for efficiently learning conditional distributions, enabling streamlined inference and providing theoretical guarantees fo…
Neural Concept Binder
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Computer Vision Visual Question Answering 🏒 Computer Science Department, TU Darmstadt
The Neural Concept Binder (NCB) framework learns expressive, inspectable, and revisable visual concepts unsupervised, integrating both continuous and discrete representations for seamless use in neura…
Neural Combinatorial Optimization for Robust Routing Problem with Uncertain Travel Times
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AI Theory Optimization 🏒 Sun Yat-Sen University
Neural networks efficiently solve robust routing problems with uncertain travel times, minimizing worst-case deviations from optimal routes under the min-max regret criterion.
Neural collapse vs. low-rank bias: Is deep neural collapse really optimal?
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AI Generated AI Theory Optimization 🏒 Institute of Science and Technology Austria
Deep neural collapse, previously believed optimal, is shown suboptimal in multi-class, multi-layer networks due to a low-rank bias, yielding even lower-rank solutions.
Neural Collapse To Multiple Centers For Imbalanced Data
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Machine Learning Deep Learning 🏒 Shanxi University
Researchers enhance imbalanced data classification by inducing Neural Collapse to Multiple Centers (NCMC) using a novel cosine loss function, achieving performance comparable to state-of-the-art metho…
Neural Collapse Inspired Feature Alignment for Out-of-Distribution Generalization
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Machine Learning Deep Learning 🏒 Tsinghua University
Neural Collapse-inspired Feature Alignment (NCFAL) significantly boosts out-of-distribution generalization by aligning semantic features to a simplex ETF, even without environment labels.
Neural Characteristic Activation Analysis and Geometric Parameterization for ReLU Networks
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AI Generated Machine Learning Deep Learning 🏒 University of Cambridge
Researchers introduce Geometric Parameterization (GmP), a novel neural network parameterization resolving instability in ReLU network training, leading to faster convergence and better generalization.
Neur2BiLO: Neural Bilevel Optimization
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AI Theory Optimization 🏒 University of Toronto
NEUR2BILO: a neural network-based heuristic solves mixed-integer bilevel optimization problems extremely fast, achieving high-quality solutions for diverse applications.
NeuMA: Neural Material Adaptor for Visual Grounding of Intrinsic Dynamics
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Computer Vision 3D Vision 🏒 MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University
NeuMA: a novel neural material adaptor corrects existing physical models, accurately learning complex dynamics from visual observations.
Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms
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AI Generated Machine Learning Dimensionality Reduction 🏒 Rutgers University
Neuc-MDS: Revolutionizing multidimensional scaling by using bilinear forms for non-Euclidean data, minimizing errors, and resolving the dimensionality paradox!
Nesterov acceleration despite very noisy gradients
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AI Generated AI Theory Optimization 🏒 University of Pittsburgh
AGNES, a novel accelerated gradient descent algorithm, achieves accelerated convergence even with very noisy gradients, significantly improving training efficiency for machine learning models.