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๐Ÿข Imperial College London

UV-free Texture Generation with Denoising and Geodesic Heat Diffusion
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Computer Vision 3D Vision ๐Ÿข Imperial College London
UV3-TeD generates high-quality 3D textures directly on object surfaces using a novel diffusion probabilistic model, eliminating UV-mapping limitations.
Universal Sample Coding
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AI Generated Machine Learning Federated Learning ๐Ÿข Imperial College London
Universal Sample Coding revolutionizes data transmission by reducing bits needed to communicate multiple samples from an unknown distribution, achieving significant improvements in federated learning โ€ฆ
Transition Constrained Bayesian Optimization via Markov Decision Processes
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Machine Learning Reinforcement Learning ๐Ÿข Imperial College London
This paper presents a novel BayesOpt framework that incorporates Markov Decision Processes to optimize black-box functions with transition constraints, overcoming limitations of traditional methods.
Towards Universal Mesh Movement Networks
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๐Ÿข Imperial College London
Universal Mesh Movement Network (UM2N) revolutionizes mesh movement for PDE solvers, enabling zero-shot adaptation to diverse problems and significantly accelerating simulations with improved accuracyโ€ฆ
Theoretical Foundations of Deep Selective State-Space Models
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AI Theory Generalization ๐Ÿข Imperial College London
Deep learningโ€™s sequence modeling is revolutionized by selective state-space models (SSMs)! This paper provides theoretical grounding for their superior performance, revealing the crucial role of gatiโ€ฆ
OpenDlign: Open-World Point Cloud Understanding with Depth-Aligned Images
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Computer Vision 3D Vision ๐Ÿข Imperial College London
OpenDlign uses novel depth-aligned images from a diffusion model to boost open-world 3D understanding, achieving significant performance gains on diverse benchmarks.
Noether's Razor: Learning Conserved Quantities
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AI Generated Machine Learning Deep Learning ๐Ÿข Imperial College London
Noetherโ€™s Razor learns conserved quantities and symmetries directly from data via Bayesian model selection, improving dynamical systems modeling accuracy and generalizability.
Measuring Goal-Directedness
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AI Theory Ethics ๐Ÿข Imperial College London
New metric, Maximum Entropy Goal-Directedness (MEG), quantifies AI goal-directedness, crucial for assessing AI safety and agency.
Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention
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Machine Learning Representation Learning ๐Ÿข Imperial College London
Probabilistic Slot Attention achieves identifiable object-centric representations without supervision, advancing systematic generalization in machine learning.
ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling
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Computer Vision 3D Vision ๐Ÿข Imperial College London
ID-to-3D: Generate expressive, identity-consistent 3D human heads from just a few in-the-wild images using score distillation sampling and 2D diffusion models.
Feedback control guides credit assignment in recurrent neural networks
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AI Theory Optimization ๐Ÿข Imperial College London
Brain-inspired recurrent neural networks learn efficiently by using feedback control to approximate optimal gradients, enabling rapid movement corrections and efficient adaptation to persistent errorsโ€ฆ
Entrywise error bounds for low-rank approximations of kernel matrices
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AI Theory Optimization ๐Ÿข Imperial College London
This paper provides novel entrywise error bounds for low-rank kernel matrix approximations, showing how many data points are needed to get statistically consistent results for low-rank approximations.
Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces
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Machine Learning Deep Learning ๐Ÿข Imperial College London
Train discrete EBMs efficiently with Energy Discrepancy, a novel loss function that eliminates the need for Markov Chain Monte Carlo, using diffusion processes on structured spaces.
Absorb & Escape: Overcoming Single Model Limitations in Generating Heterogeneous Genomic Sequences
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Machine Learning Deep Learning ๐Ÿข Imperial College London
Absorb & Escape: a novel post-training sampling method that overcomes single model limitations by combining Autoregressive (AR) and Diffusion Models (DMs), generating high-quality heterogeneous genomiโ€ฆ