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Article
Poincaré Kernels for Hyperbolic Representations
Embedding data in hyperbolic spaces has proven beneficial for many advanced machine learning applications. However, working in hyperbolic spaces is not without difficulties as a result of its curved geometry (e.g
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Chapter and Conference Paper
L3DMC: Lifelong Learning Using Distillation via Mixed-Curvature Space
The performance of a lifelong learning (L3) model degrades when it is trained on a series of tasks, as the geometrical formation of the embedding space changes while learning novel concepts sequentially. The m...
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Chapter and Conference Paper
EndoSurf: Neural Surface Reconstruction of Deformable Tissues with Stereo Endoscope Videos
Reconstructing soft tissues from stereo endoscope videos is an essential prerequisite for many medical applications. Previous methods struggle to produce high-quality geometry and appearance due to their inade...
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Chapter and Conference Paper
A Differentiable Distance Approximation for Fairer Image Classification
Naïvely trained AI models can be heavily biased. This can be particularly problematic when the biases involve legally or morally protected attributes such as ethnic background, age or gender. Existing solution...
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Chapter and Conference Paper
Deep Laparoscopic Stereo Matching with Transformers
The self-attention mechanism, successfully employed with the transformer structure is shown promise in many computer vision tasks including image recognition, and object detection. Despite the surge, the use o...
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Chapter and Conference Paper
A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation
We propose a Transformer architecture for volumetric segmentation, a challenging task that requires kee** a complex balance in encoding local and global spatial cues, and preserving information along all axe...
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Article
Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds
Sparsity-based representations have recently led to notable results in various visual recognition tasks. In a separate line of research, Riemannian manifolds have been shown useful for dealing with features an...