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01/27/2023
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval
Large neural models (such as Transformers) achieve state-of-the-art perf...
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10/28/2022
When does mixup promote local linearity in learned representations?
Mixup is a regularization technique that artificially produces new sampl...
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05/12/2021
Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces
Negative sampling schemes enable efficient training given a large number...
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04/26/2021
Balancing Constraints and Submodularity in Data Subset Selection
Deep learning has yielded extraordinary results in vision and natural la...
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12/11/2019
Bipartite Conditional Random Fields for Panoptic Segmentation
We tackle the panoptic segmentation problem with a conditional random fi...
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12/03/2015
Prototypical Priors: From Improving Classification to Zero-Shot Learning
Recent works on zero-shot learning make use of side information such as ...
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02/11/2015
Conditional Random Fields as Recurrent Neural Networks
Pixel-level labelling tasks, such as semantic segmentation, play a centr...
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12/13/2014
Optimizing Over Radial Kernels on Compact Manifolds
We tackle the problem of optimizing over all possible positive definite ...
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12/13/2014
A Framework for Shape Analysis via Hilbert Space Embedding
We propose a framework for 2D shape analysis using positive definite ker...
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12/13/2014
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices
Symmetric Positive Definite (SPD) matrices have become popular to encode...
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11/30/2014
Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels
In this paper, we develop an approach to exploiting kernel methods with ...
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07/04/2014
