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Found 14 papers, 7 papers with code
Sparse coding for multitask and transfer learning
We investigate the use of sparse coding and dictionary learning in the context of multitask and transfer learning.
A New Convex Relaxation for Tensor Completion
We study the problem of learning a tensor from a set of linear measurements.
An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning
From concentration inequalities for the suprema of Gaussian or Rademacher processes an inequality is derived.
Conditional Random Fields as Recurrent Neural Networks
Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding.
Ranked #36 on
Semantic Segmentation
on PASCAL VOC 2012 test
The Benefit of Multitask Representation Learning
In particular, focusing on the important example of half-space learning, we derive the regime in which multitask representation learning is beneficial over independent task learning, as a function of the sample size, the number of tasks and the intrinsic data dimensionality.
An embarrassingly simple approach to zero-shot learning
Zero-shot learning consists in learning how to recognise new concepts by just having a description of them.
Ranked #8 on
Zero-Shot Action Recognition
on Olympics
Recurrent Instance Segmentation
Instance segmentation is the problem of detecting and delineating each distinct object of interest appearing in an image.
Prototypical Priors: From Improving Classification to Zero-Shot Learning
Recent works on zero-shot learning make use of side information such as visual attributes or natural language semantics to define the relations between output visual classes and then use these relationships to draw inference on new unseen classes at test time.
A Probabilistic U-Net for Segmentation of Ambiguous Images
To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses.
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
We demonstrate the model's clinical applicability by assessing its performance on a test set of 21 CT scans from clinical practice, each with the 21 OARs segmented by two independent experts.
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
Medical imaging only indirectly measures the molecular identity of the tissue within each voxel, which often produces only ambiguous image evidence for target measures of interest, like semantic segmentation.
Highly accurate protein structure prediction with AlphaFold
Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics.
Discovering faster matrix multiplication algorithms with reinforcement learning
Particularly relevant is the case of 4 × 4 matrices in a finite field, where AlphaTensor’s algorithm improves on Strassen’s two-level algorithm for the first time, to our knowledge, since its discovery 50 years ago2.
Quantum Circuit Optimization with AlphaTensor
A key challenge in realizing fault-tolerant quantum computers is circuit optimization.
