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08/27/2023
Semi-Supervised Learning in the Few-Shot Zero-Shot Scenario
Semi-Supervised Learning (SSL) leverages both labeled and unlabeled data...
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08/27/2023
Pruning the Unlabeled Data to Improve Semi-Supervised Learning
In the domain of semi-supervised learning (SSL), the conventional approa...
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06/06/2023
How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget
In Active Learning (AL), a learner actively chooses which unlabeled exam...
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10/02/2022
The Dynamic of Consensus in Deep Networks and the Identification of Noisy Labels
Deep neural networks have incredible capacity and expressibility, and ca...
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05/23/2022
Active Learning Through a Covering Lens
Deep active learning aims to reduce the annotation cost for deep neural ...
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02/06/2022
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets
Investigating active learning, we focus on the relation between the numb...
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09/13/2021
The Grammar-Learning Trajectories of Neural Language Models
The learning trajectories of linguistic phenomena provide insight into t...
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05/12/2021
Convergence Analysis of Over-parameterized Deep Linear Networks, and the Principal Components Bias
Convolutional Neural networks of different architectures seem to learn t...
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02/09/2021
More Is More – Narrowing the Generalization Gap by Adding Classification Heads
Overfit is a fundamental problem in machine learning in general, and in ...
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12/01/2020
Boosting the Performance of Semi-Supervised Learning with Unsupervised Clustering
Recently, Semi-Supervised Learning (SSL) has shown much promise in lever...
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11/25/2020
Multiclass non-Adversarial Image Synthesis, with Application to Classification from Very Small Sample
The generation of synthetic images is currently being dominated by Gener...
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03/31/2020
Learning from Small Data Through Sampling an Implicit Conditional Generative Latent Optimization Model
We revisit the long-standing problem of learning from small sample. In r...
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12/05/2019
Multi-Modal Deep Clustering: Unsupervised Partitioning of Images
The clustering of unlabeled raw images is a daunting task, which has rec...
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05/26/2019
All Neural Networks are Created Equal
One of the unresolved questions in the context of deep learning is the t...
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04/07/2019
On The Power of Curriculum Learning in Training Deep Networks
Training neural networks is traditionally done by providing a sequence o...
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01/30/2019
Blurred Images Lead to Bad Local Minima
Blurred Images Lead to Bad Local Minima...
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12/09/2018
Theory of Curriculum Learning, with Convex Loss Functions
Curriculum Learning - the idea of teaching by gradually exposing the lea...
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08/30/2018
Gaussian Mixture Generative Adversarial Networks for Diverse Datasets, and the Unsupervised Clustering of Images
Generative Adversarial Networks (GANs) have been shown to produce realis...
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02/11/2018
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks
Our first contribution in this paper is a theoretical investigation of c...
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09/28/2017
Distance-based Confidence Score for Neural Network Classifiers
The reliable measurement of confidence in classifiers' predictions is ve...
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04/20/2017
Every Untrue Label is Untrue in its Own Way: Controlling Error Type with the Log Bilinear Loss
Deep learning has become the method of choice in many application domain...
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04/21/2016
Novelty Detection in MultiClass Scenarios with Incomplete Set of Class Labels
We address the problem of novelty detection in multiclass scenarios wher...
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11/17/2015
Optimized Linear Imputation
Often in real-world datasets, especially in high dimensional data, some ...
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01/27/2015