Online Learning of a Memory for Learning Rates

The promise of learning to learn for robotics rests on the hope that by extracting some information about the learning process itself we can speed up subsequent similar learning tasks. Here, we introduce a computationally efficient online meta-learning algorithm that builds and optimizes a memory model of the optimal learning rate landscape from previously observed gradient behaviors. While performing task specific optimization, this memory of learning rates predicts how to scale currently observed gradients. After applying the gradient scaling our meta-learner updates its internal memory based on the observed effect its prediction had. Our meta-learner can be combined with any gradient-based optimizer, learns on the fly and can be transferred to new optimization tasks. In our evaluations we show that our meta-learning algorithm speeds up learning of MNIST classification and a variety of learning control tasks, either in batch or online learning settings.Comment: accepted to ICRA 2018, code available: https://github.com/fmeier/online-meta-learning ; video pitch available: https://youtu.be/9PzQ25FPPO

Online learning and detection of faces with low human supervision

The final publication is available at link.springer.comWe present an efficient,online,and interactive approach for computing a classifier, called Wild Lady Ferns (WiLFs), for face learning and detection using small human supervision. More precisely, on the one hand, WiLFs combine online boosting and extremely randomized trees (Random Ferns) to compute progressively an efficient and discriminative classifier. On the other hand, WiLFs use an interactive human-machine approach that combines two complementary learning strategies to reduce considerably the degree of human supervision during learning. While the first strategy corresponds to query-by-boosting active learning, that requests human assistance over difficult samples in function of the classifier confidence, the second strategy refers to a memory-based learning which uses ¿ Exemplar-based Nearest Neighbors (¿ENN) to assist automatically the classifier. A pre-trained Convolutional Neural Network (CNN) is used to perform ¿ENN with high-level feature descriptors. The proposed approach is therefore fast (WilFs run in 1 FPS using a code not fully optimized), accurate (we obtain detection rates over 82% in complex datasets), and labor-saving (human assistance percentages of less than 20%). As a byproduct, we demonstrate that WiLFs also perform semi-automatic annotation during learning, as while the classifier is being computed, WiLFs are discovering faces instances in input images which are used subsequently for training online the classifier. The advantages of our approach are demonstrated in synthetic and publicly available databases, showing comparable detection rates as offline approaches that require larger amounts of handmade training data.Peer ReviewedPostprint (author's final draft

Learning deficit in cognitively normal apoe ε4 carriers with low β-amyloid

Introduction: In cognitively normal (CN) adults, increased rates of amyloid beta (Aβ) accumulation can be detected in low Aβ (Aβ–) apolipoprotein E (APOE) ε4 carriers. We aimed to determine the effect of ε4 on the ability to benefit from experience (ie, learn) in Aβ–CNs. Methods: Aβ– CNs(n= 333) underwent episodic memory assessments every 18 months for 108 months. A subset (n = 48) completed the Online Repeatable Cognitive Assessment-Language Learning Test (ORCA-LLT) over 6 days. Results: Aβ– ε4 carriers showed significantly lower rates of improvement on episodic memory over 108 months compared to non-carriers (d = 0.3). Rates of learning on the ORCA-LLT were significantly slower in Aβ– ε4 carriers compared to non-carriers (d = 1.2). Discussion: In Aβ– CNs,ε4 is associated with a reduced ability to benefit from experience. This manifested as reduced practice effects (small to moderate in magnitude) over 108 months on the episodic memory composite, and a learning deficit (large in magnitude) over 6 days on the ORCA-LLT. Alzheimer’s disease (AD)–related cognitive abnormalities can manifest before preclinical AD thresholds