scholarly journals Learning to learn: 8-month-old infants meta-learn from sparse evidence

2021 ◽  
Author(s):  
Francesco Poli ◽  
Tommaso Ghilardi ◽  
Rogier B. Mars ◽  
Max Hinne ◽  
Sabine Hunnius
Keyword(s):  
Hierarchical Models ◽  
Learning Task ◽  
Social World ◽  
Task Structure ◽  
Learning To Learn ◽  
Meta Learning ◽  
Gaze Behaviour ◽  
Efficient Learning ◽  
Prior Experiences ◽  
Future Information

Infants learn to navigate the complexity of the physical and social world at an outstanding pace, but how they accomplish this learning is still unknown. Recent advances in human and artificial intelligence research propose that a key feature to achieve quick and efficient learning is meta-learning, the ability to make use of prior experiences to optimize how future information is acquired. Here we show that 8-month-old infants successfully engage in meta-learning within very short timespans. We developed a Bayesian model that captures how infants attribute informativity to incoming events, and how this process is optimized by the meta-parameters of their hierarchical models over the task structure. We fitted the model using infants’ gaze behaviour during a learning task. Our results reveal that infants do not simply accumulate experiences, but actively use them to generate new inductive biases that allow learning to proceed faster in the future.

scholarly journals Dataset2Vec: learning dataset meta-features

2021 ◽  
Author(s):  
Hadi S. Jomaa ◽  
Lars Schmidt-Thieme ◽  
Josif Grabocka
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Learning Task ◽  
Similarity Learning ◽  
Learning To Learn ◽  
Prior Learning ◽  
Optimization Task ◽  
Learning Tasks ◽  
Meta Learning ◽  
Data Driven Approach

AbstractMeta-learning, or learning to learn, is a machine learning approach that utilizes prior learning experiences to expedite the learning process on unseen tasks. As a data-driven approach, meta-learning requires meta-features that represent the primary learning tasks or datasets, and are estimated traditonally as engineered dataset statistics that require expert domain knowledge tailored for every meta-task. In this paper, first, we propose a meta-feature extractor called Dataset2Vec that combines the versatility of engineered dataset meta-features with the expressivity of meta-features learned by deep neural networks. Primary learning tasks or datasets are represented as hierarchical sets, i.e., as a set of sets, esp. as a set of predictor/target pairs, and then a DeepSet architecture is employed to regress meta-features on them. Second, we propose a novel auxiliary meta-learning task with abundant data called dataset similarity learning that aims to predict if two batches stem from the same dataset or different ones. In an experiment on a large-scale hyperparameter optimization task for 120 UCI datasets with varying schemas as a meta-learning task, we show that the meta-features of Dataset2Vec outperform the expert engineered meta-features and thus demonstrate the usefulness of learned meta-features for datasets with varying schemas for the first time.

scholarly journals Information-Theoretic Generalization Bounds for Meta-Learning and Applications

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 126
Author(s):  
Sharu Theresa Jose ◽  
Osvaldo Simeone
Keyword(s):  
Learning Algorithm ◽  
Broad Class ◽  
Performance Measure ◽  
Training Data ◽  
Learning To Learn ◽  
Data Set ◽  
Information Theoretic ◽  
Meta Learning ◽  
Task Training ◽  
Test Sets

Meta-learning, or “learning to learn”, refers to techniques that infer an inductive bias from data corresponding to multiple related tasks with the goal of improving the sample efficiency for new, previously unobserved, tasks. A key performance measure for meta-learning is the meta-generalization gap, that is, the difference between the average loss measured on the meta-training data and on a new, randomly selected task. This paper presents novel information-theoretic upper bounds on the meta-generalization gap. Two broad classes of meta-learning algorithms are considered that use either separate within-task training and test sets, like model agnostic meta-learning (MAML), or joint within-task training and test sets, like reptile. Extending the existing work for conventional learning, an upper bound on the meta-generalization gap is derived for the former class that depends on the mutual information (MI) between the output of the meta-learning algorithm and its input meta-training data. For the latter, the derived bound includes an additional MI between the output of the per-task learning procedure and corresponding data set to capture within-task uncertainty. Tighter bounds are then developed for the two classes via novel individual task MI (ITMI) bounds. Applications of the derived bounds are finally discussed, including a broad class of noisy iterative algorithms for meta-learning.

scholarly journals Adaptive quantum state tomography with neural networks

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yihui Quek ◽  
Stanislav Fort ◽  
Hui Khoon Ng
Keyword(s):  
Machine Learning ◽  
Quantum State ◽  
The State ◽  
Learning To Learn ◽  
Seamless Integration ◽  
Quantum State Tomography ◽  
Flexible Machine ◽  
Meta Learning ◽  
Long Time ◽  
State Tomography

AbstractCurrent algorithms for quantum state tomography (QST) are costly both on the experimental front, requiring measurement of many copies of the state, and on the classical computational front, needing a long time to analyze the gathered data. Here, we introduce neural adaptive quantum state tomography (NAQT), a fast, flexible machine-learning-based algorithm for QST that adapts measurements and provides orders of magnitude faster processing while retaining state-of-the-art reconstruction accuracy. As in other adaptive QST schemes, measurement adaptation makes use of the information gathered from previous measured copies of the state to perform a targeted sensing of the next copy, maximizing the information gathered from that next copy. Our NAQT approach allows for a rapid and seamless integration of measurement adaptation and statistical inference, using a neural-network replacement of the standard Bayes’ update, to obtain the best estimate of the state. Our algorithm, which falls into the machine learning subfield of “meta-learning” (in effect “learning to learn” about quantum states), does not require any ansatz about the form of the state to be estimated. Despite this generality, it can be retrained within hours on a single laptop for a two-qubit situation, which suggests a feasible time-cost when extended to larger systems and potential speed-ups if provided with additional structure, such as a state ansatz.

scholarly journals Spontaneous task structure formation results in a cost to incidental memory of task stimuli

2018 ◽  
Author(s):  
Christina Bejjani ◽  
Tobias Egner
Keyword(s):  
Structure Formation ◽  
Structure Learning ◽  
Learning Task ◽  
Underlying Structure ◽  
Task Structure ◽  
Cognitive Demands ◽  
Task Set ◽  
Stimulus Response ◽  
Attention To Task ◽  
Task Sets

Humans are characterized by their ability to leverage rules for classifying and linking stimuli to context-appropriate actions. Previous studies have shown that when humans learn stimulus-response associations for two-dimensional stimuli, they implicitly form and generalize hierarchical rule structures (task-sets). However, the cognitive processes underlying structure formation are poorly understood. Across four experiments, we manipulated how trial-unique images mapped onto responses to bias spontaneous task-set formation and investigated structure learning through the lens of incidental stimulus encoding. Participants performed a learning task designed to either promote task-set formation (by “motor-clustering” possible stimulus-action rules), or to discourage it (by using arbitrary category-response mappings). We adjudicated between two hypotheses: Structure learning may promote attention to task stimuli, thus resulting in better subsequent memory. Alternatively, building task-sets might impose cognitive demands (for instance, on working memory) that divert attention away from stimulus encoding. While the clustering manipulation affected task-set formation, there were also substantial individual differences. Importantly, structure learning incurred a cost: spontaneous task-set formation was associated with diminished stimulus encoding. Thus, spontaneous hierarchical task-set formation appears to involve cognitive demands that divert attention away from encoding of task stimuli during structure learning.

Theoretical and Empirical Analysis of a Spatial EA Parallel Boosting Algorithm

2018 ◽  
Vol 26 (1) ◽  
pp. 43-66 ◽  
Author(s):  
Uday Kamath ◽  
Carlotta Domeniconi ◽  
Kenneth De Jong
Keyword(s):  
Real World ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Real World Data ◽  
Meta Level ◽  
Meta Learning ◽  
Robustness To Noise ◽  
Boosting Algorithm ◽  
Efficient Learning ◽  
Empirical Analyses

Many real-world problems involve massive amounts of data. Under these circumstances learning algorithms often become prohibitively expensive, making scalability a pressing issue to be addressed. A common approach is to perform sampling to reduce the size of the dataset and enable efficient learning. Alternatively, one customizes learning algorithms to achieve scalability. In either case, the key challenge is to obtain algorithmic efficiency without compromising the quality of the results. In this article we discuss a meta-learning algorithm (PSBML) that combines concepts from spatially structured evolutionary algorithms (SSEAs) with concepts from ensemble and boosting methodologies to achieve the desired scalability property. We present both theoretical and empirical analyses which show that PSBML preserves a critical property of boosting, specifically, convergence to a distribution centered around the margin. We then present additional empirical analyses showing that this meta-level algorithm provides a general and effective framework that can be used in combination with a variety of learning classifiers. We perform extensive experiments to investigate the trade-off achieved between scalability and accuracy, and robustness to noise, on both synthetic and real-world data. These empirical results corroborate our theoretical analysis, and demonstrate the potential of PSBML in achieving scalability without sacrificing accuracy.

scholarly journals Meta Reinforcement Learning with Task Embedding and Shared Policy

2019 ◽  
Author(s):  
Lin Lan ◽  
Zhenguo Li ◽  
Xiaohong Guan ◽  
Pinghui Wang
Keyword(s):  
Reinforcement Learning ◽  
The Other ◽  
Specific Information ◽  
Significant Progress ◽  
Learning To Learn ◽  
Learning Capacity ◽  
Shared Information ◽  
Meta Learning ◽  
The One ◽  
High Level

Despite significant progress, deep reinforcement learning (RL) suffers from data-inefficiency and limited generalization. Recent efforts apply meta-learning to learn a meta-learner from a set of RL tasks such that a novel but related task could be solved quickly. Though specific in some ways, different tasks in meta-RL are generally similar at a high level. However, most meta-RL methods do not explicitly and adequately model the specific and shared information among different tasks, which limits their ability to learn training tasks and to generalize to novel tasks. In this paper, we propose to capture the shared information on the one hand and meta-learn how to quickly abstract the specific information about a task on the other hand. Methodologically, we train an SGD meta-learner to quickly optimize a task encoder for each task, which generates a task embedding based on past experience. Meanwhile, we learn a policy which is shared across all tasks and conditioned on task embeddings. Empirical results on four simulated tasks demonstrate that our method has better learning capacity on both training and novel tasks and attains up to 3 to 4 times higher returns compared to baselines.

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