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Found 11 papers, 4 papers with code
Generalizing across Temporal Domains with Koopman Operators
By employing Koopman Operators, we effectively address the time-evolving distributions encountered in TDG using the principles of Koopman theory, where measurement functions are sought to establish linear transition relations between evolving domains.
Hessian Aware Low-Rank Weight Perturbation for Continual Learning
We conduct extensive experiments on various benchmarks, including a dataset with large-scale tasks, and compare our method against some recent state-of-the-art methods to demonstrate the effectiveness and scalability of our proposed method.
Hone as You Read: A Practical Type of Interactive Summarization
We present HARE, a new task where reader feedback is used to optimize document summaries for personal interest during the normal flow of reading.
A Deep Learning Framework for Lifelong Machine Learning
Thus, while our framework is still conceptual, and our experiment results are surely not SOTA, we hope that this unified lifelong learning framework inspires new work towards large-scale experiments and understanding human learning in general.
Tackling Non-forgetting and Forward Transfer with a Unified Lifelong Learning Approach
Humans are the best example of agents that can learn a variety of skills incrementally over the course of their lives, and imbuing machines with this skill is the goal of lifelong machine learning.
Catching Attention with Automatic Pull Quote Selection
To advance understanding on how to engage readers, we advocate the novel task of automatic pull quote selection.
A Conceptual Framework for Lifelong Learning
Humans can learn a variety of concepts and skills incrementally over the course of their lives while exhibiting many desirable properties, such as continual learning without forgetting, forward transfer and backward transfer of knowledge, and learning a new concept or task with only a few examples.
Few-Shot Abstract Visual Reasoning With Spectral Features
We present an image preprocessing technique capable of improving the performance of few-shot classifiers on abstract visual reasoning tasks.
Learning Sentence Embeddings for Coherence Modelling and Beyond
We present a novel and effective technique for performing text coherence tasks while facilitating deeper insights into the data.
Pelee: A Real-Time Object Detection System on Mobile Devices
In this study, we propose an efficient architecture named PeleeNet, which is built with conventional convolution instead.
Clustering-Based Matrix Factorization
In this paper we propose an extension of matrix factorization which adds general neighborhood information on the recommendation model.
