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Found 10 papers, 5 papers with code
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games
In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system.
Lifelong Wandering: A realistic few-shot online continual learning setting
Online few-shot learning describes a setting where models are trained and evaluated on a stream of data while learning emerging classes.
A Closer Look at Knowledge Distillation with Features, Logits, and Gradients
Knowledge distillation (KD) is a substantial strategy for transferring learned knowledge from one neural network model to another.
Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning
Modern computer vision applications suffer from catastrophic forgetting when incrementally learning new concepts over time.
Ranked #5 on
Class Incremental Learning
on cifar100
On the Adversarial Robustness of Quantized Neural Networks
Reducing the size of neural network models is a critical step in moving AI from a cloud-centric to an edge-centric (i. e. on-device) compute paradigm.
Lipid Traffic Analysis reveals the impact of high paternal carbohydrate intake on offsprings’ lipid metabolism
Using a purpose-built computational tool for analysing both phospholipid and fat metabolism as a network, we characterised the number, type and abundance of lipid variables in and between tissues (Lipid Traffic Analysis), finding a variety of reprogrammings associated with paternal diet.
Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer
Our work investigates whether we can significantly reduce this memory budget by leveraging unlabeled data from an agent's environment in a realistic and challenging continual learning paradigm.
Eliminating Left Recursion without the Epsilon
The standard algorithm to eliminate indirect left recursion takes a preventative approach, rewriting a grammar's rules so that indirect left recursion is no longer possible, rather than eliminating it only as and when it occurs.
Data Structures and Algorithms
Unsupervised Progressive Learning and the STAM Architecture
We first pose the Unsupervised Progressive Learning (UPL) problem: an online representation learning problem in which the learner observes a non-stationary and unlabeled data stream, learning a growing number of features that persist over time even though the data is not stored or replayed.
Unsupervised Continual Learning and Self-Taught Associative Memory Hierarchies
We first pose the Unsupervised Continual Learning (UCL) problem: learning salient representations from a non-stationary stream of unlabeled data in which the number of object classes varies with time.
