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Found 24 papers, 13 papers with code
Avalanche: A PyTorch Library for Deep Continual Learning
Continual learning is the problem of learning from a nonstationary stream of data, a fundamental issue for sustainable and efficient training of deep neural networks over time.
Class-Incremental Learning with Repetition
We focus on the family of Class-Incremental with Repetition (CIR) scenarios, where repetition is embedded in the definition of the stream.
3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding
Continual Learning, also known as Lifelong or Incremental Learning, has recently gained renewed interest among the Artificial Intelligence research community.
Sample Condensation in Online Continual Learning
A popular solution in these scenario is to use a small memory to retain old data and rehearse them over time.
Continual Pre-Training Mitigates Forgetting in Language and Vision
We formalize and investigate the characteristics of the continual pre-training scenario in both language and vision environments, where a model is continually pre-trained on a stream of incoming data and only later fine-tuned to different downstream tasks.
Practical Recommendations for Replay-based Continual Learning Methods
Continual Learning requires the model to learn from a stream of dynamic, non-stationary data without forgetting previous knowledge.
Avalanche RL: a Continual Reinforcement Learning Library
Continual Reinforcement Learning (CRL) is a challenging setting where an agent learns to interact with an environment that is constantly changing over time (the stream of experiences).
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving
This paper presents a proof-of-concept implementation of the AI-as-a-Service toolkit developed within the H2020 TEACHING project and designed to implement an autonomous driving personalization system according to the output of an automatic driver's stress recognition algorithm, both of them realizing a Cyber-Physical System of Systems.
Ex-Model: Continual Learning from a Stream of Trained Models
Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years.
Is Class-Incremental Enough for Continual Learning?
The ability of a model to learn continually can be empirically assessed in different continual learning scenarios.
TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligence
This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum.
Continual Learning with Echo State Networks
Continual Learning (CL) refers to a learning setup where data is non stationary and the model has to learn without forgetting existing knowledge.
Avalanche: an End-to-End Library for Continual Learning
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning.
Distilled Replay: Overcoming Forgetting through Synthetic Samples
Replay strategies are Continual Learning techniques which mitigate catastrophic forgetting by keeping a buffer of patterns from previous experiences, which are interleaved with new data during training.
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification
In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario.
Continual Learning for Recurrent Neural Networks: an Empirical Evaluation
We propose two new benchmarks for CL with sequential data based on existing datasets, whose characteristics resemble real-world applications.
Short-Term Memory Optimization in Recurrent Neural Networks by Autoencoder-based Initialization
Training RNNs to learn long-term dependencies is difficult due to vanishing gradients.
Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory
The effectiveness of recurrent neural networks can be largely influenced by their ability to store into their dynamical memory information extracted from input sequences at different frequencies and timescales.
Continual Learning with Gated Incremental Memories for sequential data processing
The ability to learn in dynamic, nonstationary environments without forgetting previous knowledge, also known as Continual Learning (CL), is a key enabler for scalable and trustworthy deployments of adaptive solutions.
Encoding-based Memory Modules for Recurrent Neural Networks
The experimental results on synthetic and real-world datasets show that specializing the training algorithm to train the memorization component always improves the final performance whenever the memorization of long sequences is necessary to solve the problem.
Learning Style-Aware Symbolic Music Representations by Adversarial Autoencoders
Through the paper, we show how Gaussian mixtures taking into account music metadata information can be used as an effective prior for the autoencoder latent space, introducing the first Music Adversarial Autoencoder (MusAE).
Autoencoder-based Initialization for Recurrent Neural Networks with a Linear Memory
We propose an initialization schema that sets the weights of a recurrent architecture to approximate a linear autoencoder of the input sequences, which can be found with a closed-form solution.
Linear Memory Networks
By building on such conceptualization, we introduce the Linear Memory Network, a recurrent model comprising a feedforward neural network, realizing the non-linear functional transformation, and a linear autoencoder for sequences, implementing the memory component.
FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering
In this paper we present ThReeNN, a model for Community Question Answering, Task 3, of SemEval-2017.
