Hebbian continual representation learning

Continual Learning aims to bring machine learning into a more realistic scenario, where tasks are learned sequentially and the i.i.d. assumption is not preserved. Although this setting is natural for biological systems, it proves very difficult for machine learning models such as artificial neural networks. To reduce this performance gap, we investigate the question whether biologically inspired Hebbian learning is useful for tackling continual challenges. In particular, we highlight a realistic and often overlooked unsupervised setting, where the learner has to build representations without any supervision. By combining sparse neural networks with Hebbian learning principle, we build a simple yet effective alternative (HebbCL) to typical neural network models trained via the gradient descent. Due to Hebbian learning, the network have easily interpretable weights, which might be essential in critical application such as security or healthcare. We demonstrate the efficacy of HebbCL in an unsupervised learning setting applied to MNIST and Omniglot datasets. We also adapt the algorithm to the supervised scenario and obtain promising results in the class-incremental learning

Hebbian Continual Representation Learning

Continual Learning aims to bring machine learning into a more realistic scenario, where tasks are learned sequentially and the i.i.d. assumption is not preserved. Although this setting is natural for biological systems, it proves very difficult for machine learning models such as artificial neural networks. To reduce this performance gap, we investigate the question whether biologically inspired Hebbian learning is useful for tackling continual challenges. In particular, we highlight a realistic and often overlooked unsupervised setting, where the learner has to build representations without any supervision. By combining sparse neural networks with Hebbian learning principle, we build a simple yet effective alternative (HebbCL) to typical neural network models trained via the gradient descent. Due to Hebbian learning, the network have easily interpretable weights, which might be essential in critical application such as security or healthcare. We demonstrate the efficacy of HebbCL in an unsupervised learning setting applied to MNIST and Omniglot datasets. We also adapt the algorithm to the supervised scenario and obtain promising results in the class-incremental learning

The Inter-batch Diversity of Samples in Experience Replay for Continual Learning

In a Continual Learning setting, models are trained on data with occasional distribution shifts, resulting in forgetting the information learned before each shift. Experience Replay (ER) addresses this challenge by retaining part of the old training samples and replaying them alongside current data, improving the model's understanding of the overall distribution in training batches. The crucial factor in ER performance is the diversity of samples within batches. The impact of sample diversity across a sequence of batches is investigated, introducing a new metric and an associated approach to assess and leverage this diversity. This exploration opens up significant potential for future work, as various strategies can be devised to ensure inter-batch diversity. Achieving optimal results may involve striking a balance between this novel metric and other inherent properties of a batch or sequence

Badanie użyteczności reprezentacji opartych o fizykę w cheminformatyce

Porównanie metod reprezentacji molekularnej w celu klasyfikacji aktywności cząsteczki w eksperymentach projektowania leków i poszukiwanie optymalnej kombinacji deskryptorów dla różnych modeli uczenia maszynowego. Ważnym zadaniem praktycznym jest sprawdzenie skuteczności wykorzystania fizycznej reprezentacji molekuł w klasycznych problemach cheminformatyki. Praca przedstawia wyniki eksperymentów przeprowadzonych na specjalnie stworzonym workbench "Moloi", który upraszcza pracę z różnymi reprezentacjami molekuł i modeli uczenia maszynowego. Program wykorzystuje kombinacje RDKit, Mordred, MACCS, morgan i Spectrophore do tworzenia zestawów fizycznych i strukturalnych deskryptorów dla zbiorów danych BACE i clintox z cząsteczkami w formacie SMILES.Comparison of the methods of molecular representation in the task of classifying the activity of a molecule in drug discovery experiments and searching for the optimal combination of descriptors for various models of machine learning. It is an important practical task to test the effectiveness of using the physical representation of molecules in the classic problems of cheminformatics. This thesis presents the results of experiments conducted on a specially created "Moloi" workbench that simplifies the work with different representations of molecules and models of machine learning. The program uses combinations of RDKit, Mordred, MACCS, morgan and Spectrophore descriptors and fingerprints to create sets of physical and structural descriptors for BACE and clintox datasets with molecules in the SMILES notation

Remember More by Recalling Less: Investigating the Role of Batch Size in Continual Learning with Experience Replay (Student Abstract)

Experience replay is a simple and well-performing strategy for continual learning problems, often used as a basis for more advanced methods. However, the dynamics of experience replay are not yet well understood. To showcase this, we focus on a single component of this problem, namely choosing the batch size of the buffer samples. We find that small batches perform much better at stopping forgetting than larger batches, contrary to the intuitive assumption that it is better to recall more samples from the past to avoid forgetting. We show that this phenomenon does not disappear under learning rate tuning and we propose possible directions for further analysis