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Found 10 papers, 7 papers with code
Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks
Biased attributes, spuriously correlated with target labels in a dataset, can problematically lead to neural networks that learn improper shortcuts for classifications and limit their capabilities for out-of-distribution (OOD) generalization.
Randomly Weighted Neuromodulation in Neural Networks Facilitates Learning of Manifolds Common Across Tasks
Geometric Sensitive Hashing functions, a family of Local Sensitive Hashing functions, are neural network models that learn class-specific manifold geometry in supervised learning.
Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace
Many interpretable AI approaches have been proposed to provide plausible explanations for a model's decision-making.
Learning to Modulate Random Weights: Neuromodulation-inspired Neural Networks For Efficient Continual Learning
Existing Continual Learning (CL) approaches have focused on addressing catastrophic forgetting by leveraging regularization methods, replay buffers, and task-specific components.
Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship Detection
Furthermore, background knowledge represented by RWFNs can be used to alleviate the incompleteness of training sets even though the space complexity of RWFNs is much smaller than LTNs (1:27 ratio).
An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational Learning
We demonstrate that compared to LTNs, RWFNs can achieve better or similar performance for both object classification and detection of the part-of relations between objects in SII tasks while using much far fewer learnable parameters (1:62 ratio) and a faster learning process (1:2 ratio of running speed).
Beyond Tracking: Using Deep Learning to Discover Novel Interactions in Biological Swarms
Because the resulting predictive models are not based on human-understood predictors, we use explanatory modules (e. g., Grad-CAM) that combine information hidden in the latent variables of the deep-network model with the video data itself to communicate to a human observer which aspects of observed individual behaviors are most informative in predicting group behavior.
KCNet: An Insect-Inspired Single-Hidden-Layer Neural Network with Randomized Binary Weights for Prediction and Classification Tasks
Fruit flies are established model systems for studying olfactory learning as they will readily learn to associate odors with both electric shock or sugar rewards.
Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change
This method can be used to screen video frames for which additional human observation is needed.
Randomly Weighted, Untrained Neural Tensor Networks Achieve Greater Relational Expressiveness
Neural Tensor Networks (NTNs), which are structured to encode the degree of relationship among pairs of entities, are used in Logic Tensor Networks (LTNs) to facilitate Statistical Relational Learning (SRL) in first-order logic.
