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Found 7 papers, 4 papers with code
Read Between the Layers: Leveraging Intra-Layer Representations for Rehearsal-Free Continual Learning with Pre-Trained Models
We address the Continual Learning (CL) problem, wherein a model must learn a sequence of tasks from non-stationary distributions while preserving prior knowledge upon encountering new experiences.
Visually Grounded Continual Language Learning with Selective Specialization
Selective specialization, i. e., a careful selection of model components to specialize in each task, is a strategy to provide control over this trade-off.
Neuro-Symbolic Spatio-Temporal Reasoning
In this chapter, we view this integration problem from the perspective of Neuro-Symbolic AI.
Knowing Earlier what Right Means to You: A Comprehensive VQA Dataset for Grounding Relative Directions via Multi-Task Learning
Spatial reasoning poses a particular challenge for intelligent agents and is at the same time a prerequisite for their successful interaction and communication in the physical world.
What is Right for Me is Not Yet Right for You: A Dataset for Grounding Relative Directions via Multi-Task Learning
Grounding relative directions is more difficult than grounding absolute directions because it not only requires a model to detect objects in the image and to identify spatial relation based on this information, but it also needs to recognize the orientation of objects and integrate this information into the reasoning process.
Explain yourself! Effects of Explanations in Human-Robot Interaction
However, the results show that the robot that explains its moves is perceived as more lively and human-like.
DRILL: Dynamic Representations for Imbalanced Lifelong Learning
Continual or lifelong learning has been a long-standing challenge in machine learning to date, especially in natural language processing (NLP).
