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Found 17 papers, 7 papers with code
Causal State Distillation for Explainable Reinforcement Learning
This lack of transparency in RL models has been a long-standing problem, making it difficult for users to grasp the reasons behind an agent's behaviour.
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.
From Neural Activations to Concepts: A Survey on Explaining Concepts in Neural Networks
In this paper, we review recent approaches for explaining concepts in neural networks.
Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic
Recent advancements in large language models have showcased their remarkable generalizability across various domains.
Internally Rewarded Reinforcement Learning
We study a class of reinforcement learning problems where the reward signals for policy learning are generated by an internal reward model that is dependent on and jointly optimized with the policy.
Learning Bidirectional Action-Language Translation with Limited Supervision and Incongruent Input
Only occasionally, a learning infant would receive a matching verbal description of an action it is committing, which is similar to supervised learning.
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations
Natural language explanations promise to offer intuitively understandable explanations of a neural network's decision process in complex vision-language tasks, as pursued in recent VL-NLE models.
Ranked #1 on
Explanation Generation
on VCR
Neuro-Symbolic Spatio-Temporal Reasoning
In this chapter, we view this integration problem from the perspective of Neuro-Symbolic AI.
Learning Flexible Translation between Robot Actions and Language Descriptions
In this work, we propose the paired gated autoencoders (PGAE) for flexible translation between robot actions and language descriptions in a tabletop object manipulation scenario.
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.
Language Model-Based Paired Variational Autoencoders for Robotic Language Learning
Human infants learn language while interacting with their environment in which their caregivers may describe the objects and actions they perform.
Generalization in Multimodal Language Learning from Simulation
Neural networks can be powerful function approximators, which are able to model high-dimensional feature distributions from a subset of examples drawn from the target distribution.
Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation
A key problem in computational sustainability is to understand the distribution of species across landscapes over time.
Multiagent Simple Temporal Problem: The Arc-Consistency Approach
The Simple Temporal Problem (STP) is a fundamental temporal reasoning problem and has recently been extended to the Multiagent Simple Temporal Problem (MaSTP).
A Survey of Qualitative Spatial and Temporal Calculi -- Algebraic and Computational Properties
Qualitative Spatial and Temporal Reasoning (QSTR) is concerned with symbolic knowledge representation, typically over infinite domains.
