Search Results for author:
Found 21 papers, 6 papers with code
Neuro-Symbolic Approaches for Text-Based Policy Learning
We outline a method for end-to-end differentiable symbolic rule learning and show that such symbolic policies outperform previous state-of-the-art methods in text-based RL for the coin collector environment from 5-10x fewer training games.
Foundation Models for Generalist Geospatial Artificial Intelligence
This paper introduces a first-of-a-kind framework for the efficient pre-training and fine-tuning of foundational models on extensive geospatial data.
TensorBank: Tensor Lakehouse for Foundation Model Training
Storing and streaming high dimensional data for foundation model training became a critical requirement with the rise of foundation models beyond natural language.
Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning
Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do not generalize well to unseen games.
Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosis
The proposed system addresses the lack of explainability of current Neural Network models and provides a more trustworthy solution for mental disorder diagnosis.
DiffG-RL: Leveraging Difference between State and Common Sense
We propose a novel agent, DiffG-RL, which constructs a Difference Graph that organizes the environment states and common sense by means of interactive objects with a dedicated graph encoder.
Commonsense Knowledge from Scene Graphs for Textual Environments
They are usually imperfect information games, and their interactions are only in the textual modality.
LOA: Logical Optimal Actions for Text-based Interaction Games
We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural language interaction games.
Neuro-Symbolic Reinforcement Learning with First-Order Logic
Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided.
Reinforcement Learning with External Knowledge by using Logical Neural Networks
Specifically, we propose an integrated method that enables model-free reinforcement learning from external knowledge sources in an LNNs-based logical constrained framework such as action shielding and guide.
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games
Our bootstrapped agent shows improved generalization in solving unseen TextWorld games, using 10x-20x fewer training games compared to previous state-of-the-art methods despite requiring less number of training episodes.
Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos
In this paper, we study the case of event detection in sports videos for unstructured environments with arbitrary camera angles.
Spatially-weighted Anomaly Detection with Regression Model
Visual anomaly detection is common in several applications including medical screening and production quality check.
Spatially-weighted Anomaly Detection
Many types of anomaly detection methods have been proposed recently, and applied to a wide variety of fields including medical screening and production quality checking.
Injective State-Image Mapping facilitates Visual Adversarial Imitation Learning
Experimental results show that the proposed adversarial learning method from raw videos produces a similar performance to state-of-the-art imitation learning techniques while frequently outperforming existing hand-crafted video imitation methods.
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention
This paper is a contribution towards interpretability of the deep learning models in different applications of time-series.
Internal Model from Observations for Reward Shaping
During reinforcement learning, the agent predicts the reward as a function of the difference between the actual state and the state predicted by the internal model.
DAQN: Deep Auto-encoder and Q-Network
When it is extended a real-task in the real environment with an actual robot, the method will be required more training images due to complexities or noises of the input images, and executing a lot of actions on the real robot also becomes a serious problem.
Model-based imitation learning from state trajectories
We present a model-based imitation learning method that can learn environment-specific optimal actions only from expert state trajectories.
Reward Estimation via State Prediction
We present a novel reward estimation method that is based on a finite sample of optimal state trajectories from expert demon- strations and can be used for guiding an agent to mimic the expert behavior.
