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Found 25 papers, 7 papers with code
Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization
Synthetic image data generation represents a promising avenue for training deep learning models, particularly in the realm of transfer learning, where obtaining real images within a specific domain can be prohibitively expensive due to privacy and intellectual property considerations.
Assessing SATNet's Ability to Solve the Symbol Grounding Problem
We propose an MNIST based test as an easy instance of the symbol grounding problem that can serve as a sanity check for differentiable symbolic solvers in general.
logLTN: Differentiable Fuzzy Logic in the Logarithm Space
A significant trend in the literature involves integrating axioms and facts in loss functions by grounding logical symbols with neural networks and operators with fuzzy semantics.
MECTA: Memory-Economic Continual Test-Time Model Adaptation
The proposed MECTA is efficient and can be seamlessly plugged into state-of-theart CTA algorithms at negligible overhead on computation and memory.
Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling
As deep learning blooms with growing demand for computation and data resources, outsourcing model training to a powerful cloud server becomes an attractive alternative to training at a low-power and cost-effective end device.
Feasible and Desirable Counterfactual Generation by Preserving Human Defined Constraints
Through user studies, we demonstrate that incorporating causal constraints during CF generation results in significantly better explanations in terms of feasibility and desirability for participants.
MA-Dreamer: Coordination and communication through shared imagination
Multi-agent RL is rendered difficult due to the non-stationary nature of environment perceived by individual agents.
Expert Human-Level Driving in Gran Turismo Sport Using Deep Reinforcement Learning with Image-based Representation
We demonstrate that the proposed method performs expert human-level vehicle control under high-speed driving scenarios even with game screen images as high-dimensional inputs.
Explainable Automatic Hypothesis Generation via High-order Graph Walks
In this paper, we study the automatic hypothesis generation (HG) problem, focusing on explainability.
The Emergence of Abstract and Episodic Neurons in Episodic Meta-RL
In this work, we analyze the reinstatement mechanism introduced by Ritter et al. (2018) to reveal two classes of neurons that emerge in the agent's working memory (an epLSTM cell) when trained using episodic meta-RL on an episodic variant of the Harlow visual fixation task.
Interpretable Relational Representations for Food Ingredient Recommendation Systems
We propose and test two variants of the model.
Logic Tensor Networks
In this paper, we present Logic Tensor Networks (LTN), a neurosymbolic formalism and computational model that supports learning and reasoning through the introduction of a many-valued, end-to-end differentiable first-order logic called Real Logic as a representation language for deep learning.
Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation
Then, the key is to capture the temporal evolution of node pair (term pair) relations from just the positive and unlabeled data.
Continuous Value Iteration (CVI) Reinforcement Learning and Imaginary Experience Replay (IER) for learning multi-goal, continuous action and state space controllers
This paper presents a novel model-free Reinforcement Learning algorithm for learning behavior in continuous action, state, and goal spaces.
Injecting Prior Knowledge for Transfer Learning into Reinforcement Learning Algorithms using Logic Tensor Networks
Facts are provided as background knowledge a priori to learning a policy for how to act in the world.
Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning
In spite of the recent impact of AI, several works have identified the need for principled knowledge representation and reasoning mechanisms integrated with deep learning-based systems to provide sound and explainable models for such systems.
Computational Models of Tutor Feedback in Language Acquisition
This paper investigates the role of tutor feedback in language learning using computational models.
External Evaluation of Event Extraction Classifiers for Automatic Pathway Curation: An extended study of the mTOR pathway
This paper evaluates the impact of various event extraction systems on automatic pathway curation using the popular mTOR pathway.
Referential Uncertainty and Word Learning in High-dimensional, Continuous Meaning Spaces
This paper discusses lexicon word learning in high-dimensional meaning spaces from the viewpoint of referential uncertainty.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
Extracting Biological Pathway Models From NLP Event Representations
This paper describes an an open-source software system for the automatic conversion of NLP event representations to system biology structured data interchange formats such as SBML and BioPAX.
Measuring the State of the Art of Automated Pathway Curation Using Graph Algorithms - A Case Study of the mTOR Pathway
This paper evaluates the difference between human pathway curation and current NLP systems.
Grounded Lexicon Acquisition - Case Studies in Spatial Language
This paper discusses grounded acquisition experiments of increasing complexity.
Grounding Dynamic Spatial Relations for Embodied (Robot) Interaction
This paper presents a computational model of the processing of dynamic spatial relations occurring in an embodied robotic interaction setup.
Robust Natural Language Processing - Combining Reasoning, Cognitive Semantics and Construction Grammar for Spatial Language
We present a system for generating and understanding of dynamic and static spatial relations in robotic interaction setups.
