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Found 22 papers, 6 papers with code
Learning Cyclic Causal Models from Incomplete Data
Under the additive noise model, MissNODAGS learns the causal graph by alternating between imputing the missing data and maximizing the expected log-likelihood of the visible part of the data in each training step, following the principles of the expectation-maximization (EM) framework.
TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs
Compared with static knowledge graphs, temporal knowledge graphs (tKG), which can capture the evolution and change of information over time, are more realistic and general.
Large Language Models Can Learn Temporal Reasoning
To be specific, we first teach LLM to translate the context into a temporal graph (TG).
TEILP: Time Prediction over Knowledge Graphs via Logical Reasoning
We first convert TKGs into a temporal event knowledge graph (TEKG) which has a more explicit representation of time in term of nodes of the graph.
Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation
Translating natural language sentences to first-order logic (NL-FOL translation) is a longstanding challenge in the NLP and formal logic literature.
NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning
Learning causal relationships between variables is a well-studied problem in statistics, with many important applications in science.
Generalizing LTL Instructions via Future Dependent Options
In many real-world applications of control system and robotics, linear temporal logic (LTL) is a widely-used task specification language which has a compositional grammar that naturally induces temporally extended behaviours across tasks, including conditionals and alternative realizations.
Temporal Inductive Logic Reasoning
Inductive logic reasoning is one of the fundamental tasks on graphs, which seeks to generalize patterns from the data.
Structure Learning in Graphical Models from Indirect Observations
For the first time, we show that the correct graphical structure can be correctly recovered under the indefinite sensing system ($d < p$) using insufficient samples ($n < p$).
A General Compressive Sensing Construct using Density Evolution
This paper proposes a general framework to design a sparse sensing matrix $\ensuremath{\mathbf{A}}\in \mathbb{R}^{m\times n}$, in a linear measurement system $\ensuremath{\mathbf{y}} = \ensuremath{\mathbf{Ax}}^{\natural} + \ensuremath{\mathbf{w}}$, where $\ensuremath{\mathbf{y}} \in \mathbb{R}^m$, $\ensuremath{\mathbf{x}}^{\natural}\in \RR^n$, and $\ensuremath{\mathbf{w}}$ denote the measurements, the signal with certain structures, and the measurement noise, respectively.
A Density Evolution framework for Preferential Recovery of Covariance and Causal Graphs from Compressed Measurements
In this paper, we propose a general framework for designing sensing matrix $\boldsymbol{A} \in \mathbb{R}^{d\times p}$, for estimation of sparse covariance matrix from compressed measurements of the form $\boldsymbol{y} = \boldsymbol{A}\boldsymbol{x} + \boldsymbol{n}$, where $\boldsymbol{y}, \boldsymbol{n} \in \mathbb{R}^d$, and $\boldsymbol{x} \in \mathbb{R}^p$.
A Machine Learning Framework for Distributed Functional Compression over Wireless Channels in IoT
IoT devices generating enormous data and state-of-the-art machine learning techniques together will revolutionize cyber-physical systems.
Visual Question Answering based on Formal Logic
To achieve this, we take a symbolic reasoning based approach using the framework of formal logic.
Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programming
In this work, we propose a new hierarchical framework via symbolic RL, leveraging a symbolic transition model to improve the data-efficiency and introduce the interpretability for learned policy.
Hierarchical Reinforcement Learning
Inductive logic programming
+2
Improving Actor-Critic Reinforcement Learning via Hamiltonian Monte Carlo Method
In this work, inspired by the previous use of Hamiltonian Monte Carlo (HMC) in VI, we propose to integrate the policy network of actor-critic RL with HMC, which is termed as {\it Hamiltonian Policy}.
Restructuring, Pruning, and Adjustment of Deep Models for Parallel Distributed Inference
In this paper, we consider the parallel implementation of an already-trained deep model on multiple processing nodes (a. k. a.
Accelerating Reinforcement Learning Agent with EEG-based Implicit Human Feedback
Providing Reinforcement Learning (RL) agents with human feedback can dramatically improve various aspects of learning.
Incorporating Relational Background Knowledge into Reinforcement Learning via Differentiable Inductive Logic Programming
Most importantly, it allows for incorporating expert knowledge into the learning, and hence leading to much faster learning and better generalization compared to the standard deep reinforcement learning.
Deep Reinforcement Learning with Implicit Human Feedback
Building atop the baseline, we then make the following novel contributions in our work: (i) We argue that the definition of error-potentials is generalizable across different environments; specifically we show that error-potentials of an observer can be learned for a specific game, and the definition used as-is for another game without requiring re-learning of the error-potentials.
Inductive Logic Programming via Differentiable Deep Neural Logic Networks
In particular, we show that our proposed method outperforms the state of the art ILP solvers in classification tasks for Mutagenesis, Cora and IMDB datasets.
Nested Dithered Quantization for Communication Reduction in Distributed Training
In distributed training, the communication cost due to the transmission of gradients or the parameters of the deep model is a major bottleneck in scaling up the number of processing nodes.
Learning Algorithms via Neural Logic Networks
In particular, we propose a new framework for learning the inductive logic programming (ILP) problems by exploiting the explicit representational power of NLN.
