A Unified Framework for Human-Allied Learning of Probabilistic Circuits

• URL: http://arxiv.org/abs/2405.02413v1
• Date: Fri, 3 May 2024 18:14:29 GMT
• Title: A Unified Framework for Human-Allied Learning of Probabilistic Circuits
• Authors: Athresh Karanam, Saurabh Mathur, Sahil Sidheekh, Sriraam Natarajan,
• Abstract summary: Probabilistic Circuits (PCs) have emerged as an efficient framework for representing and learning complex probability distributions. We propose a novel unified framework that can integrate diverse domain knowledge into the parameter learning process of PCs.
• Score: 9.153589597382082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Probabilistic Circuits (PCs) have emerged as an efficient framework for representing and learning complex probability distributions. Nevertheless, the existing body of research on PCs predominantly concentrates on data-driven parameter learning, often neglecting the potential of knowledge-intensive learning, a particular issue in data-scarce/knowledge-rich domains such as healthcare. To bridge this gap, we propose a novel unified framework that can systematically integrate diverse domain knowledge into the parameter learning process of PCs. Experiments on several benchmarks as well as real world datasets show that our proposed framework can both effectively and efficiently leverage domain knowledge to achieve superior performance compared to purely data-driven learning approaches.

Related papers

• A Unified Framework for Neural Computation and Learning Over Time [56.44910327178975]
  Hamiltonian Learning is a novel unified framework for learning with neural networks "over time" It is based on differential equations that: (i) can be integrated without the need of external software solvers; (ii) generalize the well-established notion of gradient-based learning in feed-forward and recurrent networks; (iii) open to novel perspectives.
  arXiv  Detail & Related papers  (2024-09-18T14:57:13Z)
• Fairness, Accuracy, and Unreliable Data [0.0]
  This thesis investigates three areas targeted at improving the reliability of machine learning; fairness in machine learning, strategic classification, and algorithmic robustness. A theme throughout this thesis is thinking about ways in which a plain' empirical risk minimization algorithm will be misleading or ineffective because of a mis-match between classical learning theory assumptions and specific properties of some data distribution in the wild.
  arXiv  Detail & Related papers  (2024-08-28T17:44:08Z)
• Domain Generalizable Knowledge Tracing via Concept Aggregation and Relation-Based Attention [10.95112067894146]
  We propose a domain generalization approach for knowledge tracing, where existing education systems are considered source domains. We also present a concept aggregation approach designed to reduce conceptual disparities within sequences of student interactions from diverse domains. To fully leverage exercise information, we propose a new knowledge tracing model tailored for the domain generalization KT task, named Domain-General Relation-based Knowledge Tracing (DGRKT)
  arXiv  Detail & Related papers  (2024-07-02T13:13:44Z)
• Informed Decision-Making through Advancements in Open Set Recognition and Unknown Sample Detection [0.0]
  Open set recognition (OSR) aims to bring classification tasks in a situation that is more like reality. This study provides an algorithm exploring a new representation of feature space to improve classification in OSR tasks.
  arXiv  Detail & Related papers  (2024-05-09T15:15:34Z)
• A Unified and General Framework for Continual Learning [58.72671755989431]
  Continual Learning (CL) focuses on learning from dynamic and changing data distributions while retaining previously acquired knowledge. Various methods have been developed to address the challenge of catastrophic forgetting, including regularization-based, Bayesian-based, and memory-replay-based techniques. This research aims to bridge this gap by introducing a comprehensive and overarching framework that encompasses and reconciles these existing methodologies.
  arXiv  Detail & Related papers  (2024-03-20T02:21:44Z)
• Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study [0.0]
  The FAIR Guiding Principles aim to improve the findability, accessibility, interoperability, and reusability of digital content by making them both human and machine actionable. These principles have not yet been broadly adopted in the domain of machine learning-based program analyses and optimizations for High-Performance Computing. We design a methodology to make HPC datasets and machine learning models FAIR after investigating existing FAIRness assessment and improvement techniques.
  arXiv  Detail & Related papers  (2022-11-03T18:45:46Z)
• Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity [61.05259660910437]
  We propose a global consistency and complementarity network (CoCoNet) to learn representations from multiple views. On the global stage, we reckon that the crucial knowledge is implicitly shared among views, and enhancing the encoder to capture such knowledge can improve the discriminability of the learned representations. Lastly on the local stage, we propose a complementarity-factor, which joints cross-view discriminative knowledge, and it guides the encoders to learn not only view-wise discriminability but also cross-view complementary information.
  arXiv  Detail & Related papers  (2022-09-16T09:24:00Z)
• An Extensible Benchmark Suite for Learning to Simulate Physical Systems [60.249111272844374]
  We introduce a set of benchmark problems to take a step towards unified benchmarks and evaluation protocols. We propose four representative physical systems, as well as a collection of both widely used classical time-based and representative data-driven methods.
  arXiv  Detail & Related papers  (2021-08-09T17:39:09Z)
• LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose Prediction [42.38793195337463]
  We propose a novel learning-based ensemble approach named LENAS, which integrates neural architecture search with knowledge distillation for 3D radiotherapy dose prediction. Our approach starts by exhaustively searching each block from an enormous architecture space to identify multiple architectures that exhibit promising performance. To mitigate the complexity introduced by the model ensemble, we adopt the teacher-student paradigm, leveraging the diverse outputs from multiple learned networks as supervisory signals.
  arXiv  Detail & Related papers  (2021-06-12T10:08:52Z)
• Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning [93.18238573921629]
  We study how Ensemble of deep learning models can improve test accuracy, and how the superior performance of ensemble can be distilled into a single model. We show that ensemble/knowledge distillation in deep learning works very differently from traditional learning theory. We prove that self-distillation can also be viewed as implicitly combining ensemble and knowledge distillation to improve test accuracy.
  arXiv  Detail & Related papers  (2020-12-17T18:34:45Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.